{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detailed Secondary Frequency Regulation Study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we will show 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",
    "References:\n",
    "\n",
    "1. J. Wang et al., \"Electric Vehicles Charging Time Constrained Deliverable Provision of Secondary Frequency Regulation,\" in IEEE Transactions on Smart Grid, vol. 15, no. 4, pp. 3892-3903, July 2024, doi: [10.1109/TSG.2024.3356948](https://ieeexplore.ieee.org/document/10411057).\n",
    "1. NERC, “Standard BAL-001-2 – Real Power Balancing Control Performance,” https://www.nerc.com/pa/Stand/Reliability%20Standards/BAL-001-2.pdf"
   ]
  },
  {
   "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 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": [],
   "source": [
    "andes.config_logger(stream_level=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.34256, 0.     ])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.RTED.dud.v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05, 0.05])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.RTED.du.v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dynamic case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating code for 1 models on 12 processes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Following PFlow models in addfile will be overwritten: <Bus>, <PQ>, <PV>, <Slack>, <Shunt>, <Line>, <Area>\n",
      "AMS system 0x3327aeb10 is linked to the ANDES system 0x33274ede0.\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": 10,
   "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": 10,
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x334cff350>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/__/n5kx_m_s0tbg6n5qd7rh51700000gn/T/ipykernel_34212/3863718312.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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building system matrices\n",
      "Parsing OModel for <RTED>\n",
      "Evaluating OModel for <RTED>\n",
      "Finalizing OModel for <RTED>\n",
      "<RTED> solved as optimal in 0.0119 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": [
      "<ACOPF> solved in 0.2337 seconds, converged in 24 iterations with PYPOWER-PIPS.\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",
      "--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 0.0118 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": [
      "<ACOPF> solved in 0.2808 seconds, converged in 30 iterations with PYPOWER-PIPS.\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 0.0119 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": [
      "<ACOPF> solved in 0.3197 seconds, converged in 34 iterations with PYPOWER-PIPS.\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": 14,
   "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": 15,
   "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  376.586537  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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPS1 score: 100.00360717514486\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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x334ccbbc0>"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ACE [MW]', ylabel='Density'>"
      ]
     },
     "execution_count": 19,
     "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)"
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    "## 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."
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