agc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RAMSESWarning: RAMSES: Gnuplot executable could not be found in the system path, so the runtime observables are disabled.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pyramses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from agc_models import end_simulation\n",
    "from agc_models import move_file\n",
    "from agc_models import sfc\n",
    "from agc_models import Gens\n",
    "from agc_models import sortGens\n",
    "from agc_models import multipleIncreaseDelay\n",
    "\n",
    "from agc_models import chop_curve\n",
    "from agc_models import rate_of_change\n",
    "from agc_models import settling_time\n",
    "from agc_models import curve_of_rate_of_change"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load a simulator instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ram = pyramses.sim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load saved test-case & Add more observation(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "case = pyramses.cfg('cmd.txt')\n",
    "case.addRunObs('MS CCGTG2.01')  # Monitor, will write real-time (voltage-time or) frequency-time data of bus G2 (GBdynHVDC.dat)\n",
    "monitor = ['CCGTG2.01']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Universe Parameters. Start: 300sec, Settling before: 1200sec (+900sec = +15min), End: 1500sec; Time step: 1sec."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "prepared_folder_address = 'cur'\n",
    "nominal_frequency = 1.0\n",
    "start_time = 150.0\n",
    "settling_end_time = 800.0\n",
    "end_time = 800.0  # (dst file)\n",
    "agcTimeStep = 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Breaker (one of distrubances) & Selected generators (to help fix errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['COALG15.02', 'COALG23.02', 'COALG4.02', 'COALG11.02', 'COALG16.02']\n",
      "[0.2, 0.2, 0.2, 0.2, 0.2]\n",
      "[0.062, 0.062, 0.122, 0.122, 0.122]\n"
     ]
    }
   ],
   "source": [
    "breaker = '8-10a_8-10b' # (dst file)\n",
    "\n",
    "# gen's code     name       weight  mini delay              \n",
    "gen4_2 = Gens('COALG4.02', 0.200, 0.1220)    # 2386.699/17489.086 = 0.136468\n",
    "gen11_2 = Gens('COALG11.02', 0.200, 0.1220)  # 1559.337/17489.086 = 0.089161\n",
    "gen15_2 = Gens('COALG15.02', 0.200, 0.0620)  # 8027.401/17489.086 = 0.458995\n",
    "gen16_2 = Gens('COALG16.02', 0.200, 0.1220)  # 3911.223/17489.086 = 0.223638\n",
    "gen23_2 = Gens('COALG23.02', 0.200, 0.0620)  # 1604.416/17489.086 = 0.091738\n",
    "\n",
    "liST = []\n",
    "liST.append(gen4_2)\n",
    "liST.append(gen11_2)\n",
    "liST.append(gen15_2)\n",
    "liST.append(gen16_2)\n",
    "liST.append(gen23_2)\n",
    "list_of_gens, weight_of_gens,  mini_list_of_td = sortGens(liST)\n",
    "\n",
    "print(list_of_gens)\n",
    "print(weight_of_gens)\n",
    "print(mini_list_of_td)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test with fixed kp and ki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kp = 0.3100\n",
      "ki = 0.0100\n",
      "[0.062, 0.062, 0.122, 0.122, 0.122]\n",
      "kill gnuplot successfully\n",
      "endSim() successfully\n",
      "delete ram & case successfully\n",
      "re-write cur successfully\n",
      "copy cur successfully\n",
      "delete temp_display(_).cur successfully\n",
      "delete some trace files successfully\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    kp = 0.31\n",
    "    ki = 0.01\n",
    "    list_of_td = multipleIncreaseDelay(100, mini_list_of_td)  # 100%\n",
    "    \n",
    "    kp = \"{0:.4f}\".format(round(float(kp),4))\n",
    "    ki = \"{0:.4f}\".format(round(float(ki),4))\n",
    "    print(\"kp = \" + str(kp))\n",
    "    print(\"ki = \" + str(ki))\n",
    "    print(list_of_td)\n",
    "\n",
    "    sfc(ram, case, monitor, list_of_gens, weight_of_gens, list_of_td, prepared_folder_address, start_time, end_time, agcTimeStep, breaker, nominal_frequency, kp, ki)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embed the frequency/voltage response in the output\n",
    "**https://pyramses.paristidou.info/_modules/pyramses/extractor.html#extractor.getSync**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ext = pyramses.extractor(case.getTrj())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A. Check frequency in the grid via monitor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# t_ori, f_ori, t, f = chop_curve(ext.getSync('CCGTG2.01').S.time, ext.getSync('CCGTG2.01').S.value, chop_value=start_time)\n",
    "t = ext.getSync('CCGTG2.01').S.time\n",
    "f = ext.getSync('CCGTG2.01').S.value\n",
    "realSettlingTime = settling_time(t, f, 1/5000, nominal_frequency)\n",
    "# ext.getSync('CCGTG2.01').S.plot()\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "plt.plot(t, f, label = ext.getSync('CCGTG2.01').S.msg + \": settling time = \" + str(realSettlingTime) + \" sec.\")\n",
    "plt.legend()\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('frequency (pu)')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B. Check power & power rate of the five generators (power rate = delta power / delta time, should be around 10% (CCGT) or 5% (COAL) of power capacity per min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Pnom(G4.02) = 2028.694 MW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ext.getSync('COALG4.02').P.plot()\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "plt.plot(ext.getSync('COALG4.02').P.time, ext.getSync('COALG4.02').P.value, label = ext.getSync('COALG4.02').P.msg)\n",
    "plt.legend()\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('power (MW)')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### COALG4.02: 5% capacity/min = 101.435 MW/min:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (651,) and (650,)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-4dcbe3306363>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcurve_of_rate_of_change\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'COALG4.02'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetSync\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'COALG4.02'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mP\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetSync\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'COALG4.02'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mP\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msettling_end_time\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0magcTimeStep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munit_converter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Desktop\\GitHub\\summer19\\agc_models.py\u001b[0m in \u001b[0;36mcurve_of_rate_of_change\u001b[1;34m(name_of_machine, input_x_axis, input_y_axis, start_value_of_x, end_value_of_x, step_x, unit_converter)\u001b[0m\n\u001b[0;32m    446\u001b[0m         \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    447\u001b[0m         \u001b[0mstr_label\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname_of_machine\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\": activate power rate (MW/min); Max activate rate = \"\u001b[0m \u001b[1;33m+\u001b[0m  \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax_rate\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"MW/min\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 448\u001b[1;33m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mramp_rate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstr_label\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    449\u001b[0m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m't (s)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'power rate (MW/min)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2787\u001b[0m     return gca().plot(\n\u001b[0;32m   2788\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[1;32m-> 2789\u001b[1;33m         is not None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2790\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2791\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1663\u001b[0m         \"\"\"\n\u001b[0;32m   1664\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_alias_map\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1665\u001b[1;33m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1666\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1667\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    223\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m                 \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 225\u001b[1;33m             \u001b[1;32myield\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    389\u001b[0m             \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    390\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 391\u001b[1;33m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    392\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    393\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'plot'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[1;34m(self, x, y)\u001b[0m\n\u001b[0;32m    268\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    269\u001b[0m             raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[1;32m--> 270\u001b[1;33m                              \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[0;32m    271\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    272\u001b[0m             raise ValueError(\"x and y can be no greater than 2-D, but have \"\n",
      "\u001b[1;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (651,) and (650,)"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "curve_of_rate_of_change('COALG4.02', ext.getSync('COALG4.02').P.time, ext.getSync('COALG4.02').P.value, start_time, settling_end_time, agcTimeStep, unit_converter=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Pnom(G11.02) = 1403.403 MW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ext.getSync('COALG11.02').P.plot()\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "plt.plot(ext.getSync('COALG11.02').P.time, ext.getSync('COALG11.02').P.value, label = ext.getSync('COALG11.02').P.msg)\n",
    "plt.legend()\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('power (MW)')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### COALG11.02: 5% capacity/min = 70.170 MW/min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curve_of_rate_of_change('COALG11.02', ext.getSync('COALG11.02').P.time, ext.getSync('COALG11.02').P.value, start_time, settling_end_time, agcTimeStep, unit_converter=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Pnom(G15.02) = 6823.291 MW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ext.getSync('COALG15.02').P.plot()\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "plt.plot(ext.getSync('COALG15.02').P.time, ext.getSync('COALG15.02').P.value, label = ext.getSync('COALG15.02').P.msg)\n",
    "plt.legend()\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('power (MW)')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### COALG15.02: 5% capacity/min = 341.165 MW/min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curve_of_rate_of_change('COALG15.02', ext.getSync('COALG15.02').P.time, ext.getSync('COALG15.02').P.value, start_time, settling_end_time, agcTimeStep, unit_converter=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Pnom(G16.02) = 3520.101 MW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ext.getSync('COALG16.02').P.plot()\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "plt.plot(ext.getSync('COALG16.02').P.time, ext.getSync('COALG16.02').P.value, label = ext.getSync('COALG16.02').P.msg)\n",
    "plt.legend()\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('power (MW)')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### COALG16.02: 5% capacity/min = 176.005 MW/min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curve_of_rate_of_change('COALG16.02', ext.getSync('COALG16.02').P.time, ext.getSync('COALG16.02').P.value, start_time, settling_end_time, agcTimeStep, unit_converter=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Pnom(G23.02) = 1363.753 MW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ext.getSync('COALG23.02').P.plot()\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "plt.plot(ext.getSync('COALG23.02').P.time, ext.getSync('COALG23.02').P.value, label = ext.getSync('COALG23.02').P.msg)\n",
    "plt.legend()\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('power (MW)')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### COALG23.02: 5% capacity/min = 68.188 MW/min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curve_of_rate_of_change('COALG23.02', ext.getSync('COALG23.02').P.time, ext.getSync('COALG23.02').P.value, start_time, settling_end_time, agcTimeStep, unit_converter=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}