{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detailed Secondary Frequency Regulation Study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we will demonstrate how to use AMS and ANDES to mimic the system secondary frequency regulation, where system automatic generation control (AGC) is used to maintain the system frequency at the nominal value.\n",
    "\n",
    "This demo is prepared by [Jinning Wang](https://jinningwang.github.io/).\n",
    "\n",
    "Reference:\n",
    "\n",
    "1. J. Wang et al., \"Electric Vehicles Charging Time Constrained Deliverable Provision of Secondary Frequency Regulation,\" in IEEE Transactions on Smart Grid, doi: [10.1109/TSG.2024.3356948](https://ieeexplore.ieee.org/document/10411057).\n",
    "1. “Standard BAL-001-2 – Real Power Balancing Control Performance.” [Online]. Available: https://www.nerc.com/pa/Stand/Reliability%20Standards/BAL-001-2.pdf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import chain\n",
    "\n",
    "import numpy as np\n",
    "import scipy\n",
    "import pandas as pd\n",
    "\n",
    "import ams\n",
    "import andes\n",
    "\n",
    "import datetime\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reset matplotlib style to default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcdefaults()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ensure in-line plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last run time: 2024-11-24 17:50:01\n",
      "andes:1.9.2\n",
      "ams:0.9.12\n"
     ]
    }
   ],
   "source": [
    "print(\"Last run time:\", datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n",
    "\n",
    "print(f'andes:{andes.__version__}')\n",
    "print(f'ams:{ams.__version__}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "andes.config_logger(stream_level=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ams.config_logger(stream_level=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dispatch case\n",
    "\n",
    "We use the IEEE 39-bus system as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp = ams.load(ams.get_case('ieee39/ieee39_uced.xlsx'),\n",
    "              setup=True,\n",
    "              no_output=True,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In [RTED documentation](https://ltb.readthedocs.io/projects/ams/en/stable/typedoc/DCED.html#id24),\n",
    "we can see that Var ``rgu`` and ``rgd`` are the variables for RegUp/Dn reserve,\n",
    "and Constraint ``rbu`` and ``rbd`` are the equality constraints for RegUp/Dn reserve balance.\n",
    "\n",
    "As for the RegUp/Dn reserve requirements, it is defined by parameter ``du`` and ``dd`` as percentage of the total load,\n",
    "and later ``dud`` and ``ddd`` are the actual reserve requirements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.34256, 0.     ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.RTED.dud.v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05, 0.05])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.RTED.du.v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dynamic case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Following PFlow models in addfile will be overwritten: <Bus>, <PQ>, <PV>, <Slack>, <Shunt>, <Line>, <Area>\n",
      "AMS system 0x169825970 is linked to the ANDES system 0x16bd2b380.\n",
      "Parsing OModel for <PFlow>\n",
      "Building system matrices\n",
      "Evaluating OModel for <PFlow>\n",
      "Finalizing OModel for <PFlow>\n"
     ]
    }
   ],
   "source": [
    "sa = sp.to_andes(addfile=andes.get_case('ieee39/ieee39_full.xlsx'),\n",
    "                 setup=True,\n",
    "                 no_output=True,\n",
    "                 default_config=True,\n",
    "                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Device `ACEc` is used to calculate the Area Control Error (ACE)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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>idx</th>\n",
       "      <th>u</th>\n",
       "      <th>name</th>\n",
       "      <th>bus</th>\n",
       "      <th>bias</th>\n",
       "      <th>busf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ACE_1</td>\n",
       "      <td>1</td>\n",
       "      <td>300.0</td>\n",
       "      <td>BusFreq_2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     idx    u   name  bus   bias       busf\n",
       "uid                                        \n",
       "0      1  1.0  ACE_1    1  300.0  BusFreq_2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sa.ACEc.as_df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthetic load\n",
    "\n",
    "ISO-NE provides various grid data, such as [Five-Minute System Demand](https://www.iso-ne.com/isoexpress/web/reports/load-and-demand/-/tree/dmnd-five-minute-sys).\n",
    "In this example, we revise the March 02, 2024, 18 PM data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x16ceba5a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeMAAAE6CAYAAAAlRjrfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACG/ElEQVR4nO2dd3hUxfrHP7ubZNM7qYQQaoBQQw0dKdJEEMVGE/mJKAgoCoKIWBDvlQtXBMVLUS4KKjauEYnSpfcSOoFASAgppJfN7vn9sdmT3ewmJCGw2Tif59mHPXPmzJl3T9jvzsw776uQJElCIBAIBAKB1VBauwMCgUAgEPzdEWIsEAgEAoGVEWIsEAgEAoGVEWIsEAgEAoGVEWIsEAgEAoGVEWIsEAgEAoGVEWIsEAgEAoGVEWIsEAgEAoGVEWIsEAgEAoGVEWIsENQQ1q5di0Kh4PDhw9buClevXkWhULB27doK1b9y5Qovv/wyTZo0wcnJCWdnZ1q0aMHcuXNJSEi4v50VCGoBdtbugEAgsG3+97//8eSTT+Lr68vLL79M27ZtUSgUnDp1itWrV/Prr79y7Ngxa3dTIKjRCDEWCARVJi4ujieffJImTZqwfft2PDw85HN9+vRh6tSp/Pjjj9VyL41Gg0KhwM5OfG0Jah9imlogsDH27NnDQw89hJubG87OzkRFRfHrr7+a1Ll9+zaTJ0+mefPmuLq64ufnR58+fdi9e7dZezdv3uSJJ57Azc0NDw8PRo0aRVJSUoX6snjxYnJycli+fLmJEBtQKBSMGDFCPq5fvz7jxo0zq9erVy969eolH+/YsQOFQsG6det49dVXCQ4ORq1Wc+bMGRQKBatWrTJr47fffkOhUPDLL7/IZRcvXuTpp5/Gz88PtVpNs2bN+PTTTytkm0DwIBFiLBDYEDt37qRPnz5kZGSwatUqvvnmG9zc3Bg6dCgbN26U66WlpQHw9ttv8+uvv7JmzRoaNGhAr1692LFjh1wvLy+Pvn37snXrVhYuXMh3331HQEAAo0aNqlB/tm7dir+/P507d65WOw3Mnj2b+Ph4PvvsMzZv3kxISAht27ZlzZo1ZnXXrl2Ln58fgwYNAiA2NpYOHTpw+vRpPv74Y/73v/8xePBgpk6dyjvvvHNf+isQVBlJIBDUCNasWSMB0qFDh8qs07lzZ8nPz0/KysqSy4qKiqSIiAipbt26kk6ns3hdUVGRpNFopIceekgaPny4XL5ixQoJkH7++WeT+hMnTpQAac2aNeX22dHRUercuXMFrNMTGhoqjR071qy8Z8+eUs+ePeXj7du3S4DUo0cPs7r//ve/JUA6f/68XJaWliap1Wrp1VdflcsGDBgg1a1bV8rIyDC5/uWXX5YcHR2ltLS0CvdbILjfiJGxQGAj5OTkcODAAUaOHImrq6tcrlKpGD16NDdu3OD8+fNy+WeffUa7du1wdHTEzs4Oe3t7/vzzT86ePSvX2b59O25ubjzyyCMm93r66afvv0EV4LHHHjMre+aZZ1Cr1Sae3t988w0FBQWMHz8egPz8fP7880+GDx+Os7MzRUVF8mvQoEHk5+ezf//+B2WGQHBXhBgLBDZCeno6kiQRGBhodi4oKAiA1NRUQL+W++KLL9KpUyc2bdrE/v37OXToEA8//DB5eXnydampqfj7+5u1FxAQUKE+1atXj7i4uKqYUyEs2ert7c0jjzzCV199hVarBfRT1B07dqRFixaA3q6ioiI++eQT7O3tTV6GaeyUlJT71m+BoLIIt0SBwEbw8vJCqVSSmJhodu7mzZsA+Pr6AvDf//6XXr16sWLFCpN6WVlZJsc+Pj4cPHjQrL2KOnANGDCATz75hP3791do3djR0ZGCggKz8pSUFLnvxigUCovtjB8/nu+++46YmBjq1avHoUOHTGz18vKSZwxeeukli22EhYXdtb8CwYNCjIwFAhvBxcWFTp068cMPP5iMbnU6Hf/973+pW7cuTZo0AfQiplarTa4/efIk+/btMynr3bs3WVlZJh7IAF9//XWF+jR9+nRcXFyYPHkyGRkZZuclSTLZ2lS/fn1OnjxpUufChQsm0+sVoX///gQHB7NmzRrWrFmDo6MjTz31lHze2dmZ3r17c+zYMVq1akX79u3NXj4+PpW6p0BwPxEjY4GghrFt2zauXr1qVj5o0CAWLlxIv3796N27N6+99hoODg4sX76c06dP880338gjySFDhvDuu+/y9ttv07NnT86fP8+CBQsICwujqKhIbnPMmDH861//YsyYMbz//vs0btyY6Ohofv/99wr1NSwsjA0bNjBq1CjatGkjB/0AvTfz6tWrkSSJ4cOHAzB69GieffZZJk+ezGOPPca1a9f46KOPqFOnTqU+I5VKxZgxY1i8eDHu7u6MGDHCbGvV0qVL6datG927d+fFF1+kfv36ZGVlcenSJTZv3sy2bdsqdU+B4L5iZQcygUBQjMGbuqxXXFycJEmStHv3bqlPnz6Si4uL5OTkJHXu3FnavHmzSVsFBQXSa6+9JgUHB0uOjo5Su3btpJ9++kkaO3asFBoaalL3xo0b0mOPPSa5urpKbm5u0mOPPSbt3bu3Qt7UBi5fvixNnjxZatSokaRWqyUnJyepefPm0owZM+R+S5Ik6XQ66aOPPpIaNGggOTo6Su3bt5e2bdtWpjf1d999V+Y9L1y4IH82MTExFuvExcVJzz33nBQcHCzZ29tLderUkaKioqT33nuvQnYJBA8KhSRJkpV+BwgEAoFAIECsGQsEAoFAYHWEGAsEAoFAYGWEGAsEAoFAYGWEGAsEAoFAYGWEGAsEAoFAYGWEGAsEAoFAYGVE0I8qotPpuHnzJm5ubmWG7BMIBAJB7UeSJLKysggKCkKprOIY18r7nKVPP/1Uql+/vqRWq6V27dpJu3btKrf+f//7X6lVq1aSk5OTFBAQII0bN05KSUkxqfP9999LzZo1kxwcHKRmzZpJP/zwg1k7N27ckJ555hnJ29tbcnJyklq3bi0dPny4wv2+fv16uQEaxEu8xEu8xOvv9bp+/XqFNaQ0Vh0Zb9y4kWnTprF8+XK6du3K559/zsCBA4mNjaVevXpm9ffs2SOH7xs6dCgJCQlMmjSJ559/Xo5/u2/fPkaNGsW7777L8OHD+fHHH3niiSfYs2cPnTp1AvTZb7p27Urv3r357bff8PPz4/Lly3h6ela4725ubgDExcXh7e197x9GDUGj0bB161b69++Pvb29tbtTbQi7bIfaaBMIu2yNytiVmZlJSEiIrAtVwapivHjxYiZMmMDzzz8PwJIlS/j9999ZsWIFCxcuNKu/f/9+6tevz9SpUwF9XNwXXniBjz76SK6zZMkS+vXrx+zZswGYPXs2O3fuZMmSJXzzzTcALFq0iJCQENasWSNfV79+/Ur13TA17ebmhru7e6WurcloNBqcnZ1xd3evdf+xhF22QW20CYRdtkZV7LqXJUuriXFhYSFHjhxh1qxZJuX9+/dn7969Fq+Jiopizpw5REdHM3DgQJKTk/n+++8ZPHiwXGffvn1Mnz7d5LoBAwawZMkS+fiXX35hwIABPP744+zcuZPg4GAmT57MxIkTy+xvQUGBSeq3zMxMQP/ANBpNhe2u6RhsqU02gbDLlqiNNoGwy9aojF3VYbvVxDglJQWtVmuW2Nzf37/MXKpRUVGsX7+eUaNGkZ+fT1FREY888giffPKJXCcpKemubV65coUVK1YwY8YM3nzzTQ4ePMjUqVNRq9WMGTPG4r0XLlzIO++8Y1a+fft2nJ2dK2y3rRATE2PtLtwXhF22Q220CYRdtkZF7MrNzb3n+1jdm7r0sF6SpDKH+rGxsUydOpV58+YxYMAAEhMTmTlzJpMmTWLVqlUVblOn09G+fXs++OADANq2bcuZM2dYsWJFmWI8e/ZsZsyYIR8b1gh69+5dq/KiajQaYmJi6NevX62bchJ22Qa10SYQdtkalbHLMFN6L1hNjH19fVGpVGaj4OTkZLORrYGFCxfStWtXZs6cCUCrVq1wcXGhe/fuvPfeewQGBhIQEHDXNgMDA2nevLlJnWbNmrFp06Yy+6tWq82StQPY29uX+aAkSaKoqAitVltmuzUNrVaLnZ0dWq226i76NZC/o10qlQo7Ozub3XpX3v8tW0bYZVtUxK7qsNtqYuzg4EBkZCQxMTFy4nHQTwkMGzbM4jW5ubnY2Zl2WaVSAXrhA+jSpQsxMTEm68Zbt24lKipKPu7atSvnz583aefChQuEhobem1FGFBYWkpiYWC3TFw8SSZIICAjg+vXrNvslbom/q13Ozs4EBgbi4OBghd4JBIKKYtVp6hkzZjB69Gjat29Ply5dWLlyJfHx8UyaNAnQTw0nJCTw1VdfATB06FAmTpzIihUr5GnqadOm0bFjR4KCggB45ZVX6NGjB4sWLWLYsGH8/PPP/PHHH+zZs0e+7/Tp04mKiuKDDz7giSee4ODBg6xcuZKVK1dWi106nY64uDhUKhVBQUE4ODjYjADodDqys7NxdXWtVSPIv5tdkiRRWFjI7du3iYuLo3HjxrXKboGgtmFVMR41ahSpqaksWLCAxMREIiIiiI6OlkeoiYmJxMfHy/XHjRtHVlYWy5Yt49VXX8XT05M+ffqwaNEiuU5UVBQbNmxg7ty5vPXWWzRs2JCNGzfKe4wBOnTowI8//sjs2bNZsGABYWFhLFmyhGeeeaZa7CosLESn0xESEmJzzl06nY7CwkIcHR1r1Zf339EuJycn7O3tuXbtmlxHIKjp7L+SyrJtl5gzuBnNAmvPttG7YXUHrsmTJzN58mSL59auXWtWNmXKFKZMmVJumyNHjmTkyJHl1hkyZAhDhgypcD+rQm360hfYJuJvUFBT0Wh12KvM/z7f/V8sZ25mMnDpbq5+ONjClbUT8T9VIBAIBPeVrHyN7NcD+tFvs7e28EH0WbO6V27nyO/TcwofSP9qAkKMBQKBQHDf2HomiVbvbGXez2fksjV/xVGkk1i56wpppQQ30KNkOSUlu4C/C0KMBVbn6tWrKBQKjh8/bu2uAHDu3Dk6d+6Mo6Mjbdq0sVhW2T6PGzeORx999L71WSCoiRyMS+P/1h1BkmDd/mtyeXpOScSqD38zHR3fzioR4PTc2hXVqzyEGAtMGD9+fLU5slU3ly5dYvz48dStWxe1Wk1YWBhPPfUUhw8frtb7vP3227i4uHD+/Hn+/PNPi2UhISGy02FFWLp0qUUfiHth/vz5tGvXrlrbFAiqk9V74kyONVodAMlZ+XLZZaNp6XyNlqyCIvk4PVdMUwsENYrDhw8TGRnJhQsX+Pzzz4mNjeXHH38kPDycV199tVrvdfnyZbp160ZoaKgcXa10mUqlIiAgwGzfe1l4eHhUKiuYQFAbyNOYBjx6Yd0RtDqJW5klo9+8wpI6xqNigDtCjAXVjSRJ5BYWPfCXsdNEdbBz5046duyIWq0mMDCQWbNmUVRU8kt2y5YtdOvWDU9PT3x8fBgyZAiXL182aePgwYO0bdsWR0dH2rdvz7Fjx+762Y0bN47GjRuze/duBg8eTMOGDWnTpg1vv/02P//8s1z31KlT9OnTBycnJ3x8fPi///s/srOzTdpbs2YNzZo1w9HRkfDwcJYvXy6fUygUHDlyhAULFqBQKJg/f77FMkvT1GfOnGHw4MG4u7vj5uZG9+7dZdtLT1NLksRHH31EgwYNcHJyonXr1nz//ffy+R07dqBQKPjzzz9p3749zs7OREVFycFq1q5dyzvvvMOJEyfw8vJCpVJV+8hbILhXcguLTI63nUtm65kkE5G+lVkySr5dao347zRNbfWtTX8X8jRams/7/YHfN3bBAJwdqucxJyQkMGjQIMaNG8dXX33FuXPnmDhxIo6OjsyfPx+AnJwcZsyYQcuWLcnJyWHevHkMHz6c48ePo1QqycnJYciQIfTp04f//ve/xMXF8corr5R73+PHj3PmzBm+/vpri1t1DCPO3NxcHn74YTp37syhQ4dITk7m+eef5+WXX2b16tUAfPHFF7zzzjssW7aMtm3bcuzYMSZOnIiLiwtjx44lMTGRvn378vDDD/Paa6/h6urKpEmTzMpSUlLMPpsePXrQq1cvtm3bhru7O3/99ZfJDxVj5s6dyw8//MCKFSto3Lgxu3bt4tlnn6VOnTr07NlTrjdnzhw+/vhj6tSpw6RJk3juuef466+/GDVqFKdPn2bLli1s2rQJNzc3vLy8KvooBYIHQla++d//8et3TI7TcgvR6iRUSgWnbmSYnPs7TVMLMRZUmOXLlxMSEsKyZctQKBSEh4dz8+ZN3njjDebNm4dSqeSxxx4zuWbVqlX4+fkRGxtLREQE69evR6vVsnr1apydnWnRogU3btzgxRdfLPO+Fy9eBCA8PLzc/q1fv568vDy++uorXFxcAFi2bBlDhw5l4cKFODk58f777/Pxxx8zYsQIQJ8TOzY2ls8//5yxY8fKU8+urq4EBAQA4OrqalZWWow//fRTPDw82LBhgxyntkmTJhb7mZOTw+LFi9m2bRtdunQBoEGDBuzZs4fPP//cRIzff/99+XjWrFkMHjyY/Px8nJyc5H75+/vj7u4u9hQLahyWxDg2UZ9UIczXhbiUHCRJPx3t46rmamqOSd3U7ELe+uk0GXkaWgS580T7ELxcamdoVyHGDwgnexWxCwZY5b7VxdmzZ+nSpYtJaM+uXbuSnZ3NjRs3qFevHpcvX+att95i//79pKSkoNPpHTbi4+OJiIjg7NmztG7d2iQymUGQysIw1X63kKKGtg1CbOifTqfj/Pnz1K1bl+vXrzNhwgST3NVFRUV4eHhU/IOwwPHjx+nevXuFAsbHxsaSn59Pv379TMoLCwtp27atSVmrVq3k94GBgYA+8Um9evXuqb8CwYMgK18/zfxCjwZ8vusKAGcTswAI8nQkPi0XrU7iaPwd+jX3JyVbPxJu7OfKxeRsziVlcjpBL96/nLhJWm4hswc2s4Il9x8hxg8IhUJRbdPF1sJSesvSQjl06FBCQkL44osvCAoKQqfTERERQWFhoUn9ymAYYZ49e1bealTR/hlQKBTyD4MvvvjCJDwqlCQcqSpOTk4Vrmvox6+//kpwcLDJudKZwYzF3WCb4XqBoCYjSRLZxZ7R47uGcTMjn80nbsp7h4M9najv48zl2zkcjEvVi3GxA1eTADcuJmdz8VYpf4+/rtZaMRbzWoIK07x5c/bu3WsiqHv37sXNzY3g4GBSU1M5e/Ysc+fO5aGHHqJZs2akp6ebtXHixAny8vLksv3795d73zZt2tC8eXM+/vhji0J0584due3jx4+Tk1My1fXXX3+hVCpp0qQJfn5+BAcHc+XKFRo1amTyCgsLq8pHItOqVSt2796NRnN3h5PmzZujVquJj48360dISEiF7+ng4GBT6TkFfy9yCrXoir8qPJ3t6d7I1+R8XS9nnmiv/3s/m5hFYZFOduBqXhyTuqDI9P97YZEOjVZHcmZ+rfO0tu2hmuC+kJmZKTtcGfD29mby5MksWbKEKVOm8PLLL3P+/HnefvttZsyYgVKpxMvLCx8fH1auXElgYCDx8fHMmjXLpO2nn36aOXPmMGHCBObOncvVq1f55z//WW5/FAoFa9asoW/fvvTo0YM333yT8PBwsrOz2bx5M1u3bmXnzp0888wzvP3224wdO5b58+dz+/ZtpkyZwujRo/H39yczM5N58+Yxbdo03N3dGThwIAUFBRw+fJj09HRmzJhR5c/s5Zdf5pNPPuHJJ59k9uzZeHh4sH//fjp27EjTpk1N6rq5ufHaa68xffp0dDod3bp1IzMzk7179+Lq6srYsWMrdM/69esTFxfHqVOnaNq0KR4eHhZzbgsE95XUSzRI3oLywDUwmmHS5mmYoLqIUqlAfSiONreymaC6Lp/vmXocXzc1yao4iIOPF6yjp1aipwoGZZ8hTXXN0t34cF60/P7/ejTA390R3IOh+TCwMDN2KTmbet7OONjV7LGnEGOBGXv27CEyMtKkbOzYsaxdu5bo6GhmzpxJ69at8fb2lkUV9EkJNmzYwNSpU4mIiKBp06b8+9//plevXnI7rq6ubN68mUmTJtG2bVuaN2/OokWLzBy/StOxY0cOHz7M+++/z8SJE0lJSSEwMJCoqCiWLFkC6HP3/v7777zyyit06NABZ2dnHnvsMRYvXiy38/zzz+Pq6so//vEPXn/9dVxcXGjZsiXTpk27p8/Mx8eHbdu2MXPmTHr27IlKpaJNmzZ07drVYv13330XPz8/Fi5cyJUrV/D09KRdu3a8+eabFb7nY489xqZNmxg6dCgZGRmsWbOGcePG3ZMdAkFlUf08iZaJxyHBtNwDeMuwyrIVmhgfA8Tq/zEpM+jlkVLlZbHP6P1zW6Ge6fLTvsupPPWFfuYtbuGgMpexdl+8TYiXM/V9XSyefxAopOreiPo3ITMzEw8PD1JSUuTAEAby8/OJi4sjLCzM5tLW6XQ6MjMza5137t/VLlv8W9RoNERHRzNo0KAKOcTZCrXVLulfESgyrqNr8BBKFx9OJWRw6XY2Tf3dOH8rCxcHO/o39ye7oIiYs7cAcFApGdxS75D44/EEszaHtQmmsEjLb6eT7nr/HsqT+CiyYNR/odlQk3NPrdzPviupAGx/rRdhFsT2xPU7DPv0LwCTLFGVeV4GPcjIyMDdvWppH8XIWCAQCARVR6f3W9D2mo0ipD1DZxdPI9/Q/xPh507/x7pTlFvI9JMxADwbWY/Bj7YE4Kr7BZb+edGkyeGPDcYRmH7s17vefqPDAnwU50Br7q9x+Fqa/P7K7WyLYnwqwXRvc75Gi2M17kKpKLVniCAQCASCB4IkSey/kkrCnTyQip0IFSqzDEwAbmr9qNLdsWR06WhXInYN6pgKpIdT5WYNiqTitnSme5qLtDo02pKJX0t9A0zWkh9ZtoeIt3/n0+2XKtWH6kCIsUAgEAgqxa6LKTy5cj8v/vcISMUez0oVq/+KM6trEFelsmS9NsAoTWK3Rr44O5SI87whzeX30VO7M71vEza92IVT8/uz6cUo+dzkXg0BKKL42lIj4/i0XJPjssTYzqhfJ29kUKST+Mfv5y3WvZ+IaWqBQCAQVIq9l/UR6E7eyEDy0qIANDoFn26/bFa3kZ+r/H7ekObsvHCbkZF15TIfVzXRU7tTpJOQJInG/m7yueZB7jQPKlmDre/jbHLd79N6cP3TfwCQlZdHyZVwLbViYpxbWDO2B4qRsUAgEAgqRYGmZP+vTqufHr50O89i3RZGYvpctzC+fK4jns6mIS3r+7rQyM/VRIgt4W0UCtPDyR5/dzVFxWPKXWdvmtT9bKfpD4PUnEKOXEvn0NU0k/LSySwM6HQSh24r6L14N/sup5bbr+pAiLFAIBAIKsWN9BLhlYoD8ZwsDnNZ2kmqV1O/aruvQqFg6kON6RTmzeCWgXg42cvT1Ml3SqJ1SZLEgTi96BpmoW9l5vPYir08/tk+0nMKef7Lw4xY/heXk3PM7gP6BBa/XVdyIz1P3h5lCUmSiIm9u9f33RDT1AKBQCAwQ6uTmPDlIfI1Wr58riPqYqergiItfxRvUQKQir2p41LyAQXdG/sSl6IXuGaB7jg5VK9n8ox+pglYWtXzhQSwV5RMNxtiXAPMHBDOoi3nOJeUJZedTcyUbTgaf8fifZ7+zyHuGM1sp+UU4u5ox4kbd2gb4oVSqWDXhdtM33ic22mW26gMYmQsEAgEAjMu3Mpix/nb7L+SxpGrJWFtfzhaal9wsRin5un/bWI01eyqvv9bhOwd9FPX2qISBy5DqExPZ3taBuuTwNzOKsmVfP5WFqWp5+1schyXmotWKnHuOpuYSdt3Y3hsxT4+KnbwWrTlHKllrEVXFjEyFggEAoEZ14zSGcYmZhJVHFv6bHEKRBlJCwqDGNsR7FWSNOVB7NdV2unFuEnBGTj4BQAOqTmMVsXhbedAWNxZRquumFzjfeYQo1WmcfNDVc5cU5k6fRmzfd0fPFqkAxXk7AF8IuiSGks7lY6+bbzpdY92CDEWWJ2rV68SFhbGsWPHys3KJBAIHhxXjbyRb97Jl98nZ+pHmG8Pbc7HWy+gQr9mnJZTBKjxdSmJj662u/9irFLrvbW7aA9D9GEAQoF37YFCYG/xe2NuwrDSZVnA3bY4G5+PhrkKfVnmuXsPZCnEWGDC+PHjSUlJYfPmzdbuikW+/vprRo8ezcSJE/nss8+s3R2BwKbIyNOw/VwyD0cE3HXUarw1yJD2ECAxUy/MgR5OtKrrgSpBL0SpeXqvZG/XEo/nhnXuf6zn3Nbj+O74BdztCuUgH65qO7ILiqjjqqZtPS+2VsDBKtDDkcSM/LvWM9Cqrgcnb2SgALo29QE2VNECPUKMBTbF6tWref3111mxYgWLFy/G2dn57hdVEY1GU6tiCAsEc386zeYTN3nxVkPeeDi83LrG09SpOSVifLtYjP3d1bSp6y4niCjQ6tdXvZztWTCsBb+eTGRy70bVbIE5av/GzCyaBMY7lIqXj4eEBNLpqXbkHr7O69+fLLMNT2d71o7qyMvFMaqf6liPbw7Gl3/j4vgmHk727B7RGcbfmxgLB64HhSRBYc6Df1VzHpCdO3fSsWNH1Go1gYGBzJo1i6Kikv8FW7ZsoVu3bnh6euLj48OQIUO4fNl0v9/Bgwdp27Ytjo6OtG/fnmPHjlXo3levXmXv3r3MmjWL8PBwvv/+e0CfLKFu3bpmI+WjR4+iUCi4ckW/XpSRkcELL7yAn58f7u7u9OnThxMnTsj158+fT5s2bVi9ejUNGjRArVYjSVKFbNq7dy9t2rSRbfrpp59QKBQcP35crhMbG8ugQYNwdXXF39+f0aNHk5KSUiHbBYKqcDurAI22ZE/w5hP6vbhr/7p612tNRsZZeiclSZJkT+U6bmrC/Ut+DGuL5cTJXsWYLvXZ+EKXSoe2rArO5XhrG+7/RPsQLr0/kEfbBJmcf6ZTPX6f1oNDc/rSuq4HAyMCGN42mFCj4CITuoaWe39P5+qx0epivHz5cjmjTGRkJLt37y63/vr162ndujXOzs4EBgYyfvx4UlNNN2Rv2rRJTuDevHlzfvzxxzLbW7hwIQqF4p5T6N0VTS58EPTgX5qyHRIqS0JCAoMGDaJDhw6cOHGCFStWsGrVKt577z25Tk5ODjNmzODQoUP8+eefKJVKhg8fjq54L2JOTg5DhgyhadOmHDlyhPnz5/Paa69V6P6rV69m8ODBeHh48Oyzz7Jq1SpAn7rxySefZP369Sb1v/76a7p06UKDBg2QJIlRo0aRlJREdHQ0R44coV27djz00EOkpZUEAbh06RLffvstmzZtkoX0bjZlZWUxdOhQWrZsydGjR3n33Xd54403TPqSmJhIz549adOmDYcPH2bLli3cunWLJ554onIPQSCoIEeupdPh/T+Y+o3+x65OV/LD3F5lOZWggXyNlpsZJXuJU3MKGL3qAM3mbaGwWNx9XdXU9ShZH5aK5aSsNIX3C2eHsid4/dxKwm7aqZTU8ymZNj80py/vD29J0wA37FVKFAoFK56N5F+j2pgEF2nq72YiuOEBpoFJPKvpB4dVxXjjxo1MmzaNOXPmcOzYMbp3787AgQOJj7c8PbBnzx7GjBnDhAkTOHPmDN999x2HDh3i+eefl+vs27ePUaNGMXr0aE6cOMHo0aN54oknOHDggFl7hw4dYuXKlbRq1eq+2VibWL58OSEhISxbtozw8HAeffRR3nnnHT7++GNZmB577DFGjBhB48aNadOmDatWreLUqVPExuqTl65fvx6tVsvq1atp0aIFQ4YMYebMmXe9t06nY+3atTz77LMAPPnkk+zbt49Ll/QB3Z955hn++usvrl27JtffsGGDXH/79u3Exsby7bff0r59exo3bsw///lPPD095RE2QGFhIevWraNt27a0atUKhUJRIZsUCgVffPEFzZs3Z+DAgWY2rVixgnbt2vHBBx8QHh5O27ZtWb16Ndu3b+fChQv38lgEAjRaHf/8/TwxsSX7f78+oP8eNaQhNKQSBNPkCJa4lpprMqmWkl3I7osp5BdH3vJwssfRXkWwZ4loaVFW2yixMqiUChztLdvTLtTT5HhgRABO9iqGtg6ijpva4jUAPkZi7O5kR76mZA/zxv/rYlI3yNOJ6sCqa8aLFy9mwoQJspguWbKE33//nRUrVrBw4UKz+vv376d+/fpMnToVgLCwMF544QU++ugjuc6SJUvo168fs2fPBmD27Nns3LmTJUuW8M0338j1srOzeeaZZ/jiiy9MRnb3DXtnePPm3evdj/tWE2fPnqVLly4mv3y7du1KdnY2N27coF69ely+fJm33nqL/fv3k5KSIot0fHw8ERERnD17Vp7ZMNClSxeze5Vm69at5OTkMHDgQAB8fX3p378/q1ev5oMPPqBt27aEh4fzzTffMGvWLHbu3ElycrI88jx69Cg5OTnUqVPHpN28vDyTKefQ0FCzOnez6fz587Rq1cokX3DHjh1N2jhy5Ajbt2/H1dWV0ly+fJkmTZqYlQsEFWXDoessK840ZMjJm55bsv+1oEjL+DWH5OOMPA2SJJU5ijXsw20d4snJG3fMVrsC3PV/63WMxHdk+xAm9om4d2OqgKvannxNgVl5pzDTXPPNAt05NLcvzndxXjMeGbs72tO/eQC/nLhJYz9XPEr94AgPqFr+4tJYTYwLCws5cuQIs2bNMinv378/e/futXhNVFQUc+bMITo6moEDB5KcnMz333/P4MElCaH37dvH9OnTTa4bMGAAS5YsMSl76aWXGDx4MH379q2QGBcUFFBQUPKwMzP1e+00Gg0ajWm2EI1G/4eu0+nkL24A7KrnF1SlkKQqrRsb+m+M4di4XKvVmtQfOnQodevW5fPPPycoKAidTkerVq3Iz883+TyM2zAuK31PA6tWrSItLc1ExHU6HceOHeOdd95BpVLx9NNP8/XXX/P666+zfv16+vfvj7e3NzqdDq1WS0BAANu2bTP7AvL09ESn0yFJEi4uLmZ9qIhNCoXC4udiOK/VahkyZAgffvihmW2BgYFl2n03pOJna+l5Ge4vSRIajQaV6sHnaK0Khv9Ppf9f2TrVbVeRVsephExa1/Xg0q2Svb+FhYXEJmax7VyyXHY9NRuM/uw1Won07DzcHM1Hst8dSWDpNr2w1/V05MR183v7uTno7SgqlKdXZw1ogr2jvVWem6taRUq2adn3L3RCIWnRaEwTQaiVoNUWoS0nP4S7umSk7WwHk3uG4eygZEznenrHTpVC9twe3NKvWmy2mhinpKSg1Wrx9/c3Kff39ycpybIbelRUFOvXr2fUqFHk5+dTVFTEI488wieffCLXSUpKumubGzZs4OjRoxw6dIiKsnDhQt555x2z8u3bt5t59NrZ2REQEEB2djaFhdUTneVBYfijysoyj1DTsGFDNm/eTEZGhixo27Ztw83NDTc3N65evcrZs2f55z//SYcOHQD9jyPQj0AzMzMJCwtj3bp13Lp1Cycn/Y+THTt2APq1WcOPHGPS0tL45ZdfWLVqFeHhJR6gOp2OQYMGsWnTJh5++GGGDh3KW2+9xa5du/j+++/5+OOP5fbCw8O5desWBQUF1KtXz+wemZmZFBQUoNVqTfqQlpZ2V5tCQ0NZv349t2/fRq3WT33t2bPHxKYWLVqwefNmvL29sbMz/W9X+p5VwdLzAv0Xc15eHrt27TJxtLMFYmJirN2F+8K92nU0RUGRBNtvKrmZq2CG+w7q5t5gTvGf1ZWVX5Gao2COXYn63vl6HW+pIN9IkRNWf4VTKQXIKYKc20qeB7CDkHiJNnbmo+ewVIlrKyVUkoaw4rI//tyGpLDOD76kOyoMvzYcVXqRjDv2FwllO1CXiz6Rk/7DOXt0H8520MUOLh6+ykWgT4CS3xOUPNlAy5n9O8jNvXffHKtvbSo9Silv6iQ2NpapU6cyb948BgwYQGJiIjNnzmTSpEmyM8/d2rx+/TqvvPIKW7duNZlWvBuzZ89mxowZ8nFmZiYhISH07t0bHx/TqZD8/HyuX7+Oq6trpe5RE7C3tyczM5PLly+bfI7e3t5MmzaNzz77jLlz5/LSSy9x/vx5Fi1axPTp0/H09MTd3R0fHx++/vprGjVqRHx8PG+//TYATk5OuLu789xzz/H+++8zY8YM5syZw9WrV1m+fDkALi4uuLubT/msWbMGHx8fxowZg1JpujY0ZMgQNmzYwBNPPEHLli2Jiopi2rRpaLVannzySVnwhw4dSocOHRgzZgwLFy6kadOm3Lx5k99++41hw4bRvn171Go1KpXKpA+urq4VtmnmzJm88cYbxMfHyza5urri7u7O9OnTWbduHZMmTeK1117D19eXS5cusXHjRlauXFnlUaskSWRlZeHm5mbx/01+fj5OTk706NHDZv4WNRoNMTEx9OvXr1ZtLasOu/IKtbzy7p/ycRApTC1cafpNngLhwEPGZRnQXoFpPdMAVDKtjesUYFkl8opfxRQp1fTtN0AOTfmgeevYNvKLs0f99koPdJJkFt6ysng1vsWxY0cZNtD8eQ1CPzNhp9J/H93rj2mwohj7+vqiUqnMRsHJyclmI1sDCxcupGvXrrJzTKtWrXBxcaF79+689957BAYGEhAQUG6bR44cITk5mcjISPm8Vqtl165dLFu2jIKCAotfjGq1Wh71GGNvb2/2oLRaLQqFAqVSaSYetsCePXto3769SdnYsWNZu3Yt0dHRzJw5k7Zt2+Lt7c2ECRN46623ZFs3bNjA1KlTadWqFU2bNuXf//43vXr1ks+7u7uzefNmJk2aRGRkJM2bN2fRokU89thjZX5ea9asYfjw4WYjSoCRI0cyatQobt++jb+/P8888wwvvfQSY8aMwcXFNODAt99+y0cffcTzzz/P7du3CQgIoEePHgQGBqJUKmUxM+5DRWzy9PRk8+bNvPjii7Rr146WLVsyb948nn76aZydnVEqldStW5e//vqLN954g4EDB1JQUEBoaCgPP/wwdnZ2VfZANUxNG/7eSmOwy9LfaU3HFvtcEe7FrsRM0+lQd4V+RJYrqflS2x+Ax9rV5ci1dK4a7RM2xsvFgfScQpoFunMuMRPDIlavpn7ka7TsN3L0eijcHzdHO/ZdSaVNiCeO9ipyC4toVKfE90Gr03Ew2ZEODg5We15PdazH57uu8GzneoT5Vc8abs9wf3KuSGU+L+Oi6rBbIUnVvBG1EnTq1InIyEh5FAHQvHlzhg0bZtGB67HHHsPOzo6NGzfKZfv27SMqKoqEhASCgoIYNWoUWVlZREdHy3UGDhyIp6cn33zzDVlZWbLHrYHx48cTHh7OG2+8QURExRwQMjMz8fDwICUlxeLIOC4uTt6yZUvodDoyMzNxd3e3yR8SZfGg7Vq/fj3jx48nIyNDHp3fD+5mly3+LWo0GqKjoxk0aFCtEmNju7Qoef37k9T3dTHLQmTMtdQcgj2d5BHY0fh0Riwv8amJUFzhf+q53JS8iSpYBsDa8R1Yvv0yB6+m0TrEkxPX78j1XdV2dKjvxfbzt83u9e6jEVxLyeE/e+LksvXPd6JrcUzqithlreeVla9h/5U0ujf2rbZ42JWxy6AHGRkZFmf3KoJVp6lnzJjB6NGjad++PV26dGHlypXEx8czadIkQD81nJCQwFdffQXopxonTpzIihUr5GnqadOm0bFjR4KC9Ju5X3nlFXr06MGiRYsYNmwYP//8M3/88Ye8hufm5mYmuC4uLvj4+FRYiAWC0nz11Vc0aNCA4OBgTpw4wRtvvMETTzxxX4VYYLv8djqRX4oDcPxfjwa4qs2/irecTmTSf48yMrIu/3y8NQApWaYew/bovZCKJBUd6ntx6Go6d3I1sid1ZD0vEzH2cLJHVcaP0duZ+SRlmoaD9Ctn+09Nws3Rnn7NLc+o2gpWHfqMGjWKJUuWsGDBAtq0acOuXbuIjo4mNFQf8SQxMdFkz/G4ceNYvHgxy5YtIyIigscff5ymTZvyww8/yHWioqLYsGEDa9asoVWrVqxdu5aNGzfSqVOnB26f4O9DUlISzz77LM2aNWP69Ok8/vjjrFy50trdEtRQrqaUOPyUlZj+33/qPZq/P3JDLjOk66vn7czcwc1QGcQYlRzg4k5uIXfy9NPZpffZNvZ3LXMt9WxSFv87mWhSVt5eXEH1YnUHrsmTJzN58mSL59auXWtWNmXKFKZMmVJumyNHjmTkyJEV7oPBm1cgqCqvv/46r7/+urW7IbARjNdzD1xJY3jbuibnJUkitnSqQkpGxl0a+PB89wbcOuECKaBQ2csBN9JzNXI+33b1vPjXqNZM36gP+/rJU21RKRWs/ks/Fd0pzJs+4X4s/O2cScAQgN5N6zyQcJYCPVYXY4FAIPg7cPBqGvuTFQwCzieVbEUzjgFtwHg0DHDhVhZN/N1ILhZjn+LMSE+3D4YtEORdErIx4U6evAfW09me4W3r8mibYBMnwa8ndmL3xRSm9W3MgStplObCewPvGqVLUL2IT/s+YkXfOIEAEH+D94uMPA2/nLhpkoThbjyz6jDfXFax62IK54zEOD4tl4IiLZPXH2HTkRsUFGmZWSrD0CPL9pCVr5FFvEGxN3OYt16U1Wo1Xs7691du66NfONgpcSp2ZirtrR/V0Jc3Hg5HbaciwMPUse/xyLpCiK2AGBnfBwyed7m5ucKBR2BVDMEIapNXck1g/i9n+PFYAk91DGHhCMux7SVJ4tvD1/Fzd6RtiKdc/tcl08Q2tzLz+e1UEtHFr1e/O0Fp8jU6zidlcaU4zJScrEBbvNVJaS+HcDwafwcAXxeHCm2ZKy3G7mJq2ioIMb4PqFQqPD09SU7Wh6NzdnZ+4JlMqopOp6OwsJD8/Pxat7Xp72SXJEnk5uaSnJyMp6enzYTCtBV+PKZP4vvNwetlivHha+m8sekUAN9NKom/fi1N/wPJ28WBtJxCinQS0zYev+s9E+7kkZmnD2zhZYidrCsWY5U9LYM9TOr7uFbM+cpNbYeLg4ocfdgpsU5sJYQY3ycCAgIAZEG2FSRJIi8vDycnJ5v5AVER/q52eXp6yn+LgvtEyiXIMw9nlXnxFm0Veo/o5DNFtFXonabsEuNpqyigvrMLaVIhmXmW4xo7O6jILSwJoKyLL6SF7hoowDPVG7Ls4HZxxi+lHY38TJOQGCc7KA+FQkGAhyOXb+udytwdhSxYA/Gp3ycUCgWBgYH4+VVPEPEHhUajYdeuXfTo0aNWTW3+He2yt7cXI+L7TF/lEVj2tMVzDwEPGQanh2Cw4X0BoAYMy8blDWCNzx2F4YbjdaXqqexRKBSsGd9Bzs5kcPKqCIEeTiViLEbGVkGI8X1GpVLZ1BeiSqWiqKgIR0fHWiVawi5BdVFk5LTVUKEP3CHZu6Bw8eV6eh6SJKG2V2GnVJBToJ9WdlbbkVtgmqjDRW1HoVaHpkjfnoOdksKish3CnBxU5BVqUSoV1DXOoauyh7ajAUzK/d0rHnHNeN3Yq4IjakH1IsRYIBAIKkFKdkkmNhV68dxc1Ik//OfxS1JxzvJCaFfPU3am6hjkzcGrpluI/q9zA3aevy3nDj4/72FGrtjHqYQMAAZGBNC7qR+vb9J7Vod7u3EuKYtgTyf+mtbHYt/qepUE9GgWWPGwjAFGwh1QCREXVB9CjAUCgaAS3DIKGaksFuMcjSSHtzRgEGKAxMw8SlPHVY1EydYztZ2KzVO6cTurgF9O3OSxdsF4OjuQW1jE/M2x8nao8qaRnRxU9G/uT3xaLn2b+VXYpno+JSJe38elnJqC+4UQY4FAIKgEpmKsF1PdXUI2JGXkm5W1CHLnSooXF25lm5TXcVMzoVuYfOzmaCq+bhbiWBuzckz7cs9bYmirIDLzNEQ19MXJwXaW1WoTQowFAoGgEtwyStagUuhHxjrK99A3RMQypnMDH1oEeeCqtjMLh2lM6ZGw233wdnZyUPF89wbV3q6g4tSeDZcCgUDwAEi2ME2tNfoqXTk60uwaA2qVXpS/n9QFpVKBh7M9cwY3p3lQ2eu7LqVGqq5i61GtRDxVgUAgqAA7zifzrz8umpQZHLiMp6kDPcqOuvd/4VqmPlm5vL/aUiFNLaVbFNg+4qkKBAJBBRhXvH/XGEsjYw8ne4vblJwdVAQ5m25vqggd6nubHIuRce1ETFMLBALBXbC0/7eul5M8MvZwLonO4eFkzz9GtqKulxP9jRLeP9WhLs5V0FFHexWfG019382BS2CbCDEWCAQAJGbkodOJLE+WKJ28wdFeyYu9GspiXGT0VermaMewNsHseaMPL/RsKJdP7B5GVTGOFy2mqWsnQowFAgH/O3mTLgu3yUnnBaZsLrWH+OEWAYyMrEuPxj4A5BpFvFUqSzyrI0O9+H1aD07M64/PPUS2cjfa3uTqKCKt1UaEGAsEAl7++hgA7/161so9qTlodRIZZSRxUCoUqO1UNPLVO2v1CvdHoYAXephvD2oa4IaH870JqLtTyWjY20WIcW1EzHcIBALcHO3Iyq+8c1FtZtrG42w5ncjW6T3NzhUY4lPr9FmVGvl7sG/WQ/hWIjlDZfBzcyTE2wlNkURkPe+7XyCwOYQYCwQC6ripZTHOK9TWyihMkiRVOH2mJEny1PRnOy6bnX+6Y73iisUpDhUqk2QL1Y2DnZI/Z/SiSKfD2UF8bddGxDS1QPCAWPtXHMt3XLJ2NyyiMhKpGd8et15H7hPv/i+WsNnRdP1wG1duZ9+1/u3skihbV1P1qQWdHVSceWcAv73Sna6NfPUndcUjZOX9/yp1sFMKIa7FCDEWCB4ASRn5zN8cy0dbzpOYYZ40wNoYJ7H/7XSSFXtSOQqKtEjS3T3AV+3RO6Yl3Mnjw9/O8fuZJP67/xoA0acSmbD2EDfSc+X6l4ziRR+I02db8nJ2wEVtZ5oNyWhkLBDcC+JnlkDwADCkyQNIzMgvN0qTNcgptL314qSMfIYu20PzQHe+fK5jmfXScwpNjq+k5PDCuiMAdArzZtqG4xRqdaTlFvLj5K5otDqWW5iaDjXKbCQjGUbGQowF94YYGQsEDwBjr9wRy/dy807NGh3nGCW+V9vpvxYSM/LIMxox1zS+P3Kd21kF7LxwG43WPCgHwJbTSbR9N8ak7FJyyaj3yLV0CouvPZuYCcD6/dfYcynFrC1fV7VZmcGBC4X4KhXcG2JkLBA8APJLidqX+64ye2AzK/XGlMIinUlWoYIiHYeupvH4Z/sAOPDmQ/jXsITzey+n8M+tF3ha9Scz7TaiWqSAUs5ZGq2OKI2Wk8UaaqdUoNUZZxAGhy1KBqlLhFxaaMfjhVpGqM2nvl3i7GBhKQcwTfHUtpimFtwjQowFggdAnsZUjK+m5Fisp9HqsFMqKuz1W1GW/nGR7AINbw5qZta28ajYwHeHr8vvNxy8zit9G1drf+6V177VR8QaotyHlyIbCs3r2AP2xqZKYJbpUAeOxmUF4IKFemDxHoBeiAMiKtZxgaAMrC7Gy5cv5x//+AeJiYm0aNGCJUuW0L179zLrr1+/no8++oiLFy/i4eHBww8/zD//+U98fHzkOps2beKtt97i8uXLNGzYkPfff5/hw4fL5xcuXMgPP/zAuXPncHJyIioqikWLFtG0adP7aqvg70tpMVYpzb/tM/I09P/XTvzcHPn5pa4mkZwqw4xvj3MpOZtvX+iCo72K62m5/OuPCwCMjapPXS/TtU/DerHaTonaTklmfhE5BSX9jUu5u/fxg6agOFa0IZ/w1Y7zqd/pEfn8r6cS+ej38ybXvNCjATfv5LH5ZGKZ7S4Z1Yafj91k+4VkALZO6yFP25eJoye4+JRfRyC4C1Zd6Ni4cSPTpk1jzpw5HDt2jO7duzNw4EDi4+Mt1t+zZw9jxoxhwoQJnDlzhu+++45Dhw7x/PPPy3X27dvHqFGjGD16NCdOnGD06NE88cQTHDhwQK6zc+dOXnrpJfbv309MTAxFRUX079+fnBzLoxWB4F7JLyXGlpLN772Uwq3MAk4lZJBQxTVlnU7ih6MJnLyRwY7zt4GSrTkAt7MKzK4xCK+r2k5OZH8treSazScTuZNb1rDwwfDjsRtsP58sH3sXh5Y0xIb+96Fs8Gkov84X1uGaFMA1KYCO7dpzTQqgZ5fO+NZrJpdbeg3fkMTa80quSQG88Gg/1P6NTdq1+BJCLKgGrCrGixcvZsKECTz//PM0a9aMJUuWEBISwooVKyzW379/P/Xr12fq1KmEhYXRrVs3XnjhBQ4fPizXWbJkCf369WP27NmEh4cze/ZsHnroIZYsWSLX2bJlC+PGjaNFixa0bt2aNWvWEB8fz5EjR+63yYK/KaVHxqnZpqK4+cRNXlx/VD7OzLcchvFuGEfRSiv2Ik4xupdFMS4eGTurVXIM5GspJdt8tDqJ/VfSqtSf6uB0QgbTN55g/JpDcvYkg9OVQYwzCySTHwzX0/U/ZmYOaMo/Hm/N1Q8HE+zpRPMgD7P2B7cMZFDLALPyIM+atU4uqN1YbZq6sLCQI0eOMGvWLJPy/v37s3fvXovXREVFMWfOHKKjoxk4cCDJycl8//33DB48WK6zb98+pk+fbnLdgAEDTMS4NBkZGQB4e5cdZq6goICCgpIvssxMveelRqNBo6naF2dNxGBLbbIJrG9XbrG4ejnbk56rISW7wKQvU745ZlL/Tk4+Go2FrTTFpGYX4OFkj1TszWtoKzMvX66Tkau/x1d7r8pltzLy+OFIPEv+vMSnT7UhPMCNjBz937WLvQo3R70jUlbxOrKq2Onp0q1MNE0fzAiw9LO6mJQhn7uQeIemAW5kFGdmcHVQQhHoUHD6RjqdwvT/h2/e0f+Y8HdzMPmcWwS4mN1vZLsgLiZnE33KdH+1u1pZrX8v1v4bvF8Iu6rHdquJcUpKClqtFn9/f5Nyf39/kpIsBx2Iiopi/fr1jBo1ivz8fIqKinjkkUf45JNP5DpJSUmValOSJGbMmEG3bt2IiCjbCWPhwoW88847ZuXbt2/H2bnsL01bJSYm5u6VbJAHaVdCDqy+oKJngI4bOQpASROXAg7kKrl1J5fo6Gij2qb/FXf8dYCUWMvBLM5nKPgsVkn3AIkRYfqRocGu5LyStg6fOod/RizHrpe0vffoKX67oRfcoZ/uY1wTbfH0mIqC3CxyCyWMJ8zqOuu4lq1g9/HzOKWcxfcBDhYNNv2ZoAD0ff75jz208JLIzFcBCtxVhcVirOTn7QdIPav/zK4l6c9fiT1OdILpD51RDRTsTFTSwkvCw0Ei4/wB/IA23kqOp5XYfvzAX1y/D/aK/1u2RUXsys3NvWudu2F1B67Snp3lxY+NjY1l6tSpzJs3jwEDBpCYmMjMmTOZNGkSq1atqlKbL7/8MidPnmTPnj3l9nP27NnMmDFDPs7MzCQkJITevXubOI/ZOhqNhpiYGPr164e9fe3JDmMNu57/6igp+SlsuqpicMsAuJ1Ep5aNObDtMgU6Bb37DsDJQUVqTiHs22FybXhEGwa1DjQpKyzSkZWvYe3Xx9GRwc4kBSv+7yETu87czITj+wHwCgihZef6sP8vuQ2voFC4cUM+XntBxfyhzeDCWeoF1CHUx5lT+0t8NqKahXDt0A0O3FZy4LaSpU+0sjilW52Uflb7fomFeH2fvziv4tS8h9Dt/xMAH3cXuA1alNRr2JRBPfVZk+af2A5oGNynG0383UzaH1TGfQcPht6Ld3OjeIp7+OD+1Zo7WPzfsi0qY5dhpvResJoY+/r6olKpzEasycnJZiNbAwsXLqRr167MnDkTgFatWuHi4kL37t157733CAwMJCAgoMJtTpkyhV9++YVdu3ZRt27dcvurVqtRq803/dvb29eqP0ADwq5757qRE1ZhscNWgIczDnZKCot0ZBTocHdxpPOHW82uzSuSzPr54teH2XXxtrxuCsh1DHYVSSU/Ou/kFXEn3zQYRlqO+Tam+Zv1aRPruDvS1DjUI9AhzIdvDpWI9yvfnqR7U3/ZgQr0a8qWvMPvFYNNNzNM17nj0kqm4lUK/eeqRSl/ZkVaHenF09j+ni6Vet5FRo51ni6O1b7FDMT/LVujInZVh91WE2MHBwciIyOJiYkx2XYUExPDsGHDLF6Tm5uLnZ1pl1Uq/fSVIT5tly5diImJMVk33rp1K1FRUfKxJElMmTKFH3/8kR07dhAWFlZtdgkEBpRGX+QGb2pnBxW+Lg7czMgnNafQJNNPy2APAj0c2Rp7i2wLe3//OHurVImE4sZBAtMPojhbBCoVzomZDFTqk1E0SHPB4fxpBiqvlFxyFgaW4bbZMe8KbXO9GKi8IJcNsSvggPMZE4ey79ed5/+660eg3xy+wYq4QFZPHkAjP9dKZUaqKAnpplOA8Wn6Y2cHFYriNXMdSvkzMziuKRX6eNKVYXCrQFbtiWNQy4D7IsQCQVlYdZp6xowZjB49mvbt29OlSxdWrlxJfHw8kyZNAvRTwwkJCXz11VcADB06lIkTJ7JixQp5mnratGl07NiRoKAgAF555RV69OjBokWLGDZsGD///DN//PGHyTT0Sy+9xNdff83PP/+Mm5ubPJL28PDAyalmxQwW2CaXkrNMvJgNQuFor8LXTa0X4+wCkzCZ/xrVRk5ekF2B3MLtFBex+3I+HQGu6suaAysM+pMB7DM6vhtX9C+T+j/AIgDjsiTgO/3bp4AQbQv+FRPGwJYBzPnxNJ8+3Y5ujX0reNPykSSJxAz9SNjgTJacqf9cXdV2cqIGnaSQP+OUbL0Ye7uoKz1in9qnMfV9nHk4IvDulQWCasSqYjxq1ChSU1NZsGABiYmJREREEB0dTWhoKACJiYkme47HjRtHVlYWy5Yt49VXX8XT05M+ffqwaNEiuU5UVBQbNmxg7ty5vPXWWzRs2JCNGzfSqVMnuY5h61SvXr1M+rNmzRrGjRt3/wwW/C24kZ7LoH/vMZlOvlUsKE4OKnyKp3hTsgu4Uzyd6u5oRyM/V3mNsvTWpiILsZfrK/Q/IjMkF85JIQD4uDjo16DRh38M9HDkenoermoV2QXlx5mOrOeJSqng4NV0AEK9nQkoDoMpIcnldkoFkfW8oCALbp0iSJFKTmERL3+td5R6/qtDnHt3YIU+q7uRkaeRM0o1rOPChVvZJGcZiXHxyFiLUv4BY/gR5OtauVExgIezPaO71K+GngsElcPqDlyTJ09m8uTJFs+tXbvWrGzKlClMmTKl3DZHjhzJyJEjyzxfkZRrAkFlyC0sYvrG47Sq60kjP1cTIQa4WSzGjnZKOeFASnYhGXl64fQsnk71dNavPWXkacgr1LL+wDVCfVzoUN/L7J6uCv2a9G5dBC9rXgEg2NmJhMKSteqWPh6cKsxgdLtQ1hWPui0xom0wnUa1AeDrDcc4fv0OP03sCsU/HBRAZuwtJn51GJVSwf7HH8LnzimUqx7CQVGEzui/VL7GctKGqmAIfuLr6iBPOSdn6T9LF7UdFP9I0aGU90un5hjE2EJiB4GghmJ1MRYIbBZNPlzZDpo8dp9Jwu5sIrFnIbJLKIOVloUv8EY6XQvukKu8hV/8ZRwK3RisvEI9lTOcTifiThqDldeon+jKa/N/AOAYEDG4GYOVZ7G3U7DkiTbM2nSKyKKLAGRLJUsrtzLzTe53KkG/R7dTA28TMW7i70p9Hxe2xurXoQ2RtwCWPtnW4tpvzyZ1UCj0Dlsd3v+D5oqrRKvBDi06XdV+4OYWFnHhVjZtQjwtnr9evD4c7OmkF18jG90c7SBbL8bGI+Ozifp0lT5VGBkLBNZCiLFAUFV2/QN2/xOAAcAAw3f/Eehclg5sg3rAow5AnP71qQOQBXwPnSm+NgPTddqY4nro630Ihq23ZFISyKKoDFHs1siXGf2asDhG75xVUKTD161k5OjmaPpVYMl5ycFOSYC7o7yGqyn++rCnyKLDWUWY+9NpfjiawD9GtuLx9iFm588n6eNiN/Z3k53gEoq3Hnk5O0BmyTT1mRsZbDpyg5W79A5rNS1ntEBQHkKMBYKqklWccMCrPmdy3MnM0wuSp4s9d3IsR+RpX9+LO3kaLt3KxtXRDh8XB66l5uLr6kBjfzcy8zWcSTDfsxjk6cjNO/k4OShpE+LFiRt3yC3QUi/Al403epXbTRcHFR5O9kx9qDFf7btKSnYhEUEe+BptTzKEwbwb9bydjcRY/2vAHi3Hr9+R6xjr+OXb2ey7nMpTHeuZOVOduZnBD0cTAPjn1vMWxdiQpKJhHVeuFcfYNuwD9nS2lx24pOI0S69+d0K+tns1OZEJBA8CIcYCQVWR9FOkUvsJPBXTlEyNXoxDlE5c11hO9HBh7EAy0nJ4avEu0MCT4SFsSLrOmPBQFgyLIDk5S3+uFP3r+LP19i3aBHjy07iuvLtiL4evpTO2bj0uXzdPrDKlTyM+2abf4lTXy1ke6X76dDv+dzKR1/o35ecTCXL90iPjsjBehy0yGhkbozJS4/FrDhGflsvcn06z6LGWjOpQTz731k+n5fdlBde4eSe/2AYnOa62YfTv5exg4sBVmq6NhBgLbAerJooQCGyaYjH+z56rZBptRbqephfiNweFE+JdMlUa6qMP+NHIz426Xvpyw4jSs3jNtiynoyvF+Y8NomnYn/zlPssZzp7rWrJ33sulZNTbqYEP7z4agYezPT4uJffycKrYyNhYtAulMsTYaARs2BMM8MamUyaObUfj78jvXcsYmRscuII8nXBVq0zOebk4yCNjb1fTKel/jWp9V1sEgpqEEGOBoKoUe+UnZZpnQgLwdHLAQVXyX+x/U7rJ74M99eJxLknvbOQhe1NbXmy+lKyfrjVsi2phIfuQgQ71vWSv7PKo71sSU93PvWKex892DpXfFxVPU9spdCgoEVnDwNh4D7UBg6cz6LcqGUjPMU/RqNVJJBU7awV7OuFcavTcPNAddPr7vvWIPq58mxBPDs/ty/C25UfUEwhqGmKaWiCoKpJhW43lwBLuTvZ8NLI1L60/yuxB4bgZjf6Mw0mCPpuTgW6NfNlzKQWA4W2D+fFYyXSyT/HIuXR6v0k9wvhsV5y+josahULBGw+HszjmPFMfamyxf+EBJaEvw3xdy7e1mIhgD76Z2JmnvthP0yAvKM6sOEh5UBZntKCN1ZCWmssA5TmT6zWns8Fb/yMgMvckjZR6wXbMUcFZ/Q8ORZGWwDtHyDpeQF9iUaoU1EnQEn4nhQHKkpmATgVa0OrFvXmQFyfnt8ZNbSciZwlsEiHGAkFVkUr2uFqijpuayFAv9r/5kNm50iPXIM+SadZ3hrXg463nmdAtjNjELBMxNtQrPZ3dyK9ETJ0d9KI4qWcDJvVsUKY4qZQK/prVh7xCrdmPg/Lo0tCH7a/1wl2lgaX6sk8d/m1a6VsIAz4v3axRApyPwNRjfKP+HzvQRxWLM7r+W+gF9DKu/62xMQ4VdkITCGoiQowFgqpiNDLuGObNosda0fufO+TT9bzLTq1Zejo62EiMG9ZxZfkzkQAk3DHdNzy0OJNT6T20/m5q7JQKinQSTQL0WYoqMkI0vm9lCPMtnmLu9SaFF/4gr3jb0YVbWeVe18DXFR8XByTg8LU0k3NtQzyxUyrRSRLp6Wng4MqV1Bzc1HaEB7hTUKTlVEImEhKR9bxKYn8HtQEPMS0tsG2EGAsEVURTpMUe/baaz56NxNvFAX93Nbcy7x6O0dPIYUqhAH93y4lzjR2m2tbzxM9NX8/Y+Qr0I+11Ezrx+5kknjLyWL7v9HoDh15vyAPcx2f9Wm71+ZHNGdc1jMxcDY8v0GerMsSc3jWiN/V8nNFqNOyJjuaGWzP+sfUijzYPYsmTbVEDqvh0vJwdUPq6lHsfgcDWEGIsEFSRK7czaQp4u6rlNV+NUQq+8kamxtmEfF3VONhZnup2M3JaMl7jLT2t7OlsT8sQN7o0rFm5td3Udswd0ow3Np0CSjIqZRXo14oNgUTi03K5mZGHk4OKT7ddICdJgatCX9f4h0rbeuZhQQWC2oDwphYIKolWJzF61QGuFm836ts8UBbeUR30gSuGtQkqtw0PozXjgDJGxYCJ05efUcSs0gE0Kpsq8EHxXLcwRnWoJzuRpeXqBdYQscvd0Y4m/vr17pW7rtDh/T9Yuy+e7+JURnGpRYxpQe1HiLFAUEmu3M5m98UUlOhHwc0CS7YZTe/bhI8ea8VbQ5qX24axeBqLbGlcjaapy9t+pC5jZG1tDH02bMn6sTjiVlbxvmxXtR2N/PRr3NvOJZtcuzVWf1ynnM9HIKgt1Mz/wQJBDcYQCcqwt9berkQwHeyUPNEh5K6juUCPktFweXWN14w9nUxHv3/M6AFAQ7eak4Vs1sBwk2PDGnfj4tFvkU4it7CIzOI9yG6O9iae4JYQYiz4OyDEWCCoJIbpU8PImCrsaw0wEmOJssXU1aFEjI2jeQE08nNj+4zuvNCs/DzFD5IXejSgT7iffGwY9Xdp4IOfm5qCIh0nb2TIP2jquKkJ8y3b69xQRyCo7dyzA9eNGzdQKBQEBwdXR38EghrP+9FnAWMxrvxvWnujyFzlRdNSKhWsm9CRS8nZtAw2r1fXy4mTKgsXWgmFQkGoj3lkL4VCQX1fF5KzCkjJLpA9zv3c1AQYZVdS2ykJ9XbmQnHEMYA6Ys1Y8DegSiNjnU7HggUL8PDwIDQ0lHr16uHp6cm7776LTld9icUFgpqGRqvjTq5+irWRX/H2miqIMejDYz7fLYzHIsvfI9u9cR3Gdw2zychSxlPwhq1eqdmFJGfp90/7uakJdHfErtgh7euJnQkv3idtoKJxswUCW6ZKI+M5c+awatUqPvzwQ7p27YokSfz111/Mnz+f/Px83n///erup0BQI7h5pyQbU7CHGlKpshhHBHsQYWG0a+tkGyXNMJ4BMOyNTs0uINkwTe3uiFKp4NIHg0quUUj8clKfnjKqoQ9Kpe39CBEIKkuVxPjLL7/kP//5D4888ohc1rp1a4KDg5k8ebIQY0Gt5WyiPsJUmK8LCskwCyTEwpiHmvnx3ZEbZlu2vIo9qlNzCmUxtuRJ3izQjX90LKJHn754uVYtQphAYGtUSYzT0tIIDw83Kw8PDyctLc3CFQKB7bH9fDLbzyUzZ3Az1Hb6hdmfiuNEezjZy+Ewq+LAVZsZ0CKAteM7mK2FG6abM/I0JBdnYyor8piDSh/YxL6GbtkSCKqbKv2lt27dmmXLlpmVL1u2jNatRR5RQe1g/JpDfLXvGl/uvSqXGWIwt6rrIadQrOo0dW1FoVDQq6mfmRe0sRjfLmdkLBD8HanSyPijjz5i8ODB/PHHH3Tp0gWFQsHevXu5fv060dHR1d1HgcCqnE8q8ew15OPt3dQP9gkxrgwGMb6WmkuRTv/ZiehaAoGeKn2L9OzZkwsXLjB8+HDu3LlDWloaI0aM4Pz583Tv3r26+ygQPHAkqWTvb76mZB9varY+nKOPq4PRNLUQ44rgXhzAJD4tF9BPQ5cVk1sg+LtR5X3GQUFBwlFLYDOk5RSy6ISKK06Xmd7f3N+hNHlGApxVHEdZkiQjMVaLNeNK4lEqh7OYohYISqiwGJ88eZKIiAiUSiUnT54st26rVq3uuWMCQXWh1Un0WbybnEIFS7dd5qU+Te46IjNkFwJkZ6PM/CIKtXoB9nERI+PK4u5YSozLSZAhEPzdqLAYt2nThqSkJPz8/GjTpg0KhcJkKs+AQqFAq6054fkEgv+dvElOYcnf5Ff7rvJ89wblXpOeo5HfX0nJobBIR2q2fr3YVW2Ho71KiHElKR28Q4yMBYISKizGcXFx1KlTR34vENgC11JzeGXDcZOyNX/dXYwNqf4ACot0XEvN4U5xcgOf4khSQowrh7ODadzO0iNlgeDvTIW/RUJDQ+VwfNeuXSM4OJjQ0FCTV3BwMNeuXatUB5YvX05YWBiOjo5ERkaye/fucuuvX7+e1q1b4+zsTGBgIOPHjyc1NdWkzqZNm2jevDlqtZrmzZvz448/3vN9BbaHJEn0/McOs/KEO3kkZ+bT/aNtfFAcZ7o0hlGwgUvJ2SQXx1M2pAOUtzaJoB8VonQ4T1d1DQqqLRBYmSr9pO/du7fF4B4ZGRn07t27wu1s3LiRadOmMWfOHI4dO0b37t0ZOHAg8fHxFuvv2bOHMWPGMGHCBM6cOcN3333HoUOHeP755+U6+/btY9SoUYwePZoTJ04wevRonnjiCQ4cOFDl+wpsE+N1X4DBISVT1b+dTuJ6Wh4rd10ht7Co9KV8d/iGyfHey6nsvKDPr1vPuzgRghgZV5oeTfSza3Xc1IyJqm/dzggENYgqeVNLkmQxaH1qaiouLi4Vbmfx4sVMmDBBFtMlS5bw+++/s2LFChYuXGhWf//+/dSvX5+pU6cCEBYWxgsvvMBHH30k11myZAn9+vVj9uzZAMyePZudO3eyZMkSvvnmmyrdF6CgoICCgpLRUmZmJgAajQaNRmPxGlvEYEttsOnq7Sz5/Yg2gXRxuM7OZBXZBVquppTsHd57MZmexSIB+q1M+67oZ1taBbtzMiGTvZdT5DXPrg290Wg02Ol0KIAinQ7JSp+XrT2vFU+1JrugCO/i2QVL/bY1myqKsMu2qIxd1WF7pcR4xIgRgH66ady4cajVJQ4YWq2WkydPEhUVVaG2CgsLOXLkCLNmzTIp79+/P3v37rV4TVRUFHPmzCE6OpqBAweSnJzM999/z+DBg+U6+/btY/r06SbXDRgwgCVLllT5vgALFy7knXfeMSvfvn07zs7l52O1RWJiYqzdhSqRXgBLT6to5yvh6ygBKsLcJHo6XQfAUVFENgoOxMZhmBha/fsRci6VZBu7kQOG/xqDfNM4mWDHjbRssuwBFMSfO070zeP0yszAAzh48BC3z+VhTWz1eZVHbbQJhF22RkXsys3Nvef7VEqMPTz0sWYlScLNzQ0np5Ig7g4ODnTu3JmJEydWqK2UlBS0Wi3+/v4m5f7+/iQlJVm8JioqivXr1zNq1Cjy8/MpKirikUce4ZNPPpHrJCUlldtmVe4L+hH2jBkz5OPMzExCQkLo3bs3Pj4+FbLZFtBoNMTExNCvXz/s7Wu4g03iCZSXtpoU7budxmhtOtzSH09VQVN3V/o51+Hy5cu86uxEoqYAD60dfVX66Wn3XDsGu4aiUEC+RsefV5OZqsoh0EPNcN9gclVX9I1pABU86xmCj4sDSqVegDt27oxUv8eDstoEm3peFaQ22gTCLlujMnYZZkrvhUqJ8Zo1awCoX78+r732WqWmpMui9HR3WVPgALGxsUydOpV58+YxYMAAEhMTmTlzJpMmTWLVqlWVarMy9wVQq9UmMwEG7O3ta9UfoAGbsOvnFyD1kklRN6Bb6W6nAn9BM/Qv7NELq6FeEVDsv+cCPGKok6u/bkbp9o6YHto5eYCVPyubeF6VpDbaBMIuW6MidlWH3VVaM3777bfv+ca+vr6oVCqz0WhycrLZqNXAwoUL6dq1KzNnzgT0wUVcXFzo3r077733HoGBgQQEBJTbZlXuK6ih5N3R/9v8UXDyAuBMYibH4++YVOsY5k0DX2fi4+O5pXPj8u0cs6Y6N/CmYR1Xvj9yg4Ii/ZR16xAPIoI8iD6dKO87VijgyY71SjwfvUIhqF312yYQCP5WVDkc5vfff8+3335LfHw8hYWmXqtHjx696/UODg5ERkYSExPD8OHD5fKYmBiGDRtm8Zrc3Fzs7Ey7rFLpt0cYApB06dKFmJgYk3XjrVu3ymvZVbmvoKZSvLWo1yzwawZA9O/n+PTKZZNa3/bpgq6uGyejo7nqHM6//iwZTTvZq/ShLy/AWJ9QtjgkcStf76i3tFMbItoEs6foFF8f0Hva+7mpeXpo3wdgm0Ag+DtRpT0Z//73vxk/fjx+fn4cO3aMjh074uPjw5UrVxg4cGCF25kxYwb/+c9/WL16NWfPnmX69OnEx8czadIkQL9OO2bMGLn+0KFD+eGHH1ixYgVXrlzhr7/+YurUqXTs2JGgoCAAXnnlFbZu3cqiRYs4d+4cixYt4o8//mDatGkVvq/ARrCwzzcr33ybUkSwu/w+zNfU2a5BnZKlli/3XeNWZonHfIsg/XUPtwiQy/zcRdQogUBQ/VRpZLx8+XJWrlzJU089xZdffsnrr79OgwYNmDdvnsX9x2UxatQoUlNTWbBgAYmJiURERBAdHU1oaCgAiYmJJnt/x40bR1ZWFsuWLePVV1/F09OTPn36sGjRIrlOVFQUGzZsYO7cubz11ls0bNiQjRs30qlTpwrfV2ArGFIY6sX40NU0vtqnDzrTxN+VC7f025ecHezkrQcN65j6OTSo48qZm+bOF1P6NKKRnxsA9X1KrnFT1741MYFAYH2qJMbx8fHytK+TkxNZWfr9nKNHj6Zz584sW7aswm1NnjyZyZMnWzy3du1as7IpU6YwZcqUctscOXIkI0eOrPJ9BTZCqZHx45/tk0+N7lKf+NQcOjcw9XRvVMeV7o192X0xRT62xJgu9eX3QZ4lCQ0GtQywUFsgEAjujSqJcUBAAKmpqXIYzP3799O6dWvi4uIsJo8QCO4PpiNjY7ydHRjd2XymQ6lU8OX4jrz8zVEUKGgd4mFWx89NTR2jJAZ2KiVvD23O6YRMRnWoV33dFwgEgmKqJMZ9+vRh8+bNtGvXjgkTJjB9+nS+//57Dh8+LAcGEQjuO/LvPgVHrpkuj7g5lv2nrVQqWP5MJAAXbmWZnf/l5W5mZeO7hlW5mwKBQHA3qiTGK1euRKfTb/+YNGkS3t7e7Nmzh6FDh5p4KAsE9xe9GhfqJB5bsc/kTHlibExdLycUCqMZbyDAQ+TZFQgED5YqeVMrlUqTLUZPPPEEb775JhcvXqRJkybV1jmBoFyKFfR6unkoOrcKpudzdrBjzbgO8rS0cYxqgUAgeFBUSozv3LnDM888Q506dQgKCuLf//43Op2OefPm0bBhQ/bv38/q1avvV18FglLoxfhOnvl2Jv9KbEHq1dSP7a/14sMRLVn+jAjgIRAIHjyVmqZ+88032bVrF2PHjmXLli1Mnz6dLVu2kJ+fT3R0ND179rxf/RQIzCkeGedrSpI8PB5Zl45h3hUeGRtwVdvxZEfhnCUQCKxDpcT4119/Zc2aNfTt25fJkyfTqFEjmjRpImdEEggeLHoxztPo/+3cwJt/PN7amh0SCASCKlGpaeqbN2/SvHlzABo0aICjo6OcE1ggeOCUGhk7O1Q5uqtAIBBYlUqJsU6nM8lOoVKpqiVzk0BQFaTikXGuRguAk4PKmt0RCASCKlOpoYQkSYwbN05OJZifn8+kSZPMBPmHH36ovh4KBBaQJAlNkRYHIC5V703tIsRYIBDYKJUS47Fjx5ocP/vss9XaGYGgopy/lUWYJIECDlxJB1zENLVAILBZKvXttWbNmvvVD4GgUpxPyqJh8TT1zYx89GIsRsYCgcA2qVLQD4HA2ly5nYOiWIyl4kQR7k4io5JAILBNhBgLbJLkrHxZjHXFYlzREJgCgUBQ0xBiLLBJ0nIKKcnVpH+ntJC9SSAQCGwBIcYCmyQ9R4NSUTJN7eygolsjXyv3SiAQCKqGmNcT2CRpuYXy+z2z+lDg4IOHs1gzFggEtokQY4FNkpZdIL93tLfDUQixQCCwYcQ0tcDmKNLqSM8tMCoRa8UCgcC2EWIssDm2nEkylV/huCUQCGwcIcYCm+NgXJq8rUkgEAhqA0KMBTaHfluTkRiLkbFAILBxhBgLbI6s/KJSq8RCjAUCgW0jxFhQ4zl1I4PTCRnycVa+RoyMBQJBrUJsbRLUaPI1WoYu2wPA6XcG4Kq2Kx4ZG68ZCzEWCAS2jRgZC2o0N+/kye/PJWYC+mlqE8TIWCAQ2DhWF+Ply5cTFhaGo6MjkZGR7N69u8y648aNQ6FQmL1atGgh19FoNCxYsICGDRvi6OhI69at2bJli0k7RUVFzJ07l7CwMJycnGjQoAELFixAp9PdNzsFVSMxI19+H5+WC1iYphYjY4FAYONYVYw3btzItGnTmDNnDseOHaN79+4MHDiQ+Ph4i/WXLl1KYmKi/Lp+/Tre3t48/vjjcp25c+fy+eef88knnxAbG8ukSZMYPnw4x44dk+ssWrSIzz77jGXLlnH27Fk++ugj/vGPf/DJJ5/cd5sFlcN4ZJyQnodGqyOnUCvWjAUCQa3CqmK8ePFiJkyYwPPPP0+zZs1YsmQJISEhrFixwmJ9Dw8PAgIC5Nfhw4dJT09n/Pjxcp1169bx5ptvMmjQIBo0aMCLL77IgAED+Pjjj+U6+/btY9iwYQwePJj69eszcuRI+vfvz+HDh++7zYLKkWQ0Mr6RnidPUQtvaoFAUJuwmgNXYWEhR44cYdasWSbl/fv3Z+/evRVqY9WqVfTt25fQ0FC5rKCgAEdHR5N6Tk5O7NmzRz7u1q0bn332GRcuXKBJkyacOHGCPXv2sGTJkjLvVVBQQEFBSQjGzEz9+qVGo0Gj0VSov7aAwZaaYtON9Fz5/fX0HFKz9Mcu6pLfkZqiIlCU39+aZld1URvtqo02gbDL1qiMXdVhu9XEOCUlBa1Wi7+/v0m5v78/SUlJd70+MTGR3377ja+//tqkfMCAASxevJgePXrQsGFD/vzzT37++We0Wq1c54033iAjI4Pw8HBUKhVarZb333+fp556qsz7LVy4kHfeecesfPv27Tg7O9+1v7ZGTEyMtbsAwImLSgwTOBcSUtkcsxOww4mSP/4tv/+OTulQofZqil3VTW20qzbaBMIuW6MiduXm5t61zt2w+tYmRan1PkmSzMossXbtWjw9PXn00UdNypcuXcrEiRMJDw9HoVDQsGFDxo8fz5o1a+Q6Gzdu5L///S9ff/01LVq04Pjx40ybNo2goCDGjh1r8X6zZ89mxowZ8nFmZiYhISH07t0bHx+fSlhcs9FoNMTExNCvXz/s7a2fCWlR7C5AP1Wdkq8g3a0BEE+Yvxck6+s8/PBAsFOX205Ns6u6qI121UabQNhla1TGLsNM6b1gNTH29fVFpVKZjYKTk5PNRsulkSSJ1atXM3r0aBwcTEdEderU4aeffiI/P5/U1FSCgoKYNWsWYWFhcp2ZM2cya9YsnnzySQBatmzJtWvXWLhwYZlirFarUavNv/Dt7e1r1R+ggZpgV05BETeN1owB1u3XO/cFeahlMba3dwC7ivW1Jth1P6iNdtVGm0DYZWtUxK7qsNtqDlwODg5ERkaaTQHExMQQFRVV7rU7d+7k0qVLTJgwocw6jo6OBAcHU1RUxKZNmxg2bJh8Ljc3F6XS1HSVSiW2NtUwLiVnA+Dt4sDozqEm59rU8yo5EN7UAoHAxrHqNPWMGTMYPXo07du3p0uXLqxcuZL4+HgmTZoE6KeGExIS+Oqrr0yuW7VqFZ06dSIiIsKszQMHDpCQkECbNm1ISEhg/vz56HQ6Xn/9dbnO0KFDef/996lXrx4tWrTg2LFjLF68mOeee+7+GiyoFIeupgHQqq4HozqEsG7/NQDUdkoGtwyAPw01hRgLBALbxqpiPGrUKFJTU1mwYAGJiYlEREQQHR0te0cnJiaa7TnOyMhg06ZNLF261GKb+fn5zJ07lytXruDq6sqgQYNYt24dnp6ecp1PPvmEt956i8mTJ5OcnExQUBAvvPAC8+bNu2+2CirPtVS9U0REkAc+riXLEWG+Lrg6qEoqipGxQCCwcazuwDV58mQmT55s8dzatWvNyjw8PMr1XOvZsyexsbHl3tPNzY0lS5aUu5VJYH2y8vUe0+5Odng5l4ixu5M9SCICl0AgqD1YPRymQFAWhgAfbo72ONqXjIRdHFQgInAJBIJahBBjQY2lRIz1Ezg+LvrRcRN/N9ORsRBjgUBg4wgxFliNA1dSGbP6IOeTsiyezyooGRkDzH+kBQNa+DO5VyNMRsYCgUBg41h9zVjw9+XDLec4Fn+H+NQcdszsbXbesGZsGBkPbR3E0NZBxScNYixGxQKBwPYRI2OB1TgWfweAq6mWHfIM09TujhZ+M0rFe8LFFLVAIKgFCDEWlItGq2PmdydY8seFB3pfSZLILjVNXapG8b9CjAUCge0jxFhQLofi0vjuyA2W/HGRjLy7ZyaRpKqt5RqmpA3kFmrR6vRtuVkcGRffRyH+hAUCge0jvskE5WIcG/rE9Tvl1l29J442C2I4nZBx13ZLi/aBK2nEpeSw8VA8Wp0kT1GrlAqcjLY1GbWg/0dMUwsEglqAEGNBuSSk58nv/zx7q8x6yZn5LPhfLBl5GhZtOXfXdv84m2xynJpTwPSNx3lj0ymW/nnRxHnLYhYvSUxTCwSC2oPwphaUS2pOgfz+y33XmP9IixJxzL4NGfpwpbuOJNBKcRWApto0SCj/T+vgnrO0UqTJx6pELbobV2mlgBunb1HgUZ9WissEOjhCwhHzBrKLxVyMjAUCQS1AiLGgXDJLrROnZBdSx02tF+J/tQCtXqxHAiMNGSZvAl+U3+4cAOOMlEeNrs8AouEXNfpUxuW1JdaMBQJBLUCIsaBcMovXbg1cTM7Si/Gda3ohVqjAPZgbd0q2JznZq/BxMc/9bExydj6FRTrslEqKdDpcHOzIKSwyq3fXtiKGV84ggUAgqIEIMRaUS+mRcXxqLh3r69h+OoF+AF6hJI3dR7eFcj5Detevw5rxHcttd8ySXZxLyqJ7Y192X0zh0WZB/HT8plm9joHefDupS3WYIhAIBDUWMccnMOF2VgGn0xSyt3NmsSNVoIcjAGm5hfxwNIH/7LwIgEZSsvdyikkbOQXau97H4C1d18sZ0E9/W2L2oPAqWCEQCAS2hRgZ/424nVXAz8cTOBqfTpsQT/6vR0OzOo8s30dKtorIc7d5uFUwmXl60Qz1cSYxI5+07EL+PJuMo0JfnlukZMa3J0zayCksIjkrn/QcDU0D3Cz2xSDydb2cALh5J8+szuCWgbSt51V1gwUCgcBGECPjvxHzN5/hvV/PEn0qiQ+iz3ErM9/k/OXb2fIIde8VvaezQTTDfF0B/cg4zNcFO/ThKJNzStZ5DVmV4lNz6fHRdh5euovraSVryRdvZfHWT6e5lZlPTnF0LX93/Yg7qVRfABr5ud670QKBQGADCDH+m3Ant5BfTyaalMWnmcaE/t+JkvM5hUXkFWrJLdRPOYf56qeT03IKycjToEJfbqTFTOvXBNBnW8rX6JAk073J49YcYt3+a7z89VGKg2vh7653zjLcp45bibOW2l78eQoEgr8H4tuulhKfmktusXdySnYBXRZuM6uTmGE6Gk3KLJkq3nT0Jh9En5WP63m7AJCeU0hGrgb7YjEuKl7pqOOmpkdjX7N7zN8cC4BWJ5FQPBV96Go6oN8ibCy+oB9dd6zvjVIBfcL9KmGxQCAQ2C5izbgWcj4piwFLdgHwx4wenE7IJE9j7lSVWGqd9nqa6fG6/dfk9wbRTM0pxNlBhW+xGGuLf8/lFhTh7GD5z0mSJL45GG9W7upgh6va9Bq1vYq1z3XgVmYBYb4u5dopEAgEtQUxMq6FRJ8qmW7uu3hXmUGqjEfGl5Kz2XPJ1Cvay1mfLWl422B5PTi9eJrarliMNZI+brS3q4OZsBrIyNPw+a7LZuUuanMxliQJZwc7IcQCgeBvhRDjWkjpLEdZpQJ3jO0SCugdtgystCCW6bl6562RkXXxctaLcU6hlttZBbIYa9GL8dIn2+LkYCmhA9y8k0+rup5m5Rl5GlxKifHbQ5uXaZdAIBDUVoQY/w0oLcYdw3yAku1EhUU6vj18Qz4f4GSaUcnT2R4XdYnQ6iRQKfTe1PX93Dk+rx/tircg+bqaR8s6Gp9Odr55dK08jRZ7VcmfoEIBkaHelbJNIBAIagNizdhWuRMPMfMgP9PsVN/UHBrbl3hK1z3qRHP7kvXg9oe8+NI+HbssBazzJSk9ly/tcwBoUMeFpPRscu1L5rYb/u6DnZ2SL+1vy2WBilQAQut4QvGoGWB6v8bM+fG0SX/m/mR6XBbOFlMlCgQCQe1HiLGtcuo7OPOjxVP1gfrGupYJDY2Pr0NPw/FlqAfUMxynQQiAcf1iP66elrTSLcD03j4la731vJ3Ntk8NbhnI1tgkVEoFC0e0NDnnXMaas0AgENR2xLefrVJUHD4yrAe0fhqAY9fTWbfvWpmXDG0dRO+mfkhIvFocNatfc39iYvV7gbs3rsPQVgF8HnOSy5n6kbGTg4r3H9WL5oxvj8tt/fPx1ijt1dC4v8k96hs5Xn0+OpKBS3ebnJ8zuBmfPtPOYv8cxb5igUDwN0WIsa0i6ddsqRMObZ4CYPiGX4HQMi/pENYS2tRDAfywwQOAH04DNAVg8fjBaDQacs6488Md/TC4rpMTtOmjr7vBHQAHlZLFbQdavEeQhyNPdawHSIQHuFHfx5mrqSWj44DiiFvGNPF35cKtbPo286+g8QKBQFC7sPpQZPny5YSFheHo6EhkZCS7d+8us+64ceNQKBRmrxYtWsh1NBoNCxYsoGHDhjg6OtK6dWu2bNli1lZCQgLPPvssPj4+ODs706ZNG44csZDEvqYiFe8bLs7nq9NJ5VTW4+FkL78f06Vs0W7sXtKW0sK+qNLe2sYoFPrp54UjWqFQKEwEdvfrvVEqzdv7z5gOzBvSnDcHNburDQKBQFAbsaoYb9y4kWnTpjFnzhyOHTtG9+7dGThwIPHx5gEiAJYuXUpiYqL8un79Ot7e3jz++ONynblz5/L555/zySefEBsby6RJkxg+fDjHjh2T66Snp9O1a1fs7e357bffiI2N5eOPP8bT0/N+m1x9GEbGxWKcmmOe9ahvM9MIVsaezr2a1jE599HIVvJ7Y7001uIpfRoBmK31lse0fk0I83WhfaiXnBSiNPV8nHmuW5iJZ7VAIBD8nbDqNPXixYuZMGECzz//PABLlizh999/Z8WKFSxcuNCsvoeHBx4eHvLxTz/9RHp6OuPHj5fL1q1bx5w5cxg0aBAAL774Ir///jsff/wx//3vfwFYtGgRISEhrFmzRr6ufv3698PE+0cpMb6ammNWJSLYgz/OJsvHPq4lXs/+RtPFLYLceaJ9iMXbGI+Mp/dtwtOd6hHoYVlULeGqtuPPGT1RKPSjZoFAIBCYYzUxLiws5MiRI8yaNcukvH///uzdu7dCbaxatYq+ffsSGloy5VpQUICjo+m6pJOTE3v27JGPf/nlFwYMGMDjjz/Ozp07CQ4OZvLkyUycOLHMexUUFFBQUCAfZ2bqtxRpNBo0Gk2F+ludKLVFqACtBDqNhu8Pm88mhHqVfA7ODiqC3Ozlvvo4lzz6paNayeWlbWnq72pS5utsZxV775Wy7LN1aqNdtdEmEHbZGpWxqzpst5oYp6SkoNVq8fc3ddrx9/cnKSnprtcnJiby22+/8fXXX5uUDxgwgMWLF9OjRw8aNmzIn3/+yc8//4xWWxKb+cqVK6xYsYIZM2bw5ptvcvDgQaZOnYparWbMmDEW77dw4ULeeecds/Lt27fj7OxcEZOrlRY3LtEIuHIljtiCaLafVgElI88BdXXExR7D8Ijntylg6+8la+eSBE08lGh0Ck7t28GZUoPWieFa9iQpiHJMIDo64f4b9ICIiYmxdhfuC7XRrtpoEwi7bI2K2JWbm3vXOnfD6t7UpacuJUmq0HTm2rVr8fT05NFHHzUpX7p0KRMnTiQ8PByFQkHDhg0ZP368yZS0Tqejffv2fPDBBwC0bduWM2fOsGLFijLFePbs2cyYMUM+zszMJCQkhN69e+Pj41NRc6sN5da/4DY0aNSYej0Hsvj8X5Cfy7rx7fFxcaCRnwtancTpohOEeDsxfFC4WRuDBukdtYw/b41GQ0xMDNOf6Mvr9vZm19gqBrv69euHvbCrRlMbbQJhl61RGbsMM6X3gtXE2NfXF5VKZTYKTk5ONhstl0aSJFavXs3o0aNxcHAwOVenTh1++ukn8vPzSU1NJSgoiFmzZhEWFibXCQwMpHlz0xjIzZo1Y9OmTWXeU61Wo1abh3q0t7e3zh9gsX5eTs1jwPwYpGIH6Lo+riZJFlaP71il5q1m131G2GU71EabQNhla1TEruqw22ruqw4ODkRGRppNAcTExBAVFVXutTt37uTSpUtMmDChzDqOjo4EBwdTVFTEpk2bGDZsmHyua9eunD9/3qT+hQsXTNaeazzFDly/nbktCzGAr6tDGRcIBAKBoKZi1WnqGTNmMHr0aNq3b0+XLl1YuXIl8fHxTJo0CdBPDSckJPDVV1+ZXLdq1So6depERESEWZsHDhwgISGBNm3akJCQwPz589HpdLz++utynenTpxMVFcUHH3zAE088wcGDB1m5ciUrV66stA0V2d9riW3nbuHv7kiLII+7V7ZEsRjrpJIpZkd7ZZlpDAUCgUBQc7HqN/eoUaNITU1lwYIFJCYmEhERQXR0tDxCTUxMNNtznJGRwaZNm1i6dKnFNvPz85k7dy5XrlzB1dWVQYMGsW7dOpM9xB06dODHH39k9uzZLFiwgLCwMJYsWcIzzzxTaRu+O3qDyQN8K3XNwbg0nlt7GICrHw6u9D0BOeiHzshpy9dVLbYPCQQCgQ1i9WHU5MmTmTx5ssVza9euNSvz8PAo13OtZ8+exMbG3vW+Q4YMYciQIRXuZ1l8tjOOyQPaVOqai8lZ8vvCIh0OdlVYLTCMjI1WGhr5uVa+HYFAIBBYHRHy6B65k2eep/du2BmFuErOyq/ajS2I8agyAncIBAKBoGZj9ZHx35Gs/BIBv5VZQF2vKuxTLvba0qHgyQ4hdGnow8CWgdXVRYFAIBA8QIQYW4HMvJJoLRl55jGlK4SuZM346U71aFXXsxp6JhAIBAJrIKapq4GcgspNVWcajYwzjaa5s/I1ZOSZhlW7mpLDx1vPk55TyJbTiWw5XbwvW56mVuDlLLYzCQQCgS0jRsbVQFJmPg3rVNx5ynhknJmvf6/TSQz6925Ssgo58lZfnB30j+a5tYe4kpLDJ9suydfseaM3gTotKkBCiYdz7dtoLxAIBH8nxMi4GkjKqJwTlvHIOCNXL8YZeRqup+WRp9Fy8kaGfP5Kink2priUHAo1+jYkhRI3sbdYIBAIbBohxtVAYrEYp2QXcDoh4y61S0bDxu9TsksyQl28lWV2jTFL/7hIYZFejB0d7MTeYoFAILBxxJCqGkjKyEOj1fHwkl2kZBeybkJHujeuww9Hb2CnUvJI6yCT+sbT1F/sjmP1X1fRGkXyOnOzJOi4vUqBRmsa5evwtXSu+2fjATg6iClqgUAgsHXEyLgaSMzI53ZWASnZes/oPRdTSM8pZMa3J5j6zTGup5kGKTHe2gSYCDHApeRs+b3BOeuxdnX5+PHWcvmdbP1o3LEWBmYXCASCvxtCjKuB9Qfi2Xnhtnx8KiGDc0klU83GI12tTiLhTl657SUarUEbvKun9W3MY5F15YxMecVrxo5qIcYCgUBg64hp6nvkB4d5uDqo4H+wxbDD6Dp4f+/AFgf9SNl/ixp2OaCTJPIKtWxx0ItxkKcjN++YO38p8kBa7oYkSfyszAYHCPraFZQKfqSQJIcC6ir04i+mqQUCgcD2EWJ8jzRRJuCutOBAlQd+hnmHbP1LCbgC4YbyTHAva24iWZ+yWK6bov/HE/A0ukbrXu9eui8QCASCGoAQ43vkPyEfcOhG2YkrALo19GFYm2Bmbjopl41oG8Rj7ery7v/OopV0vD2kBSqlgrFrDlKolVg6qg2Hrqbx3wPxtAp2Z/bAZgBcSM7m7V/OAJAiedDHI/L+GScQCASCB4IQ43tEE9SFffFJ8vHsgeEs/O2cSZ19FyE8qi37ikNYAjzZuA3KhsG8/Uovk7oXnYu4mZFPnFske3U32adzo33jRtCgKQBOnrns05XUX9a2bvUbJRAIBIIHinDgukeCvRzl9x891oqJ3RvgaG/+sf7rjwsmx33C/Sy2d7PYeWvUyv1sO5cMQJCnk3ze11Utv28R5E7TALeqd14gEAgENQIhxvdIwzou8vsuDX1QKhX4uZUIdESwOwCnE0o8qn1cHHBztOx4pTbKbWzwqjYWYycHlfxexPoQCASC2oEQ43ukib8bHz/emhXPtCPEW58KsWWwh3w+IsjD7JrPRpe9zrtuQiezMmPBFwgEAkHtQ6wZVwOPRZqu277cpxHX0nJ4uXcjzieVBPBwsFNy/t2Hyw1f2aG+l8lxsKeTWb5jJ3sVeRotD4X7V0PvBQKBQGBthBjfB5oFuvO/Kd0ByMy/LpfXcVXfNY506fNjuoSa1fnf1G78dCyBST0bVkNvBQKBQGBtxDT1fSbY2PnKTV1OzRKiGvrI7/3cza9pWMeVV/s3NVk/FggEAoHtIsT4PtPEv8Tb2aWC4rl2fEf5vUopHpFAIBDUdsQ3/X2mjtFoOE+jLadmCQ52Soa1CSLA3ZGeTercr64JBAKBoIYg1owfAE91rMc3B+N5rX/TCl+z9Mm2aHUSKkuhNgUCgUBQqxBi/ACY/0hzJvVsQKhP5bYoCSEWCASCvwdimvoBoLZTVVqIBQKBQPD3QYixQCAQCARWRoixQCAQCARWxupivHz5csLCwnB0dCQyMpLdu3eXWXfcuHEoFAqzV4sWLeQ6Go2GBQsW0LBhQxwdHWndujVbtmwps82FCxeiUCiYNm1adZolEAgEAkGFsaoYb9y4kWnTpjFnzhyOHTtG9+7dGThwIPHx8RbrL126lMTERPl1/fp1vL29efzxx+U6c+fO5fPPP+eTTz4hNjaWSZMmMXz4cI4dO2bW3qFDh1i5ciWtWrW6bzYKBAKBQHA3rCrGixcvZsKECTz//PM0a9aMJUuWEBISwooVKyzW9/DwICAgQH4dPnyY9PR0xo8fL9dZt24db775JoMGDaJBgwa8+OKLDBgwgI8//tikrezsbJ555hm++OILvLy8St9KIBAIBIIHhtW2NhUWFnLkyBFmzZplUt6/f3/27t1boTZWrVpF3759CQ0tid9cUFCAo6OjST0nJyf27NljUvbSSy8xePBg+vbty3vvvXfXexUUFFBQUCAfZ2bqUyJqNBo0Gk2F+msLGGypTTaBsMuWqI02gbDL1qiMXdVhu9XEOCUlBa1Wi7+/aeYhf39/kpKS7np9YmIiv/32G19//bVJ+YABA1i8eDE9evSgYcOG/Pnnn/z8889otSXRrzZs2MDRo0c5dOhQhfu7cOFC3nnnHbPy7du34+zsbOEK2yYmJsbaXbgvCLtsh9poEwi7bI2K2JWbm3vP97F60I/SWYokSbprZiOAtWvX4unpyaOPPmpSvnTpUiZOnEh4eDgKhYKGDRsyfvx41qxZA8D169d55ZVX2Lp1q9kIujxmz57NjBkz5OOMjAzq1atH+/bt8fb2rnA7NR2NRsP27dvp3bs39vb21u5OtSHssh1qo00g7LI1KmNXVlYWoNevqmI1Mfb19UWlUpmNgpOTk81Gy6WRJInVq1czevRoHBwcTM7VqVOHn376ifz8fFJTUwkKCmLWrFmEhYUBcOTIEZKTk4mMjJSv0Wq17Nq1i2XLllFQUIBKZZ7QQa1Wo1aXxJlOSUkBoEmTJpUzXCAQCAS1kqysLDw8PKp0rdXE2MHBgcjISGJiYhg+fLhcHhMTw7Bhw8q9dufOnVy6dIkJEyaUWcfR0ZHg4GA0Gg2bNm3iiSeeAOChhx7i1KlTJnXHjx9PeHg4b7zxhkUhtoRhNBwfH1/lD78mkpmZSUhICNevX8fd3d3a3ak2hF22Q220CYRdtkZl7JIkiaysLIKCgqp8P6tOU8+YMYPRo0fTvn17unTpwsqVK4mPj2fSpEmAfmo4ISGBr776yuS6VatW0alTJyIiIszaPHDgAAkJCbRp04aEhATmz5+PTqfj9ddfB8DNzc3sOhcXF3x8fCy2VxbK4tSGHh4eteoP0IC7u7uwy4aojXbVRptA2GVrVNSuex2UWVWMR40aRWpqKgsWLCAxMZGIiAiio6Nl7+jExESzPccZGRls2rSJpUuXWmwzPz+fuXPncuXKFVxdXRk0aBDr1q3D09PzfpsjEAgEAkGVUEj3suL8NyYzMxMPDw8yMjJq1a9BYZdtURvtqo02gbDL1njQdlk9HKatolarefvtt02cumoDwi7bojbaVRttAmGXrfGg7RIjY4FAIBAIrIwYGQsEAoFAYGWEGAsEAoFAYGWEGAsEAoFAYGWEGAsEAoFAYGWEGFeR5cuXExYWhqOjI5GRkezevdvaXSqT+fPno1AoTF4BAQHyeUmSmD9/PkFBQTg5OdGrVy/OnDlj0kZBQQFTpkzB19cXFxcXHnnkEW7cuPFA7di1axdDhw4lKCgIhULBTz/9ZHK+uuxIT09n9OjReHh44OHhwejRo7lz545VbBo3bpzZs+vcuXONtgn0iVU6dOiAm5sbfn5+PProo5w/f96kji0+r4rYZWvPbMWKFbRq1UoObtGlSxd+++03+bwtPqeK2FXjnpMkqDQbNmyQ7O3tpS+++EKKjY2VXnnlFcnFxUW6du2atbtmkbfffltq0aKFlJiYKL+Sk5Pl8x9++KHk5uYmbdq0STp16pQ0atQoKTAwUMrMzJTrTJo0SQoODpZiYmKko0ePSr1795Zat24tFRUVPTA7oqOjpTlz5kibNm2SAOnHH380OV9ddjz88MNSRESEtHfvXmnv3r1SRESENGTIEKvYNHbsWOnhhx82eXapqakmdWqaTZIkSQMGDJDWrFkjnT59Wjp+/Lg0ePBgqV69elJ2drZcxxafV0XssrVn9ssvv0i//vqrdP78een8+fPSm2++Kdnb20unT5+WJMk2n1NF7Kppz0mIcRXo2LGjNGnSJJOy8PBwadasWVbqUfm8/fbbUuvWrS2e0+l0UkBAgPThhx/KZfn5+ZKHh4f02WefSZIkSXfu3JHs7e2lDRs2yHUSEhIkpVIpbdmy5b72vSxKC1d12REbGysB0v79++U6+/btkwDp3LlzD9QmSdJ/YQwbNqzMa2q6TQaSk5MlQNq5c6ckSbXjeVmyS5JqxzPz8vKS/vOf/9Sa52TAYJck1bznJKapK0lhYSFHjhyhf//+JuX9+/dn7969VurV3bl48SJBQUGEhYXx5JNPcuXKFQDi4uJISkoysUetVtOzZ0/ZniNHjqDRaEzqBAUFERERUWNsri479u3bh4eHB506dZLrdO7cGQ8PD6vZumPHDvz8/GjSpAkTJ04kOTlZPmcrNmVkZAAlCVZqy/MqbZcBW31mWq2WDRs2kJOTQ5cuXWrNcyptl4Ga9Jysns/Y1khJSUGr1ZqlefT39zdLB1lT6NSpE1999RVNmjTh1q1bvPfee0RFRXHmzBm5z5bsuXbtGgBJSUk4ODjg5eVlVqem2FxddiQlJeHn52fWvp+fn1VsHThwII8//jihoaHExcXx1ltv0adPH44cOYJarbYJmyRJYsaMGXTr1k1OxlIbnpclu8A2n9mpU6fo0qUL+fn5uLq68uOPP9K8eXNZUGz1OZVlF9S85yTEuIooFAqTY0mSzMpqCgMHDpTft2zZki5dutCwYUO+/PJL2WGhKvbURJurww5L9a1l66hRo+T3ERERtG/fntDQUH799VdGjBhR5nU1yaaXX36ZkydPsmfPHrNztvy8yrLLFp9Z06ZNOX78OHfu3GHTpk2MHTuWnTt3ltkXW3lOZdnVvHnzGvecxDR1JfH19UWlUpn96klOTjb79VhTcXFxoWXLlly8eFH2qi7PnoCAAAoLC0lPTy+zjrWpLjsCAgK4deuWWfu3b9+uEbYGBgYSGhrKxYsXgZpv05QpU/jll1/Yvn07devWlctt/XmVZZclbOGZOTg40KhRI9q3b8/ChQtp3bo1S5cutfnnVJZdlrD2cxJiXEkcHByIjIwkJibGpDwmJoaoqCgr9apyFBQUcPbsWQIDAwkLCyMgIMDEnsLCQnbu3CnbExkZib29vUmdxMRETp8+XWNsri47unTpQkZGBgcPHpTrHDhwgIyMjBpha2pqKtevXycwMBCouTZJksTLL7/MDz/8wLZt2wgLCzM5b6vP6252WcJWnpkxkiRRUFBgs8+pLAx2WcLqz6lS7l4CSZJKtjatWrVKio2NlaZNmya5uLhIV69etXbXLPLqq69KO3bskK5cuSLt379fGjJkiOTm5ib398MPP5Q8PDykH374QTp16pT01FNPWdy6ULduXemPP/6Qjh49KvXp0+eBb23KysqSjh07Jh07dkwCpMWLF0vHjh2Tt5RVlx0PP/yw1KpVK2nfvn3Svn37pJYtW963LRjl2ZSVlSW9+uqr0t69e6W4uDhp+/btUpcuXaTg4OAabZMkSdKLL74oeXh4SDt27DDZOpKbmyvXscXndTe7bPGZzZ49W9q1a5cUFxcnnTx5UnrzzTclpVIpbd26VZIk23xOd7OrJj4nIcZV5NNPP5VCQ0MlBwcHqV27diZbG2oahn2B9vb2UlBQkDRixAjpzJkz8nmdTie9/fbbUkBAgKRWq6UePXpIp06dMmkjLy9PevnllyVvb2/JyclJGjJkiBQfH/9A7di+fbsEmL3Gjh1brXakpqZKzzzzjOTm5ia5ublJzzzzjJSenv7AbcrNzZX69+8v1alTR7K3t5fq1asnjR071qy/Nc0mSZIs2gRIa9askevY4vO6m122+Myee+45+busTp060kMPPSQLsSTZ5nO6m1018TmJFIoCgUAgEFgZsWYsEAgEAoGVEWIsEAgEAoGVEWIsEAgEAoGVEWIsEAgEAoGVEWIsEAgEAoGVEWIsEAgEAoGVEWIsEAgEAoGVEWIsEAgEAoGVEWIsEPwNmT9/Pm3atHng992xYwcKhQKFQsGjjz5aoWvmz58vX7NkyZL72j+BwFoIMRYIahkG4SrrNW7cOF577TX+/PNPq/Xx/PnzrF27tkJ1X3vtNRITE++aHUkgsGVEPmOBoJaRmJgov9+4cSPz5s3j/PnzcpmTkxOurq64urpao3uAPvm6p6dnheoa+qpSqe5vpwQCKyJGxgJBLSMgIEB+eXh4oFAozMpKT1OPGzeORx99lA8++AB/f388PT155513KCoqYubMmXh7e1O3bl1Wr15tcq+EhARGjRqFl5cXPj4+DBs2jKtXr1a6z99//z0tW7bEyckJHx8f+vbtS05Ozj1+EgKB7SDEWCAQALBt2zZu3rzJrl27WLx4MfPnz2fIkCF4eXlx4MABJk2axKRJk7h+/ToAubm59O7dG1dXV3bt2sWePXtwdXXl4YcfprCwsML3TUxM5KmnnuK5557j7Nmz7NixgxEjRiBy2Aj+TggxFggEAHh7e/Pvf/+bpk2b8txzz9G0aVNyc3N58803ady4MbNnz8bBwYG//voLgA0bNqBUKvnPf/5Dy5YtadasGWvWrCE+Pp4dO3ZU+L6JiYkUFRUxYsQI6tevT8uWLZk8ebJVp9EFggeNWDMWCAQAtGjRAqWy5Pe5v78/ERER8rFKpcLHx4fk5GQAjhw5wqVLl3BzczNpJz8/n8uXL1f4vq1bt+ahhx6iZcuWDBgwgP79+zNy5Ei8vLzu0SKBwHYQYiwQCACwt7c3OVYoFBbLdDodADqdjsjISNavX2/WVp06dSp8X5VKRUxMDHv37mXr1q188sknzJkzhwMHDhAWFlYFSwQC20NMUwsEgirRrl07Ll68iJ+fH40aNTJ5eXh4VKothUJB165deeeddzh27BgODg78+OOP96nnAkHNQ4ixQCCoEs888wy+vr4MGzaM3bt3ExcXx86dO3nllVe4ceNGhds5cOAAH3zwAYcPHyY+Pp4ffviB27dv06xZs/vYe4GgZiGmqQUCQZVwdnZm165dvPHGG4wYMYKsrCyCg4N56KGHcHd3r3A77u7u7Nq1iyVLlpCZmUloaCgff/wxAwcOvI+9FwhqFgpJ7B8QCAQPiB07dtC7d2/S09MrHPTDQP369Zk2bRrTpk27L30TCKyJmKYWCAQPnLp16/LUU09VqO4HH3yAq6sr8fHx97lXAoH1ECNjgUDwwMjLyyMhIQHQh7kMCAi46zVpaWmkpaUBei/tyjqHCQS2gBBjgUAgEAisjJimFggEAoHAyggxFggEAoHAyggxFggEAoHAyggxFggEAoHAyggxFggEAoHAyggxFggEAoHAyggxFggEAoHAyggxFggEAoHAyvw/0lX14utiiR8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "load_isone = np.array([\n",
    "    11920.071, 11980.979, 12000.579, 12145.243, 12211.862, 12220.703,\n",
    "    12191.051, 12241.546, 12285.719, 12312.626, 12364.102, 12336.354\n",
    "])\n",
    "\n",
    "# Normalize the load\n",
    "load_min = load_isone.min()\n",
    "load_max = load_isone.max()\n",
    "load_mid = (load_max + load_min) / 2  # Midpoint of the range\n",
    "# Set to desired range\n",
    "load_range = 0.8 + 0.01 * ((load_isone - load_mid) / (load_max - load_min))\n",
    "\n",
    "load_scale_base = np.repeat(load_range, 300)\n",
    "# smooth load_scale_base\n",
    "load_scale_smooth = scipy.signal.savgol_filter(load_scale_base, 600, 4)\n",
    "\n",
    "np.random.seed(2024)  # Set random seed for reproducibility\n",
    "random_bias = np.random.normal(loc=0, scale=0.0015,\n",
    "                               size=len(load_scale_smooth))\n",
    "random_smooth = scipy.signal.savgol_filter(random_bias, 20, 1)\n",
    "\n",
    "# Add noise to the load as random ACE\n",
    "load_coeff_base = load_scale_smooth + random_smooth\n",
    "# smooth\n",
    "load_coeff = scipy.signal.savgol_filter(load_coeff_base, 10, 2)\n",
    "\n",
    "# NOTE: force the first 2 points to be the same as first interval average\n",
    "load_coeff[0:2] = load_coeff[0:300].mean()\n",
    "\n",
    "# average load every N points, for RTED dispatch\n",
    "load_coeff_avg = load_coeff.reshape(-1, 300).mean(axis=1)\n",
    "load_coeff_avg = np.repeat(load_coeff_avg, 300)\n",
    "\n",
    "fig_load, ax_load = plt.subplots(figsize=(5, 3), dpi=100)\n",
    "\n",
    "ax_load.plot(range(len(load_coeff)), load_coeff,\n",
    "             label='Load Coefficient')\n",
    "ax_load.plot(range(len(load_coeff_avg)), load_coeff_avg,\n",
    "             label='Load Average')\n",
    "\n",
    "ax_load.set_xlim([0, 3600])\n",
    "ax_load.set_xlabel('Time [s]')\n",
    "ax_load.set_ylabel('Ratio')\n",
    "ax_load.set_title('Load Curve')\n",
    "ax_load.grid(True)\n",
    "ax_load.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Co-simulation\n",
    "\n",
    "### Define constants\n",
    "\n",
    "Here we assume the AGC interval is 4 seconds, and RTED interval is 300 seconds.\n",
    "\n",
    "Between the interoperation of AMS and ANDES, there is a AC conversion step to convert the DC-based dispatch resutls to AC-based power flow results.\n",
    "For more details, check the reference paper and [AMS source code - dc2ac](https://github.com/CURENT/ams/blob/master/ams/routines/rted.py#L184).\n",
    "\n",
    "### AGC controller\n",
    "\n",
    "Since there is not an built-in AGC controller in ANDES, we can define a PI controller to calculate the control signal for the AGC:\n",
    "\n",
    "AGC_raw = kp * ACE + ki * integral(ACE)\n",
    "\n",
    "Note that, in the AGC interval, there is a cap operation to limit the AGC signal within procured reserve limits.\n",
    "\n",
    "### ANDES settings\n",
    "\n",
    "ANDES load needs to be set to constant load for effective load change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/__/n5kx_m_s0tbg6n5qd7rh51700000gn/T/ipykernel_94953/3863718312.py:70: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  maptab = sp.dyn.link.copy().fillna(False)\n"
     ]
    }
   ],
   "source": [
    "# --- time constants ---\n",
    "total_time = 610\n",
    "\n",
    "RTED_interval = 300\n",
    "AGC_interval = 4\n",
    "\n",
    "id_rted = -1  # RTED interval counter\n",
    "id_agc = -1  # AGC interval counter\n",
    "\n",
    "# --- AGC controller ---\n",
    "kp = 0.1\n",
    "ki = 0.05\n",
    "\n",
    "ACE_integral = 0\n",
    "ACE_raw = 0\n",
    "\n",
    "# --- initialize output ---\n",
    "# pd_andes: total load in ANDES\n",
    "# pd_ams: total load in AMS; pg_ams: total generation in AMS\n",
    "# pru_ams: total RegUp in AMS; prd_ams: total RegDn in AMS\n",
    "out_cols = ['time', 'freq', 'ACE', 'AGC',\n",
    "            'pd_andes', 'pd_ams', 'pg_ams',\n",
    "            'pru_ams', 'prd_ams']\n",
    "outdf = pd.DataFrame(data=np.zeros((total_time, len(out_cols))),\n",
    "                     columns=out_cols)\n",
    "\n",
    "# --- AMS settings ---\n",
    "sp.SFR.set(src='du', idx=sp.SFR.idx.v, attr='v', value=0.0015*np.ones(sp.SFR.n))\n",
    "sp.SFR.set(src='dd', idx=sp.SFR.idx.v, attr='v', value=0.0015*np.ones(sp.SFR.n))\n",
    "\n",
    "# --- ANDES settings ---\n",
    "sa.TDS.config.no_tqdm = True  # turn off ANDES progress bar\n",
    "sa.TDS.config.criteria = 0  # turn off ANDES criteria check\n",
    "\n",
    "# adjsut ANDES TDS settings to save memory\n",
    "sa.TDS.config.save_every = 0\n",
    "\n",
    "# adjust dynamic parameters\n",
    "# NOTE: might run into error if there exists a TurbineGov model that does not have \"VMAX\"\n",
    "tbgov_src = [mdl.idx.v for mdl in sa.TurbineGov.models.values()]\n",
    "tbgov_idx = list(chain.from_iterable(tbgov_src))\n",
    "sa.TurbineGov.set(src='VMAX', idx=tbgov_idx, attr='v',\n",
    "                  value=9999 * np.ones(sa.TurbineGov.n),)\n",
    "sa.TurbineGov.set(src='VMIN', idx=tbgov_idx, attr='v',\n",
    "                  value=np.zeros(sa.TurbineGov.n),)\n",
    "syg_src = [mdl.idx.v for mdl in sa.SynGen.models.values()]\n",
    "syg_idx = list(chain.from_iterable(syg_src))\n",
    "sa.SynGen.set(src='ra', idx=syg_idx, attr='v',\n",
    "              value=np.zeros(sa.SynGen.n),)\n",
    "\n",
    "# use constant power model for PQ\n",
    "sa.PQ.config.p2p = 1\n",
    "sa.PQ.config.q2q = 1\n",
    "sa.PQ.config.p2z = 0\n",
    "sa.PQ.config.q2z = 0\n",
    "sa.PQ.pq2z = 0\n",
    "\n",
    "# save the initial load values\n",
    "p0_sp = sp.PQ.p0.v.copy()\n",
    "q0_sp = sp.PQ.q0.v.copy()\n",
    "p0_sa = sa.PQ.p0.v.copy()\n",
    "q0_sa = sa.PQ.q0.v.copy()\n",
    "\n",
    "# --- Co-Sim Variables ---\n",
    "# save device index\n",
    "pq_idx = sp.PQ.idx.v  # PQ index\n",
    "\n",
    "# get a copy of link table to calculate AGC power\n",
    "# pd.set_option('future.no_silent_downcasting', True)  # pandas setting\n",
    "maptab = sp.dyn.link.copy().fillna(False)\n",
    "\n",
    "# existence of each type of generator\n",
    "maptab['has_gov'] = maptab['gov_idx'].fillna(0, inplace=False).astype(bool).astype(int)\n",
    "maptab['has_dg'] = maptab['dg_idx'].fillna(0, inplace=False).astype(bool).astype(int)\n",
    "maptab['has_rg'] = maptab['rg_idx'].fillna(0, inplace=False).astype(bool).astype(int)\n",
    "\n",
    "# initialize columns for power output\n",
    "# pg: StaticGen power reference; pru: RegUp power; prd: RegDn power\n",
    "# pgov: TurbineGov power; prg: RenGen power; pdg: DG power\n",
    "# bu: RegUp participation factor; bd: RegDown participation factor\n",
    "# agov: TurbineGov AGC power; adg: DG AGC power; arg: RenGen AGC power\n",
    "\n",
    "add_cols = ['pg', 'pru', 'prd', 'pgov',\n",
    "            'prg', 'pdg', 'bu', 'bd',\n",
    "            'agov', 'adg', 'arg']\n",
    "maptab[add_cols] = 0\n",
    "\n",
    "# output data of each unit's AGC power\n",
    "# aout: delivered AGC power; aref: AGC reference\n",
    "agc_cols = list(maptab['stg_idx'])\n",
    "agc_ref = pd.DataFrame(data=np.zeros((total_time, len(list(['time']) + agc_cols))),\n",
    "                       columns=list(['time']) + agc_cols)\n",
    "agc_out = pd.DataFrame(data=np.zeros((total_time, len(list(['time']) + agc_cols))),\n",
    "                       columns=list(['time']) + agc_cols)\n",
    "\n",
    "# output data of each dispatch interval's results\n",
    "gen_cols = list(maptab['stg_idx'])\n",
    "n_dispatch = np.ceil(total_time / RTED_interval).astype(int)\n",
    "pg_ref = pd.DataFrame(data=np.zeros((n_dispatch, len(list(['n_rted']) + gen_cols))),\n",
    "                      columns=list(['n_rted']) + gen_cols)\n",
    "pru_ref = pd.DataFrame(data=np.zeros((n_dispatch, len(list(['n_rted']) + gen_cols))),\n",
    "                       columns=list(['n_rted']) + gen_cols)\n",
    "prd_ref = pd.DataFrame(data=np.zeros((n_dispatch, len(list(['n_rted']) + gen_cols))),\n",
    "                       columns=list(['n_rted']) + gen_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building system matrices\n",
      "<RTED> reinit OModel due to non-parametric change.\n",
      "Parsing OModel for <RTED>\n",
      "Evaluating OModel for <RTED>\n",
      "Finalizing OModel for <RTED>\n",
      "<RTED> solved as optimal in 0.0121 seconds, converged in 10 iterations with CLARABEL.\n",
      "Parsing OModel for <ACOPF>\n",
      "Evaluating OModel for <ACOPF>\n",
      "Finalizing OModel for <ACOPF>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====== RTED Interval <0> ======\n",
      "--AMS: update disaptch load with factor 0.795040.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsing OModel for <RTED>\n",
      "<RTED> converted to AC.\n",
      "GENROU (xl <= xd2) out of typical upper limit.\n",
      "\n",
      "   idx     | values | limit\n",
      "-----------+--------+------\n",
      " GENROU_1  | 0.012  | 0.001\n",
      " GENROU_2  | 0.042  | 0.036\n",
      " GENROU_3  | 0.036  | 0.003\n",
      " GENROU_4  | 0.025  | 0.001\n",
      " GENROU_5  | 0.050  | 0.001\n",
      " GENROU_7  | 0.031  | 0.002\n",
      " GENROU_8  | 0.029  | 0.006\n",
      " GENROU_9  | 0.018  | 0.001\n",
      " GENROU_10 | 0.003  | 0.000\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--AMS: AC conversion successful.\n",
      "--AMS: RTED optimized.\n",
      "--ANDES: update TurbineGov reference.\n",
      "--ANDES: update DG reference.\n",
      "--ANDES: update RenGen reference.\n",
      "--ANDES: TDS initialized.\n",
      "--Watchdog: t=200 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building system matrices\n",
      "<RTED> reinit OModel due to non-parametric change.\n",
      "Evaluating OModel for <RTED>\n",
      "Finalizing OModel for <RTED>\n",
      "<RTED> solved as optimal in 0.0125 seconds, converged in 10 iterations with CLARABEL.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====== RTED Interval <1> ======\n",
      "--AMS: update disaptch load with factor 0.796370.\n",
      "--AMS: received data from ANDES.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsing OModel for <RTED>\n",
      "<RTED> converted to AC.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--AMS: AC conversion successful.\n",
      "--AMS: RTED optimized.\n",
      "--ANDES: update TurbineGov reference.\n",
      "--ANDES: update DG reference.\n",
      "--ANDES: update RenGen reference.\n",
      "--Watchdog: t=400 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building system matrices\n",
      "<RTED> reinit OModel due to non-parametric change.\n",
      "Evaluating OModel for <RTED>\n",
      "Finalizing OModel for <RTED>\n",
      "<RTED> solved as optimal in 0.0122 seconds, converged in 10 iterations with CLARABEL.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--Watchdog: t=600 sec.\n",
      "====== RTED Interval <2> ======\n",
      "--AMS: update disaptch load with factor 0.797038.\n",
      "--AMS: received data from ANDES.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsing OModel for <RTED>\n",
      "<RTED> converted to AC.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--AMS: AC conversion successful.\n",
      "--AMS: RTED optimized.\n",
      "--ANDES: update TurbineGov reference.\n",
      "--ANDES: update DG reference.\n",
      "--ANDES: update RenGen reference.\n"
     ]
    }
   ],
   "source": [
    "for t in range(0, total_time, 1):\n",
    "    # --- Wathdog ---\n",
    "    if (t % 200 == 0) and (t > 0):\n",
    "        print(f\"--Watchdog: t={t} sec.\")\n",
    "\n",
    "    # --- Dispatch interval ---\n",
    "    if t % RTED_interval == 0:\n",
    "        id_rted += 1  # update RTED interval counter\n",
    "        id_agc = -1   # reset AGC interval counter\n",
    "        print(f\"====== RTED Interval <{id_rted}> ======\")\n",
    "        # use 5-min average load in dispatch solution\n",
    "        load_avg = load_coeff[t:t+RTED_interval].mean()\n",
    "        # set load in to AMS\n",
    "        sp.PQ.set(src='p0', idx=pq_idx, attr='v', value=load_avg * p0_sp)\n",
    "        sp.PQ.set(src='q0', idx=pq_idx, attr='v', value=load_avg * q0_sp)\n",
    "        print(f\"--AMS: update disaptch load with factor {load_avg:.6f}.\")\n",
    "\n",
    "        # get dynamic generator output from TDS\n",
    "        if t > 0:\n",
    "            _receive = sp.dyn.receive(adsys=sa, routine='RTED', no_update=True)\n",
    "            if _receive:\n",
    "                print(\"--AMS: received data from ANDES.\")\n",
    "\n",
    "        # update RTED parameters\n",
    "        sp.RTED.update()\n",
    "        # run RTED\n",
    "        sp.RTED.run(solver='CLARABEL')\n",
    "        # convert to AC\n",
    "        flag_2ac = sp.RTED.dc2ac(kloss=1.02 if id_rted == 0 else 1)\n",
    "        if flag_2ac:\n",
    "            print(f\"--AMS: AC conversion successful.\")\n",
    "        else:\n",
    "            print(f\"ERROR! AC conversion failed!\")\n",
    "            break\n",
    "\n",
    "        if sp.RTED.exit_code == 0:\n",
    "            print(f\"--AMS: {sp.recent.class_name} optimized.\")\n",
    "\n",
    "            # update in mapping table\n",
    "            maptab['pg'] = sp.RTED.get(src='pg', attr='v', idx=maptab['stg_idx'])\n",
    "            maptab['pru'] = sp.RTED.get(src='pru', attr='v', idx=maptab['stg_idx'])\n",
    "            maptab['prd'] = sp.RTED.get(src='prd', attr='v', idx=maptab['stg_idx'])\n",
    "            maptab['bu'] = maptab['pru'] / maptab['pru'].sum()\n",
    "            maptab['bd'] = maptab['prd'] / maptab['prd'].sum()\n",
    "\n",
    "            # calculate power reference for dynamic generator\n",
    "            maptab['pgov'] = maptab['pg'] * maptab['has_gov'] * maptab['gammap']\n",
    "            maptab['pdg'] = maptab['pg'] * maptab['has_dg'] * maptab['gammap']\n",
    "            maptab['prg'] = maptab['pg'] * maptab['has_rg'] * maptab['gammap']\n",
    "\n",
    "            # set into governor, Exclude NaN values for governor index\n",
    "            gov_to_set = {gov: pgov for gov, pgov in zip(maptab['gov_idx'], maptab['pgov']) if bool(gov)}\n",
    "            sa.TurbineGov.set(src='pref0', idx=list(gov_to_set.keys()), attr='v', value=list(gov_to_set.values()))\n",
    "            print(f\"--ANDES: update TurbineGov reference.\")\n",
    "\n",
    "            # set into dg, Exclude NaN values for dg index\n",
    "            dg_to_set = {dg: pdg for dg, pdg in zip(maptab['dg_idx'], maptab['pdg']) if bool(dg)}\n",
    "            sa.DG.set(src='pref0', idx=list(dg_to_set.keys()), attr='v', value=list(dg_to_set.values()))\n",
    "            print(f\"--ANDES: update DG reference.\")\n",
    "\n",
    "            # set into rg, Exclude NaN values for rg index\n",
    "            rg_to_set = {rg: prg for rg, prg in zip(maptab['rg_idx'], maptab['prg']) if bool(rg)}\n",
    "            sa.RenGen.set(src='Pref', idx=list(rg_to_set.keys()), attr='v', value=list(rg_to_set.values()))\n",
    "            print(f\"--ANDES: update RenGen reference.\")\n",
    "\n",
    "            # record dispatch data\n",
    "            pg_ref.loc[id_rted, 'n_rted'] = id_rted\n",
    "            pg_ref.loc[id_rted, gen_cols] = maptab['pg'].values\n",
    "            pru_ref.loc[id_rted, 'n_rted'] = id_rted\n",
    "            pru_ref.loc[id_rted, gen_cols] = maptab['pru'].values\n",
    "            prd_ref.loc[id_rted, 'n_rted'] = id_rted\n",
    "            prd_ref.loc[id_rted, gen_cols] = maptab['prd'].values\n",
    "        else:\n",
    "            print(f\"ERROR! {sp.recent.class_name} failed: {sp.RTED.om.prob.status}\")\n",
    "            break\n",
    "\n",
    "    # --- AGC interval ---\n",
    "    if t % AGC_interval == 0:\n",
    "        id_agc += 1  # update AGC interval counter\n",
    "        # cap ACE_raw with procured capacity as AGC response\n",
    "        if ACE_raw >= 0:  # RegUp\n",
    "            ACE_input = min([ACE_raw, maptab['pru'].sum()])\n",
    "            b_factor = maptab['bu']\n",
    "        else:            # RegDn\n",
    "            ACE_input = -min([-ACE_raw, maptab['prd'].sum()])\n",
    "            b_factor = maptab['bd']\n",
    "        outdf.loc[t:t+AGC_interval, 'AGC'] = ACE_input\n",
    "\n",
    "        maptab['agov'] = ACE_input * b_factor * maptab['has_gov'] * maptab['gammap']\n",
    "        maptab['adg'] = ACE_input * b_factor * maptab['has_dg'] * maptab['gammap']\n",
    "        maptab['arg'] = ACE_input * b_factor * maptab['has_rg'] * maptab['gammap']\n",
    "\n",
    "        # set into governor, Exclude NaN values for governor index\n",
    "        agov_to_set = {gov: agov for gov, agov in zip(maptab['gov_idx'], maptab['agov']) if bool(gov)}\n",
    "        sa.TurbineGov.set(src='paux0', idx=list(agov_to_set.keys()), attr='v', value=list(agov_to_set.values()))\n",
    "\n",
    "        # set into dg, Exclude NaN values for dg index\n",
    "        adg_to_set = {dg: adg for dg, adg in zip(maptab['dg_idx'], maptab['adg']) if bool(dg)}\n",
    "        sa.DG.set(src='Pext0', idx=list(adg_to_set.keys()), attr='v', value=list(adg_to_set.values()))\n",
    "\n",
    "        # set into rg, Exclude NaN values for rg index\n",
    "        arg_to_set = {rg: arg + prg for rg, arg,\n",
    "                      prg in zip(maptab['rg_idx'], maptab['arg'], maptab['prg']) if bool(rg)}\n",
    "        sa.RenGen.set(src='Pref', idx=list(arg_to_set.keys()), attr='v', value=list(arg_to_set.values()))\n",
    "\n",
    "    # --- TDS interval ---\n",
    "    if t > 0:  # --- run TDS ---\n",
    "        # set laod into PQ.Ppf and PQ.Qpf\n",
    "        sa.PQ.set(src='Ppf', idx=pq_idx, attr='v', value=load_coeff[t] * p0_sa)\n",
    "        sa.PQ.set(src='Qpf', idx=pq_idx, attr='v', value=load_coeff[t] * q0_sa)\n",
    "        sa.TDS.config.tf = t\n",
    "        sa.TDS.run()\n",
    "        # Update AGC PI controller\n",
    "        ACE_raw = -(kp * sa.ACEc.ace.v.sum() + ki * ACE_integral)\n",
    "        ACE_integral = ACE_integral + sa.ACEc.ace.v.sum()\n",
    "\n",
    "        # record AGC data\n",
    "        # agc reference\n",
    "        agc_ref.loc[t, 'time'] = t\n",
    "        agc_ref.loc[t, agc_cols] = maptab[['agov', 'adg', 'arg']].sum(axis=1).values\n",
    "        # delivered AGC power\n",
    "        sp.dyn.receive(adsys=sa, routine='RTED', no_update=True)\n",
    "        pout = sp.recent.get(src='pg0', attr='v', idx=maptab['stg_idx'])\n",
    "        pref = sp.recent.get(src='pg', attr='v', idx=maptab['stg_idx'])\n",
    "        # agc output is the difference between output power and scheduled power\n",
    "        agc_out.loc[t, 'time'] = t\n",
    "        agc_out.loc[t, agc_cols] = pout - pref\n",
    "\n",
    "        # check if to continue\n",
    "        if sa.exit_code != 0:\n",
    "            print(f\"ERROR! t={t}, TDS error: {sa.exit_code}\")\n",
    "            break\n",
    "    else:  # --- init TDS ---\n",
    "        # set pg to StaticGen.p0\n",
    "        sa.StaticGen.set(src='p0', idx=sp.RTED.pg.get_all_idxes(), attr='v', value=sp.RTED.pg.v)\n",
    "        # set Bus.v to StaticGen.v\n",
    "        bus_stg = sp.StaticGen.get(src='bus', attr='v', idx=sp.StaticGen.get_all_idxes())\n",
    "        v_stg = sp.Bus.get(src='v', attr='v', idx=bus_stg)\n",
    "        sa.StaticGen.set(src='v0', idx=sp.StaticGen.get_all_idxes(), attr='v', value=v_stg)\n",
    "        # set vBus to Bus\n",
    "        sa.Bus.set(src='v0', idx=sp.RTED.vBus.get_all_idxes(), attr='v', value=sp.RTED.vBus.v)\n",
    "        # set load into PQ.p0 and PQ.q0\n",
    "        sa.PQ.set(src='p0', idx=pq_idx, attr='v', value=load_coeff[t] * p0_sa)\n",
    "        sa.PQ.set(src='q0', idx=pq_idx, attr='v', value=load_coeff[t] * q0_sa)\n",
    "        sa.PFlow.run()  # run power flow\n",
    "        sa.TDS.init()  # initialize TDS\n",
    "\n",
    "        if sa.exit_code != 0:\n",
    "            print(f\"ERROR! t={t}, TDS init error: {sa.exit_code}\")\n",
    "            break\n",
    "        print(f\"--ANDES: TDS initialized.\")\n",
    "\n",
    "    # --- record output ---\n",
    "    outdf.loc[t, 'time'] = t\n",
    "    outdf.loc[t, 'freq'] = sa.BusFreq.f.v[1]\n",
    "    outdf.loc[t, 'ACE'] = sa.ACEc.ace.v.sum()\n",
    "    outdf.loc[t, 'pd_andes'] = sa.PQ.Ppf.v.sum()\n",
    "    outdf.loc[t, 'pd_ams'] = sp.RTED.pd.v.sum()\n",
    "    outdf.loc[t, 'pg_ams'] = sp.RTED.pg.v.sum()\n",
    "    outdf.loc[t, 'pru_ams'] = sp.RTED.pru.v.sum()\n",
    "    outdf.loc[t, 'prd_ams'] = sp.RTED.prd.v.sum()\n",
    "\n",
    "# crop the output with valid time\n",
    "if t < total_time - 1:  # end early, means some error happened\n",
    "    outdf = outdf[0:t-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale to nominal value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "outdf_plt = outdf.copy()\n",
    "agc_out_plt = agc_out.copy()\n",
    "agc_ref_plt = agc_ref.copy()\n",
    "\n",
    "pg_ref_plt = pg_ref.copy()\n",
    "pru_ref_plt = pru_ref.copy()\n",
    "prd_ref_plt = prd_ref.copy()\n",
    "\n",
    "# scale to nominal values\n",
    "outdf_plt['freq'] *= sa.config.freq\n",
    "mva_cols = ['pd_andes', 'pd_ams', 'pg_ams',\n",
    "            'pru_ams', 'prd_ams',\n",
    "            'ACE', 'AGC']\n",
    "outdf_plt['prd_ams'] *= -1\n",
    "outdf_plt[mva_cols] *= sa.config.mva\n",
    "\n",
    "agc_out_plt[agc_cols] *= sa.config.mva\n",
    "agc_ref_plt[agc_cols] *= sa.config.mva\n",
    "\n",
    "pg_ref_plt[gen_cols] *= sa.config.mva\n",
    "pru_ref_plt[gen_cols] *= sa.config.mva\n",
    "prd_ref_plt[gen_cols] *= sa.config.mva\n",
    "\n",
    "# calculate frequency deviation\n",
    "outdf_plt['fd'] = outdf_plt['freq'] - sa.config.freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dispatch results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dispatch generation\n",
      "   n_rted    Slack_39       PV_38       PV_37       PV_36       PV_35  \\\n",
      "0     0.0  386.970702  383.193201  380.621217  380.820996  382.533818   \n",
      "1     1.0  379.935352  376.269537  373.771156  373.934512  375.594240   \n",
      "2     2.0  380.257507  376.586538  374.084835  374.249842  375.911982   \n",
      "\n",
      "        PV_34       PV_33       PV_32       PV_31       PV_30  \n",
      "0  367.937512  379.728940  385.417787  387.455488  385.588320  \n",
      "1  361.574196  372.866231  378.381743  380.365045  378.582243  \n",
      "2  361.865859  373.180473  378.703844  380.689629  378.903045  \n",
      "Dispatch RegUp\n",
      "   n_rted  Slack_39     PV_38     PV_37     PV_36     PV_35     PV_34  \\\n",
      "0     0.0  1.287971  0.825139  0.442992  0.267389  0.574634  0.266773   \n",
      "1     1.0  1.297501  0.836450  0.441245  0.263458  0.576924  0.263087   \n",
      "2     2.0  1.295520  0.837502  0.441919  0.264108  0.577644  0.263742   \n",
      "\n",
      "      PV_33     PV_32     PV_31     PV_30  \n",
      "0  0.267681  0.442814  0.513461  0.698435  \n",
      "1  0.263505  0.441893  0.513794  0.698779  \n",
      "2  0.264153  0.442587  0.514523  0.699629  \n",
      "Dispatch RegDn\n",
      "   n_rted  Slack_39     PV_38     PV_37     PV_36     PV_35     PV_34  \\\n",
      "0     0.0  1.061255  0.886641  0.453684  0.265824  0.633451  0.258944   \n",
      "1     1.0  1.068877  0.876098  0.477181  0.262271  0.627822  0.260922   \n",
      "2     2.0  1.077700  0.874332  0.476962  0.262233  0.627325  0.260933   \n",
      "\n",
      "      PV_33     PV_32     PV_31     PV_30  \n",
      "0  0.272305  0.479759  0.546196  0.729230  \n",
      "1  0.262731  0.467584  0.542867  0.750283  \n",
      "2  0.262696  0.467352  0.542516  0.749276  \n"
     ]
    }
   ],
   "source": [
    "print('Dispatch generation')\n",
    "print(pg_ref_plt)\n",
    "\n",
    "print('Dispatch RegUp')\n",
    "print(pru_ref_plt)\n",
    "\n",
    "print('Dispatch RegDn')\n",
    "print(prd_ref_plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AGC mileage in MWh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AGC milage in MWh:\n",
      "Slack_39    0.192374\n",
      "PV_38       0.018822\n",
      "PV_37       0.014038\n",
      "PV_36       0.013970\n",
      "PV_35       0.014016\n",
      "PV_34       0.012151\n",
      "PV_33       0.015037\n",
      "PV_32       0.013776\n",
      "PV_31       0.014142\n",
      "PV_30       0.014817\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "agc_row_index = np.arange(0, total_time, AGC_interval)\n",
    "agc_mileage = agc_out_plt.loc[agc_row_index, agc_cols].diff().abs().sum(axis=0) * AGC_interval / 3600\n",
    "\n",
    "print(f\"AGC milage in MWh:\\n{agc_mileage}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CPS1 score\n",
    "\n",
    "Following adjustments are made to calculate the CPS1 score:\n",
    "1. eps is the constant derived from a targeted frequency bound, where we use the average frequency deviation as the reference\n",
    "1. The $CF_{clock-minute}$ is used when calcualting $CF$ rather than $CF_{12-month}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPS1 score: 100.00360714360514\n"
     ]
    }
   ],
   "source": [
    "def cm_avg(x):\n",
    "    \"\"\"\n",
    "    Clock minute average\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x : pd.Series\n",
    "        Input time series, index is time in seconds.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    float\n",
    "        Clock minute average of the time series.\n",
    "    \"\"\"\n",
    "    return np.sum(x) / len(x)\n",
    "\n",
    "eps = outdf_plt['fd'].mean()\n",
    "bias = sa.ACEc.bias.v[0]\n",
    "\n",
    "df_cm = cm_avg(outdf_plt['fd'])\n",
    "ace_cm = cm_avg(outdf_plt['ACE'] / (10 * bias))\n",
    "cf_cm = df_cm * ace_cm \n",
    "cf = cf_cm / np.square(eps)\n",
    "cps1 = (2 - cf) * 100\n",
    "\n",
    "print(f\"CPS1 score: {cps1}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "System dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x16d307b60>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize=(10, 10), dpi=100)\n",
    "\n",
    "outdf_plt.plot(x='time', y='pd_andes', ax=ax[0, 0],\n",
    "               title='Total Load [MW]',\n",
    "               xlim=[0, total_time],\n",
    "               legend=True, label='Load ANDES')\n",
    "outdf_plt.plot(x='time', y='pd_ams', ax=ax[0, 0],\n",
    "               legend=True, label='Load AMS')\n",
    "outdf_plt.plot(x='time', y='pg_ams', ax=ax[0, 0],\n",
    "               grid=True,\n",
    "               legend=True, label='Gen AMS',\n",
    "               xlabel='Time [s]')\n",
    "\n",
    "outdf_plt.plot(x='time', y='freq', ax=ax[0, 1],\n",
    "               title='Frequency [Hz]', grid=True,\n",
    "               xlim=[0, total_time], legend=False,\n",
    "               xlabel='Time [s]')\n",
    "\n",
    "outdf_plt.plot(x='time', y='ACE', ax=ax[1, 0],\n",
    "               title='ACE [MW]', grid=True,\n",
    "               xlim=[0, total_time], legend=False,\n",
    "               xlabel='Time [s]')\n",
    "\n",
    "outdf_plt.plot(x='time', y='AGC', ax=ax[1, 1],\n",
    "               title='AGC Power [MW]',\n",
    "               xlim=[0, total_time],\n",
    "               legend=True, label='AGC',\n",
    "               xlabel='Time [s]')\n",
    "outdf_plt.plot(x='time', y='pru_ams', ax=ax[1, 1],\n",
    "               legend=True, label='RegUp',\n",
    "               style='--', color='black')\n",
    "outdf_plt.plot(x='time', y='prd_ams', ax=ax[1, 1],\n",
    "               grid=True,\n",
    "               legend=True, label='RegDn',\n",
    "               style='--', color='black')\n",
    "ax[1, 1].legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Frequency regulation performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ACE [MW]', ylabel='Density'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_freq, ax_freq = plt.subplots(1, 2, figsize=(10, 5), dpi=100)\n",
    "\n",
    "outdf_plt.plot(x='time', y='fd', kind='hist', alpha=1, bins=60, density=True,\n",
    "               ax=ax_freq[0], xlabel='Frequency Deviation [Hz]', ylabel='Density',\n",
    "               legend=False)\n",
    "outdf_plt.plot(x='time', y='ACE', kind='hist', alpha=1, bins=60, density=True,\n",
    "               ax=ax_freq[1], xlabel='ACE [MW]', ylabel='Density',\n",
    "               legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings to Improve Performance\n",
    "\n",
    "Long-term dynamic simulation can be memory-consuming, as time-series data is updated by default. To reduce the memory burden, we can configure the TDS with `save_every=0`, discarding all data immediately after each simulation step. As a trade-off, a separate output array is utilized to store the data, with a resolution matching the co-simulation time step.\n",
    "More details about ANDES settings can be found in the [ANDES Release notes - v1.7.0](https://docs.andes.app/en/latest/release-notes.html#v1-7-0-2022-05-22).\n",
    "\n",
    "The case has been tested with a complete 3600s duration. However, for demonstration purposes and to conserve CI resources, the simulated time is truncated.\n",
    "\n",
    "## Limitations\n",
    "\n",
    "1. Although the code is designed for generalization, the demo is implemented on the IEEE 39-bus case with generators set to synchronous machines, and its application to other cases is not fully tested.\n",
    "1. The load curve is synthetic, based on experience.\n",
    "1. Within each interval, generator setpoints are updated only once, without considering smooth action.\n",
    "1. In ANDES, certain dynamic parameters are adjusted to facilitate co-simulation, disregarding their actual physical implications.\n",
    "1. The used case comprises synchronous generators exclusively, necessitating further adaptation for the inclusion of renewable energy sources.\n",
    "\n",
    "## FAQ\n",
    "\n",
    "Q: Why ANDES TDS run into error?\n",
    "\n",
    "A: Most likely, the error is due to power flow not converging. Possible reasons include: 1) load is too heavy, 2) step change is too large, 3) some devices run into limits.\n",
    "\n",
    "Q: Why in AMS RTED, load and generation do not exactly match?\n",
    "\n",
    "A: The RTED is converted using ``dc2ac``, where the generation and bus voltage are adjusted using ACOPF."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "amst",
   "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": "3.10.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}