workflow_lindistflow.ipynb

{
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   "cell_type": "markdown",
   "id": "1f3d612b-e411-4ac3-a5e5-eb7e49b46c01",
   "metadata": {},
   "source": [
    "# Linearized Distribution Flow (LinDistFlow) for PV control "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "58855b1c-bb15-473a-9844-449af185a388",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T20:44:59.647931Z",
     "start_time": "2024-09-12T20:44:58.235065Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import math\n",
    "from oedisi.types.data_types import MeasurementArray, AdmittanceMatrix, Topology\n",
    "import pyarrow.feather as feather\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import glob\n",
    "\n",
    "ROOT = os.getcwd()\n",
    "OUTPUTS = f\"{ROOT}/outputs\"\n",
    "BUILDS = f\"{ROOT}/builds\"\n",
    "SCENARIOS = f\"{ROOT}/scenarios\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d845732-e22e-463a-8979-701c9f9c38c1",
   "metadata": {},
   "source": [
    "## Run OEDISI\n",
    "Each scenario is defined in the following folder structure:\n",
    "- algorithm / model / pv-level / es-level\n",
    "\n",
    "The following shows which scenarios have been generated using one of the algorithm generation scripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "d28a2fb5-b7ab-4678-a360-24e76e1e4c3e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T20:44:59.678661Z",
     "start_time": "2024-09-12T20:44:59.672491Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 /home/tylor/dev/oedisi_dopf/scenarios/lindistflow/SFO/P6U/extreme/\n",
      "1 /home/tylor/dev/oedisi_dopf/scenarios/lindistflow/SFO/P6U/low/\n",
      "2 /home/tylor/dev/oedisi_dopf/scenarios/lindistflow/SFO/P6U/high/\n",
      "3 /home/tylor/dev/oedisi_dopf/scenarios/lindistflow/SFO/P6U/medium/\n",
      "4 /home/tylor/dev/oedisi_dopf/scenarios/lindistflow/SFO/P9U/extreme/\n",
      "5 /home/tylor/dev/oedisi_dopf/scenarios/lindistflow/SFO/P9U/low/\n",
      "6 /home/tylor/dev/oedisi_dopf/scenarios/lindistflow/SFO/P9U/high/\n",
      "7 /home/tylor/dev/oedisi_dopf/scenarios/lindistflow/SFO/P9U/medium/\n",
      "8 /home/tylor/dev/oedisi_dopf/scenarios/lindistflow/SFO/P1U/extreme/\n",
      "9 /home/tylor/dev/oedisi_dopf/scenarios/lindistflow/SFO/P1U/low/\n",
      "10 /home/tylor/dev/oedisi_dopf/scenarios/lindistflow/SFO/P1U/high/\n",
      "11 /home/tylor/dev/oedisi_dopf/scenarios/lindistflow/SFO/P1U/medium/\n",
      "12 /home/tylor/dev/oedisi_dopf/scenarios/lindistflow/ieee123/\n"
     ]
    }
   ],
   "source": [
    "dir = []\n",
    "def list_files_scandir(path=\".\"):\n",
    "    with os.scandir(path) as entries:\n",
    "        for entry in entries:\n",
    "            if entry.is_file():\n",
    "                if \"system.json\" in entry.path:\n",
    "                    path, _ = entry.path.split(\"system.json\", 1)\n",
    "                    dir.append(path)\n",
    "            elif entry.is_dir():\n",
    "                list_files_scandir(entry.path)\n",
    "\n",
    "\n",
    "# Specify the directory path you want to start from\n",
    "directory_path = f\"{SCENARIOS}/lindistflow\"\n",
    "list_files_scandir(directory_path)\n",
    "for i, d in enumerate(dir):\n",
    "    print(i, d)"
   ]
  },
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   "execution_count": 211,
   "id": "fc1e6b02-6fcb-4418-87f6-df0545c64093",
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     "end_time": "2024-09-12T20:44:59.887329Z",
     "start_time": "2024-09-12T20:44:59.884702Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading the components defined in /home/tylor/dev/oedisi_dopf/scenarios/lindistflow/SFO/P1U/extreme/components.json\n",
      "Loading system json /home/tylor/dev/oedisi_dopf/scenarios/lindistflow/SFO/P1U/extreme/system.json\n",
      "Building system in /home/tylor/dev/oedisi_dopf/builds/lindistflow/SFO/P1U/extreme/\n"
     ]
    },
    {
     "data": {
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     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "index = 8\n",
    "path = dir[index]\n",
    "_, scenario = path.split(SCENARIOS, 1)\n",
    "build = f\"{BUILDS}{scenario}\"\n",
    "output = f\"{OUTPUTS}{scenario}\"\n",
    "os.system(\n",
    "    f\"oedisi build --system {path}system.json --component-dict {path}components.json --target-directory {build}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "c08366e8-7bce-4329-8119-947ae5363ba8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T20:44:59.991024Z",
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[warn] helics-cli's web interface is not installed. You may want to run `pip install \"helics[cli]\"`.\n",
      "[warn] helics-cli's observer functionality is not installed. You may want to run `pip install \"helics[cli]\"`.\n",
      "[info] Running federation: lindistflow_SFO/P1U\n",
      "[info] Running federate feeder as a background process\n",
      "[info] Running federate lindistflow as a background process\n",
      "[info] Running federate recorder_voltage_real as a background process\n",
      "[info] Running federate recorder_voltage_imag as a background process\n",
      "[info] Running federate recorder_power_real as a background process\n",
      "[info] Running federate recorder_power_imag as a background process\n",
      "[info] Running federate recorder_voltage_mag as a background process\n",
      "[info] Running federate recorder_voltage_angle as a background process\n",
      "[info] Running federate recorder_power_mag as a background process\n",
      "[info] Running federate recorder_power_angle as a background process\n",
      "[info] Running federate recorder_solver_stats as a background process\n",
      "[info] Running federate recorder_estimated_power as a background process\n",
      "[info] Running federate recorder_available_power as a background process\n",
      "[info] Running federate broker as a background process\n",
      "[info] Waiting for 14 processes to finish ...\n",
      "[info] Done.\n"
     ]
    },
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     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system(\"pkill -9 helics_broker\")\n",
    "os.system(f\"oedisi run --runner {build}system_runner.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b21cdc3-2ba4-4236-bcdf-0853d4c9be06",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "01e8545a-8584-4eff-83b6-c03c40694f96",
   "metadata": {},
   "outputs": [],
   "source": [
    "topology = Topology.parse_file(f\"{output}/topology.json\")\n",
    "base_voltage_df = pd.DataFrame(\n",
    "    {\n",
    "        \"id\": topology.base_voltage_magnitudes.ids,\n",
    "        \"value\": topology.base_voltage_magnitudes.values,\n",
    "    }\n",
    ")\n",
    "base_voltage_df.set_index(\"id\", inplace=True)\n",
    "base_voltages = base_voltage_df[\"value\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "7ff13bdc-9cfa-40a0-aa4d-7b0b52ca6473",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T20:45:00.242505Z",
     "start_time": "2024-09-12T20:45:00.043967Z"
    }
   },
   "outputs": [],
   "source": [
    "# read data\n",
    "v_real = feather.read_feather(f\"{output}/voltage_real.feather\")\n",
    "v_imag = feather.read_feather(f\"{output}/voltage_imag.feather\")\n",
    "\n",
    "# converter to complex\n",
    "true_voltages = v_real.drop(\"time\", axis=1).abs() + 1j * v_imag.drop(\"time\", axis=1)\n",
    "true_voltages[\"time\"] = pd.to_datetime(v_real[\"time\"], format=\"%Y-%m-%d %H:%M:%S\")\n",
    "true_voltages.set_index(\"time\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "03a396a8-0f46-4402-a32e-1449079aeb4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data\n",
    "v_mag = feather.read_feather(f\"{output}/voltage_mag.feather\")\n",
    "v_angle = feather.read_feather(f\"{output}/voltage_angle.feather\")\n",
    "\n",
    "# filter missing nodes\n",
    "common_columns = set(v_mag.columns).intersection(v_angle.columns)\n",
    "v_mag = v_mag.filter(common_columns)\n",
    "v_angle = v_angle.filter(common_columns)\n",
    "\n",
    "# convert to complex\n",
    "v_rad = np.radians(v_angle.drop(\"time\", axis=1).to_numpy())\n",
    "est_voltages = v_mag.drop(\"time\", axis=1) * np.exp(1j * v_rad)\n",
    "est_voltages[\"time\"] = pd.to_datetime(v_mag[\"time\"], format=\"%Y-%m-%d %H:%M:%S\")\n",
    "est_voltages.set_index(\"time\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "ae6c2ed7-90dd-4448-853d-103deb56906c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data\n",
    "p_real = feather.read_feather(f\"{output}/power_real.feather\")\n",
    "p_imag = feather.read_feather(f\"{output}/power_imag.feather\")\n",
    "\n",
    "# filter missing nodes\n",
    "common_columns = set(p_real.columns).intersection(p_imag.columns)\n",
    "p_real = p_real.filter(common_columns)\n",
    "p_imag = p_imag.filter(common_columns)\n",
    "\n",
    "# convert to complex\n",
    "true_powers = p_real.drop(\"time\", axis=1) + 1j * p_imag.drop(\"time\", axis=1)\n",
    "true_powers[\"time\"] = pd.to_datetime(p_real[\"time\"], format=\"%Y-%m-%d %H:%M:%S\")\n",
    "true_powers.set_index(\"time\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "2843c053-5ece-4a72-952f-766894643b14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data\n",
    "p_mag = feather.read_feather(f\"{output}/power_mag.feather\")\n",
    "p_angle = feather.read_feather(f\"{output}/power_angle.feather\")\n",
    "\n",
    "# filter missing nodes\n",
    "common_columns = set(p_mag.columns).intersection(p_angle.columns)\n",
    "p_mag = p_mag.filter(common_columns)\n",
    "p_angle = p_angle.filter(common_columns)\n",
    "\n",
    "# convert to complex\n",
    "est_powers = p_mag.drop(\"time\", axis=1) * np.exp(1j * p_angle.drop(\"time\", axis=1))\n",
    "est_powers[\"time\"] = pd.to_datetime(p_mag[\"time\"], format=\"%Y-%m-%d %H:%M:%S\")\n",
    "est_powers.set_index(\"time\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "7465223f-918e-4ae5-8c35-e84189e8feb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data\n",
    "pv_true = feather.read_feather(f\"{output}/available_power.feather\")\n",
    "pv_true[\"time\"] = pd.to_datetime(pv_true[\"time\"], format=\"%Y-%m-%d %H:%M:%S\")\n",
    "pv_true.set_index(\"time\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "d9365e24-72e9-4d67-9b03-22b5e3bff8e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data\n",
    "pv_est = feather.read_feather(f\"{output}/estimated_power.feather\")\n",
    "pv_est[\"time\"] = pd.to_datetime(pv_est[\"time\"], format=\"%Y-%m-%d %H:%M:%S\")\n",
    "pv_est.set_index(\"time\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "839e1621-c5fb-4db5-8764-48245c1501ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data\n",
    "stats = feather.read_feather(f\"{output}/solver_stats.feather\")\n",
    "stats[\"time\"] = pd.to_datetime(stats[\"time\"], format=\"%Y-%m-%d %H:%M:%S\")\n",
    "stats.set_index(\"time\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff3015f-3f30-49e5-942d-e62b0633731c",
   "metadata": {},
   "source": [
    "## Process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "66cbb764-c18a-4fe1-af44-ec8e68201f83",
   "metadata": {},
   "outputs": [
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                       {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('4f02763d-a78f-4c5c-850e-c7de63a741d9');\n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = dt.datetime(2018, 5, 1, 12)\n",
    "true = true_voltages.loc[ts].abs() / base_voltages\n",
    "est = est_voltages.loc[ts].abs()/base_voltages\n",
    "error = (true - est).abs()\n",
    "\n",
    "fig = make_subplots(rows=1, cols=2)\n",
    "fig.add_trace(\n",
    "    go.Scatter(name=\"base\", x=true.index, y=true.values, mode=\"markers\"), row=1, col=1\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(name=\"opt\", x=est.index, y=est.values, mode=\"markers\"), row=1, col=1\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(name=\"diff\", x=error.index, y=error.values, mode=\"markers\"),\n",
    "    row=1,\n",
    "    col=2,\n",
    ")\n",
    "fig.update_layout(template=\"plotly_white\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "f41dfbcc-9037-43c0-82b4-c57611bb1b06",
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "true = true_powers.loc[ts]\n",
    "est = est_powers.loc[ts]\n",
    "error = true - est\n",
    "\n",
    "fig = make_subplots(rows=1, cols=2)\n",
    "fig.add_trace(\n",
    "    go.Scatter(name=\"base\", x=true.index, y=true.values.real, mode=\"markers\"),\n",
    "    row=1,\n",
    "    col=1,\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(name=\"opt\", x=est.index, y=est.values.real, mode=\"markers\"), row=1, col=1\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(name=\"diff\", x=error.index, y=error.abs(), mode=\"markers\"), row=1, col=2\n",
    ")\n",
    "fig.update_layout(template=\"plotly_white\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "08ebeff1-6578-4118-a760-a3a61f0ad0e2",
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "true = pv_true.loc[ts]\n",
    "est = pv_est.loc[ts]\n",
    "\n",
    "fig = make_subplots(rows=1, cols=1)\n",
    "fig.add_trace(go.Scatter(name=\"base\", x=true.index, y=true.values.real, mode=\"markers\"), row=1, col=1)\n",
    "fig.add_trace(\n",
    "    go.Scatter(name=\"opt\", x=est.index, y=est.values.real, mode=\"markers\"), row=1, col=1\n",
    ")\n",
    "fig.update_layout(template=\"plotly_white\")\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "dbb9d74f-194e-43f5-9783-833fe8e625d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     solve_time  num_iters  optimality_gap  feasibility_gap\n",
      "time                                                                       \n",
      "2018-05-01 11:30:09    0.006770       11.0    9.736419e-10     1.426335e-14\n",
      "2018-05-01 11:31:30    0.007213       11.0    1.588479e-09     2.344963e-14\n",
      "2018-05-01 11:33:00    0.007123       11.0    1.336130e-09     2.074938e-14\n",
      "2018-05-01 11:34:30    0.007419       11.0    8.806084e-10     1.655904e-14\n",
      "2018-05-01 11:36:00    0.006759       11.0    8.739643e-10     2.296572e-14\n"
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     "metadata": {},
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   ],
   "source": [
    "print(stats.head())\n",
    "fig = make_subplots(rows=4, cols=1,shared_xaxes=True)\n",
    "fig.add_trace(go.Scatter(name=\"runtime\", x=stats.index, y=stats.solve_time, mode=\"lines\"), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(name=\"iterations\", x=stats.index, y=stats.num_iters, mode=\"lines\"), row=2, col=1)\n",
    "fig.add_trace(go.Scatter(name=\"optimality_gap\", x=stats.index, y=stats.optimality_gap, mode=\"lines\"), row=3, col=1)\n",
    "fig.add_trace(go.Scatter(name=\"feasibility_gap\", x=stats.index, y=stats.feasibility_gap, mode=\"lines\"), row=4, col=1)\n",
    "fig.update_layout(template=\"plotly_white\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "314a6556-8fc3-48b4-85c1-8072564a12ec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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