GirdObserver_ReactiveLoad.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GirdObserver- ReactiveLoad.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMX18zR04jwjQv9JHrR3yOA",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Fernando3161/GridObserver/blob/main/GirdObserver_ReactiveLoad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hyiQ1h-jH3Xb"
      },
      "outputs": [],
      "source": [
        "!pip install pandapower\n",
        "!pip install python-igraph"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import pandapower as pp\n",
        "import numpy as np\n",
        "import copy"
      ],
      "metadata": {
        "id": "2LF_weznH79b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# This is a dummy grid that will be used as experimentation object\n",
        "\n",
        "def create_net(houses=3, f_l=1, f_pv=1):\n",
        "    \"\"\"\n",
        "    f_1: Factor for scaling the loads (Too big netwokrs require lower loads for\n",
        "          the network to function)\n",
        "    f_pv: Factor for scaling the PVs Gens (To produce overvoltages)\n",
        "\n",
        "    Simple net that looks like:\n",
        "\n",
        "    ext_grid b0\n",
        "    |\n",
        "    b1\n",
        "    |\n",
        "    trafo(10/0.4)\n",
        "    |\n",
        "    b2\n",
        "    |\n",
        "    b3--------------b4 -------------b5-------------and more elements\n",
        "    |               |               |\n",
        "    |-----|----|    |-----|-----|   |-----|-----|\n",
        "    load  sto  PV   load  sto  PV   load  sto  PV\n",
        "    \"\"\"\n",
        "    net = pp.create_empty_network()\n",
        "\n",
        "    b0 = pp.create_bus(net, vn_kv=10, name=\"bus0_grid\")\n",
        "    b1 = pp.create_bus(net, vn_kv=10, name=\"bus1_trafo_hv\" )\n",
        "    b2 = pp.create_bus(net, vn_kv=0.4, name=\"bus2_trafo_lv\")\n",
        "    \n",
        "    # houses_buses__list\n",
        "    hbl=[]\n",
        "    for i in range(houses):\n",
        "      b = pp.create_bus(net, vn_kv=0.4, name=f\"bus_house_{i}\")\n",
        "      hbl.append(b)\n",
        "\n",
        "    # External grid\n",
        "    pp.create_ext_grid(net, b0)\n",
        "    pp.create_line(net, b0, b1, 0.20, \"34-AL1/6-ST1A 10.0\")\n",
        "\n",
        "    # Transformer\n",
        "    pp.create_transformer(net, b1, b2, \"0.4 MVA 10/0.4 kV\", name='tr1')\n",
        "    \n",
        "    # Line for the first house\n",
        "    pp.create_line(net, b2, hbl[0], 0.20, \"34-AL1/6-ST1A 10.0\")\n",
        "\n",
        "    # Line for from the n house to the n+1 house\n",
        "    for i in range(houses-1):\n",
        "      pp.create_line(net, hbl[i], hbl[i+1], 0.20, \"94-AL1/15-ST1A 0.4\")\n",
        "    \n",
        "    # Create the elements of the buses\n",
        "    # Load starts at 30, 40, 50 kW\n",
        "    # SGens are PVs and go from 10,15,20 kW\n",
        "    # Storages are assumed to be at 50% and can charge or discharge\n",
        "\n",
        "    for i,b in enumerate(hbl):\n",
        "        pp.create_load(net, b, p_mw=(30 + 10 * i)*f_l, q_mvar=0,\n",
        "                       name=f'load{i}', scaling=1 / 1000)\n",
        "        pp.create_sgen(net, b, p_mw=(10 + 5 * i)*f_pv, q_mvar=0,\n",
        "                       name=f'sgen{i}', scaling=1 / 1000)\n",
        "        pp.create_storage(net, bus=b, p_mw=0, q_mvar=0, max_e_mwh=1000, \n",
        "                          name=f'storage{i}', scaling=1 / 1000)\n",
        "\n",
        "    return net"
      ],
      "metadata": {
        "id": "7G-sNvIuH9mj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example only with active load\n",
        "net = create_net(5, f_l=0.66)\n",
        "pp.runpp(net)\n",
        "\n",
        "# Dataframe to save each iteration\n",
        "bus_res=pd.DataFrame()\n",
        "bus_res[0]=net.res_bus[\"vm_pu\"]\n",
        "k=1\n",
        "converge = False\n",
        "while not converge:\n",
        "  jacobian = net._ppc[\"internal\"][\"J\"].todense()\n",
        "  list_v = [0]*(len(net.res_bus[\"vm_pu\"])-1)\n",
        "\n",
        "  for v in net.res_bus[\"vm_pu\"][1:]:  # 0 is always the slack\n",
        "      if v < 0.95:\n",
        "          dv = (0.95 - v)*400\n",
        "      elif v > 1.05:\n",
        "          dv = (1.05 - v)*400\n",
        "      else:\n",
        "          dv = 0\n",
        "      list_v.append(dv)\n",
        "\n",
        "  deltas_mx = np.dot(jacobian, list_v)\n",
        "  for i in net.storage.index:\n",
        "    # here a limit can be implemented\n",
        "    lim = 200\n",
        "    if net.storage.at[i,\"p_mw\"]-deltas_mx[0,i+2]>lim:\n",
        "      net.storage.at[i,\"p_mw\"]=lim\n",
        "    \n",
        "    elif net.storage.at[i,\"p_mw\"]-deltas_mx[0,i+2]<-lim:\n",
        "      net.storage.at[i,\"p_mw\"]=-lim\n",
        "    else:\n",
        "      net.storage.at[i,\"p_mw\"]-=deltas_mx[0,i+2]\n",
        "\n",
        "  pp.runpp(net)\n",
        "  net.res_bus\n",
        "  bus_res[k]=net.res_bus[\"vm_pu\"]\n",
        "  if min(bus_res)>0.95 and max(bus_res)<1.05:\n",
        "    converge = True\n",
        "  \n",
        "  if not converge:\n",
        "    diff =bus_res[k]-bus_res[k-1]\n",
        "    if max(diff)<1/40000:\n",
        "      converge = True\n",
        "    else:\n",
        "      k+=1\n",
        "\n",
        "bus_res.T.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "RhEKXtJuIPMT",
        "outputId": "d1725781-0331-431b-d7e1-1d41f26f9951"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6c9cf18ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ajY9mn4smcdIkrdSLixZcNEsrbmkGur48LdoWRXSkf0mOTiTa4cDhcOCIc+CIi8UR6yA6OhqHw0FUVBRRUVFERERoT1ddtjTYXwaMMbiOHqV58xaaN22iactmvBWVAFiTkogcPZr4RQuJHDnKH9hHjcI2aFCf9ciDuVu91FU0U1feQm15x8/meleHsvYoK7GJkcQkRpKZk0RMYqR/OymqPT061g4CzXW11J4+RV35KWpPnaTyaGD99Cmaaqq7tCM5cSgpiZlkx0wgNikJh8QhHhuNrlbq3c00SAtN4qRCnBwWJ02WEjzBQyRWsFlsxDliiY2NITl2MLEJcf4lNrbDEhMToz1q1e9osL8EjDG4jx+nafNmmjdtpnnz5vahGFtaGjH5M3HMzCcmPx/78OEXJagbY2isaaXieAMVJQ1UljRSWdJAY01rh3KO+AgS0qPJmphCYno0CWkOEgdFE58STUS0rUudDZUVVBw/zOFtR6ksOUZVyTFqT5/C3eo8U1CE2OQUktOHkpNzLcnRQ4i1JBLhioBGqG+op87XRK00U+ZqotZTRr2lhWZa/Ve2IgJti3KQEB/P0OR0EhITSUxMJCEhoX1x9OGQlVKXOw32F5G7rIzav75J3Vtv4S7zv+zLmppKTH4+jvx8HDPzicjO7vOAZIyhvrKFiuON7cG94ngDzkY34L/zIGlIDEPHJpI0KIaE9GgS0x0kpEcTEXX2X5mWxgZKC49RUXKUyuNHqTx+jMqSY7hazgyRxKWmkTpsOMMmTCE5bijxESlEu2OwNgrecieNVfVUlTdQIvVUWcuosTVT52vCZztzV0l0VDSpqSmMTc0mNTWV1NRUUlJSSExMxG639+nPTakrmQb7PmbcbhrWrKH29Tdo+vhjAGKuvZaUL34Bx8yZRIwYcVGCe1VZEyVF1ZTuq+bU4XpcLf4LoharkDw0hhFTUkkbFkdaVhwpmbHYI849jGF8PipLj3OiuJCy4iJOFBdSV366PT8yJobUYdmMnz2XtGHDSUkeRpwvEXPKRevxejyHmml2OzllOUWVpYGqyCaqpIH6yKb2OhITExk0aCi5gWDeFtgdDkff/aCU6sc02PcR19Gj1L7xBrX/9ze8VVXYBg8m9atfJfHOO7BnZPT58RtrnJQUVVNSVEPpvmpaGvy99sRBDsbkpZOWFQjsQ2Ox2ru/qOh2Ojl5sJgTxUWU7S/i5P59tDb7A7MjIZGMcblMnr+QtKxsUjKGE9kSiet4A67j9bRurcdbX8NRKeGkvZbTjgYqoupotJ7p8ScnJDNsSDZDhgxpXzSoKxVeGuzDyNfaSsMHK6h9/XWaN28Gq5XYOXNIvPsuYmfPRvrwol9ri4ey4hpK99VQUlRN7Wl/MI2Os5OZk8yw8clk5iQRl9zzQxtej5vSwr0c3rGFsn17KT96uH1qgZTMLMbNms3QcePJGJdLfHI6riN1OA/X4dpUT33pfozHS400cTq2gZOR9ZyIq8Dp9o/9J0UnMSJzVIfAHhXVdw+SKKX8Qgr2IrIA+BVgBX5vjPlpp/zh+F8yngZUAw8aY0qD8uPxv2j8b8aYx8PU9suGr7WVqt//nuqXX8FXV4d92DDSvvlNEhZ/Bnt6ep8d1+vxcWxPFcUbT3F0dyU+r8EWYWHomCQmzB5KZk4yKRkxIQ0TORsbObJzK4e2buLIzm24Wpqx2SMYMmYc+bffTca48QwZk0NUbCyeulac+6pxvl/NyYOH8bm91FlbKE9q5mRaLWXN5TS3toAbEmMSyRk9nhEjRpCdnU1CQkKf/TyUUufWY7AXESvwG+AmoBTYIiJvG2MKg4o9C7xsjHlJROYBzwAPBeX/GFgXvmZfPhrXr+f00z/GdewYsfNvJPmBB3DMnNln97kbY6g43sC+jac4sOU0zkY30XF2Js3JZMSUVAaPSOhxWKZN7amTHNq2iUPbNlNatAfj8+FISGTcrOsYlTeTrIlTsEdGYXwGV0kDzvWV1O3bj/tkEwZDdYKTY0NqONRURl1TPTRCfHw8o8eNaQ/uSUlJffJzUEqdn1B69vnAQWPMYQARWQrcjr+n3iYX+FZgfQ3wt7YMEZkBDAKWA+d8Ge6Vxl1eTvlPf0b9u+9iH57FsD/8nthrr+2z4zXVtlK86RTFm05RfaIJi00YMTmNnFmDycpNxmINLcCXHz1M8SfrOLRtM1WlxwFIHTac/NvvYtSMmQweNQaxWDBuLy1F1TQUHcNZXI2v2YOxGKqHeDk+uooDtceob2zAUmlh5MiRzM65nhEjRpCcnKy3Nyp1GQol2GcAJUHbpUDnadkKgDvwD/UsBuJEJAWoAX4BPAjMP9cBROQx4DGArKysUNt+SRivl5o/v0bFr36FcblIffxxUr70RSyRkWE/lsft5fDOCoo3nKKkqBpjYPDIeG64fxyjZ6QTFRParYbuVifFGz5m14r3OHmwGIvVSub4CUy+8RZGzphJ4qDB/nMzBndZI01bT9O8sxzj9ILDSs0wL0es5RwoP0JDVSNWq5VRo0YxL/dGxo0bR3R0dNjPXalLwe12U1paitPp7LnwJRIVFUVmZuZ532ocrgu03wZ+LSKP4B+uKcM/gcjXgHeNMaXd9faMMUuAJQB5eXl9OJt277Ts3s2pH/wQZ2EhMddey+B//zcihg8P+3G8Hh9F60+w9d2jNNW5iE2OZMbCbMbNHEzioNDvUqkqPU7ByvcoXLea1qYmkodmMudzXyL3+rlEx8WfOV6Tm+Yd5TRvPY37VBPYLDSPsbHPfprisoM0HWvCZrMxZswYcnNzGTNmjF5UVf1SaWkpcXFxZF+E510uhDGGqqoqSktLGTFixHntG0qwLwOGBW1nBtKCG3ACf88eEYkF7jTG1IrILGC2iHwNiAUiRKTRGPPkebXyEvPW11Px3HPUvLYUW2oqGf/vl8QtWBD2Xwaf10fxptNs+ccRGqqcDBmVwI0P55KZk+SfBjcEHpeLA5vWU7ByOWX79mK12Rgz81qmzF9IxvgJ7W02PkPrgRqatp6mpbAKvAZrhoPyqy0UVO/n2JFj2Gw2xo4d2x7gI/vgrxelLidOp/OyDfQAIkJKSgoVFRXnvW8owX4LMEZERuAP8vcC93dqQCpQbYzxAU/hvzMHY8wDQWUeAfKutEBf984/OP3Tn+KtribpoQdJ++d/xhobG9ZjGJ/h4LZyNr9zhNrTzaRlxXHD/ePIyg19/LvmZBkFK5ez98NVOBvqSRw8hOsfeJQJc+bjiD9zB4yn1knTplM0bzuNt96FJcaG9apk9keeYvu+ddTtrCMhIYGbbrqJadOm6f3uasC5XAN9mwttX4/B3hjjEZHHgffx33r5gjFmr4g8DWw1xrwNzAGeERGDfxjn6xfUmsuIMYbK55+n8r9/S9TkyWQt+R1RublhP8aRgko2//0wVWVNJA+NYeGXJzFiamrI/6D1leWs/8urFH60BovFwui8q5k8fyFZEyd3uCPIU9tKw5rjNG09DT5D1NgknLPjKagsZs+eVXg8HkaMGMGCBQsYN26czt6oVD8T0pi9MeZd4N1Oaf8etP4G8EYPdbwIvHjeLbwEjDGU//RnVL/0Egl33cmQH/0orA9EGWMoKapm01uHKT/WQEJaNDd9PpfReYOwhDhc09LYwOa/vc6O5X8HIO9Ti8n71GJiEjve6uipa6VhTQlNW04BED0jjbLMFrbu2czxVcex2+1MmTKF/Px8Bg0aFLZzVEpdmOXLl/PEE0/g9Xr54he/yJNPhmcwRJ+g7cR4vZz64Y+off11kh56iEFPPRnWe+brKppZ88o+yvbXEpscydyHcsi5enDIt066Xa3seO/vbH7rdVqbm5lw/TyuuecB4lM7PrzlrW+lfk0JTZtPgQHHjHRODnfyfxs+oGpXFYmJidx8881MmzZN76ZR6jLh9Xr5+te/zooVK8jMzOSqq67itttuIzcMowoa7IMYt5sTT32X+nfeIeUrXybtiSfCOn63f8sp1v6pGItFmP3ZsUy4bmjID0D5fF4K163hk2V/oqGqghFTZzD7/kdIG97xiry33kXD2hIaN58EH8TMGETjBDtvbVjDsXeOkZKSwj333ENOTo4O1SjVjR/9fS+FJ87xOs8LlDs0nh98esI58zdv3szo0aMZOXIkAPfeey9vvfWWBvtw8rlclH3zWzSuWkXat75F6mNfClvdLqeHj5YdYN8nJxk8MoGbvpBLfEpovWljDEd2bOWjP79IZckxBo8aw4KvfZOsiZM7lPM2BIL8plPg8+GYPgiTl8Ca7evZtXQXDoeDRYsWMWPGDH0xh1KXqbKyMoYNO3PzY2ZmJps2bQpL3RrsAV9LC6WP/xNN69cz6PvfJ/nBB3reKUQVJQ188Pu91JY3k7com6tuzT6vp13XvLSE0sI9JA4awqe+8a+Mvfq6Dn9tGJ+hacMJ6pYfxXj8QT7yunQ2Fm5jw6vLMMZw7bXXMnv2bL03Xqnz0F0P/Eo04IO9t7GRkq98hZbtOxjyk5+QeOcdYanXGMOuNaV88uZBomPs3P6NaWSOC22eGGMMO5b/nXWvvkCEI4Z5j36ZyfMXYLV1fGLOU9VC9RsHcB2pI3JsEvGfGsHu40WsefUtmpqamDhxIjfeeKPOT6PUFSIjI4OSkjMTFpSWlpIRpinRB3Sw99TUUPKlx3Du20fGL54lfuHCsNTb0uhi9UtFHN1dRfakFOY9PJ7o2IjQ9m2oZ/lvn+Pwts2MnH4Vt3z1Gx3uk4eOvXksQtJdYziR0MCy11+koqKCrKws7rvvPjIzM8NyPkqpi+Oqq67iwIEDHDlyhIyMDJYuXcqf//znsNQ9YIO9p6KC45//Aq5jx8h8/r+Imzs3LPWWFtew8oW9tDS5ue6eMUyemxnyRd6Swt28+/yztNTXMffhLzFt4W1d9vX35vfjOlJP5NgkYm/LZsUnq9n+znaSk5O55557GD9+/GX/YIhSqiubzcavf/1rbrnlFrxeL5///OeZMCE8w0kDMti7T57k+COP4q6oYNjv/oeYWbN6XafP62PLP46y9b2jJKY7uPXrU0jLigtxXy8b31zKxr/+hcTBg/nMj59l0MjRHcp06M1b/b35xmHCC395iYqKCq699lrmzp2LzTYg/0mV6jcWLVrEokWLwl7vgIsMxuOh9J+fwFNVRdbvf49j+rRe1+n1+Fj+u90c3V1FzjVDmH3PmHO+mLuz+soK3n3+Wcr27SV39lxu/MJXiYjuOEVBcG8+alwSCYtHU3BwD8v/dzmRkZE8+OCDjB49+hxHUEqpARjsK5cswbl7NxnP/b+wBHqf18cHf9jL0d1VXH/vWCbNCX2c/ODWTbz/2+fwut0s/Pq3yL1+Xof8s/XmLRMSePPttygqKmLUqFEsXryY2DDP1aOU6n8GVLBv2buXyv/+LfG33kr8ggW9rs/nM6x8sYjDOyq47u4xIQd6j8vFuj/9kR3L/0569ihufeI7JA/teMXd1+ql+rV9OPdVEzUuicQ7xnCyvpw3/ud/aGhoYP78+VxzzTX6YJRSKiQDJtj7Wls5+eST2JKSGPxv3+91fcZnWPPqPg5sOc3VnxnJlBuH9bwT0FRbw5vP/JDyo4eYvuh2Zt//CLZOLyHwNriofGkv7rJGEm8fhWPmYNavX8/q1atJSEjg85//vN5po5Q6LwMm2Fc+/zytBw4ybMnvsCYm9qouYwzrlu5n3ycnybs1mxkLskPar7Gmmtef/i4NVZV85jv/xqgZnV/4Be6KZir/uBdfg4uUh3LxDIvg1Vdf5fDhw0yYMIFPf/rT+nCUUuq8DYhg37x9O1V/eIHEu+8m9vrre1WXMYb1fz3InnVlTLspi/xPhfa2mOBAf8dTPyRz/MQuZVqP11P14l4QIe2xyZR5Knnjf96gtbWV2267jWnTpuktlUqpC9LvB3x9TU2cePIp7BkZpP/rv/a6vk1vH6ZgZQmT5mYy645RIQXfxppqlvUQ6FsKq6j8391Yom2kf3UKR1tO8uqrr+JwOHjssceYPn26BnqlBoDPf/7zpKenM3Fi1zjRG/0+2J9+9lncJSUMfeY/sMbG9Kqure8eZdt7x8i9dgiz7x5zXoG+sZtA37jxBFWvFGIbHHEmCXYAACAASURBVEPaV6dwoOIoS5cuJT09nUcffZT09PSz1KyU6o8eeeQRli9fHvZ6+/UwTuPH66l9bSnJjzyC46qrelXXzpXH2fT2YcbOHMQND+SE9E7Yxuoqf6CvqeaO7/6IzJyOT8IZY6h//xgNa0uIykkm+f4c9hYX8uabb5KRkcGDDz6o4/NKXSrvPQmndoe3zsGTYOFPuy1y/fXXc/To0fAelxB79iKyQESKReSgiHR5bYqIDBeRVSKyS0TWikhmIH2qiGwQkb2BvM+G+wTOxVtXx8nvfY+IUaNI++Y3elXX7rWlrH/jIKOmp3Pj58aH9Dap4EB/51NnCfQeHzXL9tOwtoSYmYNJeSiXgr27+Otf/0pWVhYPPfSQBnqlVNj02LMXESvwG+AmoBTYIiJvG2MKg4o9C7xsjHlJROYBzwAPAc3A54wxB0RkKLBNRN43xtSG/Uw6OfWTn+CprCT717/GEhl5wfUUrj/BuqX7yZ6cyk1fyA1pemJ/oH+Kxpoa7nzqR2TkdHzxgM/poerVIloP1hJ/83Di5g5j27ZtvPPOO4waNYrPfvazRESENnGaUqqP9NADv9KE0rPPBw4aYw4bY1zAUuD2TmVygdWB9TVt+caY/caYA4H1E0A5kBaOhnen/oMPqH/776R+5StET7rwixynDtex9tV9DMtN5pYvTcAaQqBvqK7sPtA3u6n43S5aD9eRdPdY4udlsWnTJt555x3Gjh3Lvffeq4FeKRV2oQT7DKAkaLs0kBasAGibCH4xECciKcEFRCQfiAAOdT6AiDwmIltFZGtFRUWobT8rT1UVp374I6ImTCD1K1++4HrcLi8rXywkNjmKBY9NxGbv+e1ODdWVvP70d2mqreHO7z7dJdAbr4+qP+/DXd5M6sO5xMwYxEcffcTy5csZP34899xzD/ZOD1gppVQ4hOtunG8DN4jIDuAGoAzwtmWKyBDgFeBRY4yv887GmCXGmDxjTF5a2oV3/I0xnPzBD/A1NjL0Zz9FehE4N/7tEHXlLcz73PiQJjVrqK5k2Y+eOhPox43v0rbatw/RerCWpDvGEDk2iTVr1rBq1SomTZrEXXfdpTNWKqW47777mDVrFsXFxWRmZvKHP/whLPWGEl3KgOC5ADIDae0CQzR3AIhILHBn27i8iMQD/wC+Z4zZGI5Gn0vdW2/RuHIV6d/5DpG9mAWybH8Nu1aXMmluZkhvl/J63Lz1nz+hua6WO7/7NEPHju9SpnH9CZo2nSJuzjAc09NZuXIl69evZ+rUqdx22206x41SCoDXXnutT+oNJcJsAcaIyAgRiQDuBd4OLiAiqSLSVtdTwAuB9Ajg//BfvH0jfM3uyn3yJKd/8h9E580g+eHPXXA9LqeH1S8XkZAWzazPjAppn49ee5nThw+w4GvfPGugbymqou4fh4mekELcTVksX76c9evXk5eXp4FeKXVR9BhljDEe4HHgfaAIWGaM2SsiT4vIbYFic4BiEdkPDAJ+Eki/B7geeEREdgaWqeE+CQBrQgKJdyxm6DPPINaex9fP5ZM3D1Ff5eTGh8djj+y5niM7trLtnf9jys23Mib/mi75rpNNVL9WjH1oLEmfHcfmLZvZtGkTV199NbfeeqsGeqXURRHSILEx5l3g3U5p/x60/gbQpedujHkVeLWXbQyJxeFg0FNP9aqOksJq9q4rY+r8YQwZ3fNkaY3VVbz3m1+SlpXNnIe+0CXf2+Ci6qW9WKKspD6cS+mpMj744APGjRvHzTffrNMfKKUuGu1WBrS2eFj9ShFJgx3MvG1kj+V9Pi/v/eYXuF2t3PrEv2LrdLukcXuperkQX5OblIcn0Cwuli1bRmJiIosXL9YevVLqotKIE/Dx6wdoqm3lxodzsUX0PHyz5a2/cnzPLuY9+mVSMjvOZW+Mofr1/bhKG0i+dxzWwdG88cYbOJ1OPvvZz+qTsUqpi06DPXB0VyX7PjnJ9FuGM2hEfI/ly4qLWL/sVcZdcz0T59zUJb9+5XFadlWSsCCb6AmprFy5kmPHjnHbbbcxaNCgvjgFpZTq1oAP9s4mN2te3UdKRgxX3drz3PTOxkb+8V8/Jz4tnZu+9PUu4+7NO8tpWHUcx4xBxF6fyd69e9mwYQNXXXUVkydP7qvTUEr1EyUlJcydO5fc3FwmTJjAr371q7DUO+Cf4lm3dD/ORjef+qcpWO3df/cZY/jgd/9FU0019z79cyIdHadMbj1WT/Ub+4kYEU/S4tFUVlby1ltvkZmZyS233NKXp6GU6idsNhu/+MUvmD59Og0NDcyYMYObbrqJ3Nzcnnfurt4wte+KdGhHOQe2nCb/0yNIGxbXY/ldK9/jwOZPuP6BRxkyelyHPE+1k6pXCrEmRJLyYC4ur5u//OUv2Gw27r77bn06VqkrzM82/4x91fvCWmdOcg7/mt/9S5SGDBnCkCFDAIiLi2P8+PGUlZX1OtgP2GGclgYXH/65mLSsOKYvGN5j+YrjR1nz0v+SPWU6eZ9a3CHP+AzVfynGeHykPjwBi8PGW2+9RVVVFXfffTcJCQl9dRpKqX7s6NGj7Nixg5kzu76v+nwNyO6mMYYP/1xMa4uH2785vsfZLN1OJ+889zMiHTEs+No3kU63TTZtOYXrWD1Jd43Fnu5gw4YNFBYWMn/+fEaMCO0dtUqpy0tPPfC+1tjYyJ133slzzz1HfHzPN470ZED27A9uK+fQjgpmfnokKUNjeyy/5qUlVJ8oZdHj3yYmseNcOd4GF3XvHSFyZAKOGekcPXqUDz74gJycHK699tq+OgWlVD/mdru58847eeCBB7jjjjt63iEEAy7Y+3yGTW8dJnVYLFNvyuqx/L5P1rF79Qfk33Ynwyd3nemh9p3DGLePxMWjaWxs5I033iA5OZnPfOYz+oSsUuq8GWP4whe+wPjx4/nWt74VtnoHXLA/tL2cuooW8hZm9/h6wabaGlYs+TVDxozjmnse7JLvLK6mpaCC+LnDsCRHsmzZMlpbW/XBKaXUBVu/fj2vvPIKq1evZurUqUydOpV333235x17MKDG7I0xbFt+jMRBDkZO7Xne/I1v/gV3q5MFX/sW1k530/hcXmr+dhBbWjRxc4axas1qSkpKuPPOO0lPT++rU1BK9XPXXXcdxpiw1zugevbH91ZTVdrI9FuykB569bWnT7Fr5XImzbuZ5KGdX8zlf0rWW9NK0uIxVNVW88knnzBlyhQmTZrUV81XSqkLNqCC/bblR4lNimRs/uAey36y7FUsFguz7ryvS57rRCONH5fiyBtE5MgE3n//fWw2G/Pnz++LZiulVK8NmGB/8mAtJw/WMXV+FlZb96ddfvQwRes/ZNqi24hN7vAqXYzPUPN/B7FE20lcNIL9+/dz4MAB5syZQ1xczw9mKaXUpTBggv22948RFWMn97qhPZZd/5dXiHQ4yL/tri55TRtP4i5pIPFTI/FFCMuXLyclJYX8/Py+aLZSSoXFgAj2laWNHNtdxeR5mT2+faq0aA+Ht28h//a7iYrteA++t66VuvePEjkmkeipaWzatInq6moWLFig0yEopS5rAyLYb3//GPZIK5PmZHZbzhjDR6+9TExSMtMWfKpLfu3bhzBeQ9Jn/PfUf/jhh4wdO5YxY8b0VdOVUiosQgr2IrJARIpF5KCIPHmW/OEiskpEdonIWhHJDMp7WEQOBJaHw9n4UNRVNHNw62kmzB5KVIy927KHt2/hRHEhs+68D3tkx/vkWwqraNlbRfyNWdhSolm5ciVer1dns1RKhZXT6SQ/P58pU6YwYcIEfvCDH4Sl3h6DvYhYgd8AC4Fc4D4R6Tz92rPAy8aYycDTwDOBfZOBHwAzgXzgByKSxEW0Y0UJYhWmzu/+aVmfz8vHr71E4uAhTJzb8YUkvlYPtW8dxDbIQdz1GZSUlFBQUMCsWbNISUk5R41KKXX+IiMjWb16NQUFBezcuZPly5ezcePGXtcbykBzPnDQGHMYQESWArcDhUFlcoG253rXAH8LrN8CrDDGVAf2XQEsAF7rdctD0FTXyr5PTpJz9RBiEiO7Lbvv4w+pLDnGrU98p8sDVPUfHMNb7yLt/vEYgffee4+4uDhmz57dl81XSl1Cp/7jP2gtCu8Ux5Hjcxj83e92W0ZEiA1cL3S73bjd7rBMvRLKME4GUBK0XRpIC1YAtM3WsxiIE5GUEPdFRB4Tka0isrWioiLUtveoYFUJPq+PaTd336v3uN2sX/Yn0rNHMe7q6zrkuUobaPzkBDEzhxA5PJ6CggJOnDjB/PnziYzs/gtEKaUuhNfrZerUqaSnp3PTTTddVlMcfxv4tYg8AqwDygBvqDsbY5YASwDy8vLC8pxwa7ObPevKGDUjncR0R7dld61cTn3Faf9rBoOmL26/pz7WTsIt2TidTlauXElmZqa+YlCpfq6nHnhfslqt7Ny5k9raWhYvXsyePXuYOHFir+oMpWdfBgwL2s4MpLUzxpwwxtxhjJkGfC+QVhvKvn1l99oy3E4v02/p/sUkrpZmNr65lGETJjN88rQOeS17K3GXNZJ460gs0TY+/PBDmpqaWLRokc5oqZTqc4mJicydO5fly5f3uq5Qgv0WYIyIjBCRCOBe4O3gAiKSKiJtdT0FvBBYfx+4WUSSAhdmbw6k9Sm3y0vB6hKyJqT0+LrBbe++RUt9HbPve7hDADfG0PBhKbbUaKInp1FRUcGmTZuYPn06Q4f2/GCWUkpdiIqKCmprawFoaWlhxYoV5OTk9LreHodxjDEeEXkcf5C2Ai8YY/aKyNPAVmPM28Ac4BkRMfiHcb4e2LdaRH6M/wsD4Om2i7V9qWj9CZyNbmb08LrB5vo6tv79TUZfNYshYzq+U7b1UB3u0kYSF48GgeXLl2O325k3b15fNl0pNcCdPHmShx9+GK/Xi8/n45577uFTn+r63M/5CmnM3hjzLvBup7R/D1p/A3jjHPu+wJmefp/zen3sWHGcIaMSGDomsduym/+2DLezlevufahLXsOHJVhi7cRMH8T+/fs5dOgQt9xyS/tVcqWU6guTJ09mx44dYa+33z1Be2DzaRqrW3t8iXh9ZTk73/8HuTfMIyWz4906rrJGWg/UEntdBl7xsXz5clJTU3X+G6XUFatfBXvjM2x//xgpGbEMn9j9w06fvP5nEOGau+/vktewrhSJtBI7cwgbNmygpqaGhQsXYrV2P6+OUkpdrvpVsD9SUEnNqWamL8jq9m6ZqtLjFH64mqk330p8ase3SnmqWmjZVUHMzCG0+FpZt24dOTk5jBo1qq+br5RSfabfBHv/KwePEp8axejp3b8WcPPfXscWGUn+Z+7uktfwURlYhLjrhrJt2zbcbrdelFVKXfH6TbCvK2+h6kQT024ejsV67tNyNjWyf+N6cmfPwRGf0CHP2+iiaetpHNPSMQ4rmzdvZtSoUfpOWaXUFa/fTMKeOMjB535yDRHR3Y+rF3+yDo/bxcS5N3fJa/zkBHh9xN2QSWFhIY2Njdx+++191WSllLpo+k3PHsARH4HN3n2w3716BWlZ2QwaObpDuq/VS+OGk0TlpmBLjWbjxo2kpqbqWL1S6qLzer1MmzYtLPfXt+lXwb4nFceOcPrwASbOu7nLBdymzacwLR7ibsikpKSEEydOMHPmTCyWAfUjUkpdBn71q18xfvz4sNbZb4ZxQrF7zQdYbTbGXzenQ7rx+Gj8uJSIEQlEZsWzcdlyoqKimDJlyqVpqFLqkvto2X4qSxrDWmfqsFhm3zO22zKlpaX84x//4Hvf+x6//OUvw3bsAdNt9bjdFH20llFXzSI6Lr5DXvPOCrx1LuLnZFJbW0tRUREzZswgIiLiErVWKTVQfeMb3+DnP/952EcVBkzP/tDWjTgbG5jU6S1UxmdoWFeCfUgMkWOTWLdiBQBXXXXVpWimUuoy0VMPvC+88847pKenM2PGDNauXRvWugdMz3736g+IS01j+KSpHdKd+6rxlLcQd0MmLpeL7du3k5ubS2Ji9/PqKKVUuK1fv563336b7Oxs7r33XlavXs2DDz4YlroHRLCvryjn2O6dTJwzv8PLSQAaPizFmhRJ9KQ0CgoKcDqdXH311ZeopUqpgeyZZ56htLSUo0ePsnTpUubNm8err74alroHRLDf++EqACbcML9DeuvROlzH6ombnYkRw6ZNm8jIyCAzM/NSNFMppfpMvw/2xudjz9qVZE2cQkL6oA55DWtLscTYcOQN4tChQ1RVVXH11VfrW6iUUpfcnDlzeOedd8JWX78P9sf37KK+4nSXC7PuU00491UTO2solggrGzduJC4ujtzc3EvUUqWU6jv9PtjvXvMBUTGxjL5qVof0hnWliN1CzKyhlJeXc+jQIfLz83UaY6VUvxRSsBeRBSJSLCIHReTJs+RnicgaEdkhIrtEZFEg3S4iL4nIbhEpEpGnwn0C3WlpbODglg3kXDcHW9A9855aJ807K4jJH4w1xs6mTZuw2WzMmDHjYjZPKaUumh6DvYhYgd8AC4Fc4D4R6TzW8X1gmTFmGv4Xkv93IP1uINIYMwmYAXxZRLLD0/Se7ft4LV63m0nzOk561vjJCQBiZ2fQ3NxMQUEBkydPxuFwXKymKaXURRVKzz4fOGiMOWyMcQFLgc5TQRqg7bHUBOBEUHqMiNiAaMAF1Pe61SHavWYF6SNGkZ498kxDfYbmnRVEjUvClhjFtm3b8Hg8erulUqpfCyXYZwAlQdulgbRgPwQeFJFS/C8m/6dA+htAE3ASOA48a4yp7nwAEXlMRLaKyNaKiorzO4NzOH34IBVHDzOp01TGrqP1+OpdOKam4fV62bx5MyNHjtQ565VS/Vq4LtDeB7xojMkEFgGviIgF/18FXmAoMAL4FxEZ2XlnY8wSY0yeMSYvLS0tLA3as3YFVrudnGtv6JDevKsCsVuIGp9CYWEhDQ0N2qtXSl1WsrOzmTRpElOnTiUvLy8sdYYyN04ZMCxoOzOQFuwLwAIAY8wGEYkCUoH7geXGGDdQLiLrgTzgcG8b3h23q5Wij9cyJv8aomJj29ON19Cyu4Ko8cntt1umpKQwevTobmpTSqmLb82aNaSmpoatvlCC/RZgjIiMwB/k78UfxIMdB24EXhSR8UAUUBFIn4e/px8DXA08F6a2n9PBzRtobWrqcmG29VAtviYPjilplJSUUFZWxqJFi3TOeqVUF2teXEL5sfD2S9OHj2TuI4+Ftc5Q9RjljDEe4HHgfaAI/103e0XkaRG5LVDsX4AviUgB8BrwiDHG4L+LJ1ZE9uL/0vijMWZXX5xIsD1rPiAhfRDDcid1SG8uqEAirUSNTWbjxo06Z71S6rIkItx8883MmDGDJUuWhKXOkKY4Nsa8i//Ca3DavwetFwLXnmW/Rvy3X140deWnOL5nF9fc80CHSc+Mx0fL3kqiJ6RQ39xAYWEhs2bNIjIy8mI2Tyl1hbhUPXCAjz/+mIyMDMrLy7npppvIycnh+uuv71Wd/W78Ys/alSDSZdIzZ3ENxunFMSWNrVu3ApCfn38pmqiUUt3KyPDf8Jiens7ixYvZvHlzr+vsV8He5/OyZ+1KsqdMJz614109zbsqsDhsRIxKYM+ePYwcOVLnrFdKXXaamppoaGhoX//ggw+YOHFir+vtV2+qOrZrJ41Vlcz93Bc7pPtcXpyFVTimp3O6opyamhquu+66S9RKpZQ6t9OnT7N48WIAPB4P999/PwsWLOh1vf0q2O9Zs4KouHhGzpjZId1ZVI1x+3BMSWN74TZEhJycnEvUSqWUOreRI0dSUFAQ9nr7zTBOc30dB7dsJHf2XGx2e8e8ggos8RHYh8dTWFhIdnY2MTExl6ilSil18fWbYG+xWrn2sw8y+caOf+74nB6cxdU4JqVSWVVJVVWVzlmvlBpw+s0wTlRMLPm339UlvWVvFXgN0VPS2Fm4HUCHcJRSA06/6dmfS3NBBdakSCKGxVFYWEhWVhZxcXGXullKKXVR9etg72100XqwBseUNKqqqigvL9chHKXUgNSvg33LnirwQfTkNAoLCwEYP378JW6VUkpdfP062DcXVGBLj8Y+JIaioiIyMzNJSEi41M1SSqlu1dbWctddd5GTk8P48ePZsGFDr+vst8HeW9eK62gdjslp1NTUcPLkSR3CUUpdEZ544gkWLFjAvn37KCgoCMuIRL+5G6ez5t2VYCB6Shqbi3YAOoSjlApd7d8P4TrRFNY6I4bGkPjpUd2WqaurY926dbz44ov+fSIiiIiI6PWx+23PvqWgAvvQGOxpDgoLCxkyZAhJSUmXullKKdWtI0eOkJaWxqOPPsq0adP44he/SFNT7790+mXP3lPtxFXSQMLCbOrq6igrK+PGG2+81M1SSl1BeuqB9xWPx8P27dt5/vnnmTlzJk888QQ//elP+fGPf9yrevtlz755l/+l5dGT0igqKgJ0CEcpdWXIzMwkMzOTmTP9c3zdddddbN++vdf19stg37KzgoisOGzJURQWFjJo0KCwvstRKaX6yuDBgxk2bBjFxcUArFq1Kiw3l/S7YRz36Sbcp5pI+PRIGhoaOH78OHPmzLnUzVJKqZA9//zzPPDAA7hcLkaOHMkf//jHXtcZUrAXkQXArwAr8HtjzE875WcBLwGJgTJPBl5liIhMBn4HxAM+4CpjjLPXLT+H5l2VIOCYnMa2op0AesulUuqKMnXq1PY36oVLj8FeRKz4Xxx+E1AKbBGRtwPvnW3zffwvIv+tiOTif19ttojYgFeBh4wxBSKSArjDegZBjDG0FFQQOTIBa1wEhYWFpKamkp6e3leHVEqpK0IoY/b5wEFjzGFjjAtYCtzeqYzB33MHSABOBNZvBnYZYwoAjDFVxhhv75t9du4TTXgqW4iekkZTUxPHjh3TXr1SShFasM8ASoK2SwNpwX4IPCgipfh79f8USB8LGBF5X0S2i8h3znYAEXlMRLaKyNaKiorzOoFgzbsqwCJET0hl3759GGM02CulFOG7G+c+4EVjTCawCHhFRCz4h4muAx4IfC4WkS43vBtjlhhj8owxeWlpaZ2zQ9I2hBM1JhFrjJ3CwkKSkpIYNGjQBZ+UUkr1F6EE+zJgWNB2ZiAt2BeAZQDGmA1AFJCK/6+AdcaYSmNMM/5e//TeNvpsvDWt+Jxeoqek0dzczJEjR8jNzUVE+uJwSil1RQkl2G8BxojICBGJAO4F3u5U5jhwI4CIjMcf7CuA94FJIuIIXKy9ASikD9iSoxj6/Zk4JqdRXFyMz+fTIRyllAroMdgbYzzA4/gDdxH+u272isjTInJboNi/AF8SkQLgNeAR41cD/BL/F8ZOYLsx5h99cSIAYrMgNguFhYUkJCQwdOjQvjqUUkr1ieLiYqZOndq+xMfH89xzz/W63pDusw/cM/9up7R/D1ovBK49x76v4r/98qJwOp0cPnyY/Px8HcJRSl1xxo0bx86d/meEvF4vGRkZLF68uNf19rsnaPfv34/X69W5cJRSvfLee+9x6tSpsNY5ePBgFi5cGHL5VatWMWrUKIYPH97rY/e7uXEKCwuJi4sjMzPzUjdFKaV6ZenSpdx3331hqatf9exbW1s5ePAg06dPx2Lpd99jSqmL6Hx64H3B5XLx9ttv88wzz4Slvn4VEQ8cOIDH49G7cJRSV7z33nuP6dOnh+1ZoX4V7IuKioiJiSErK+tSN0UppXrltddeC9sQDvSjYO9yudi/fz85OTk6hKOUuqI1NTWxYsUK7rjjjrDV2W/G7FtbWxk3bhyTJk261E1RSqleiYmJoaqqKqx19ptgHxcXx1133XWpm6GUUpclHe9QSqkBQIO9UkoFMcZc6iZ060Lbp8FeKaUCoqKiqKqqumwDvjGGqqoqoqKiznvffjNmr5RSvZWZmUlpaSm9eYlSX4uKirqgGQI02CulVIDdbmfEiBGXuhl9QodxlFJqANBgr5RSA4AGe6WUGgDkcrvqLCIVwLFeVJEKVIapOZeD/nY+0P/Oqb+dD/S/c+pv5wNdz2m4MSbtXIUvu2DfWyKy1RiTd6nbES797Xyg/51Tfzsf6H/n1N/OB87/nHQYRymlBgAN9kopNQD0x2C/5FI3IMz62/lA/zun/nY+0P/Oqb+dz/9v735CrKrDMI5/HwalGAMVRESN/iC0iLAQIZAQQUk3FoQkBLaqRYHRpnCjBkJEhTsFSVAoR1Erl7kQqo35pzHNodIYyGGcWYjobJLyaXHegcvkvcNtBk6/c98PDPec371zeF9e7juH95y5F7rMqXEz+5RSSv/WxDP7lFJKU2SzTymlHtCYZi/pRUm/SLom6f2645kNkoYlXZY0KOl83fF0S9JBSeOSrrSsLZR0WtJv8bigzhi71SanXZJGok6DkjbVGWM3JC2XdEbSVUk/S9oe60XWqUM+JdfoIUk/SLoUOe2O9cclnY2ed1TS3I7HacLMXlIf8CuwHrgBnAO22r5aa2AzJGkYWGW7yH8GkfQCMAEctv10rH0E3LL9YfxRXmD7vTrj7EabnHYBE7Y/rjO2/0LSEmCJ7YuSHgEuAC8Br1NgnTrks4VyaySg3/aEpDnA98B24F3gpO0BSfuBS7b3tTtOU87sVwPXbP9u+x4wAGyuOaaeZ/tb4NaU5c3Aodg+RPVGLEabnIple9T2xdi+CwwBSym0Th3yKZYrE7E7J34MrAOOx/q0NWpKs18K/NGyf4PCCxwMfCPpgqQ36g5mliy2PRrbN4HFdQYzi96W9FOMeYoYeUwl6THgWeAsDajTlHyg4BpJ6pM0CIwDp4HrwG3bf8VLpu15TWn2TbXG9nPARuCtGCE0hqsZYvlzRNgHPAmsBEaBT+oNp3uS5gEngHds32l9rsQ6PSCfomtk+2/bK4FlVJOMp7o9RlOa/QiwvGV/WawVzfZIPI4DX1IVuXRjMVednK+O1xzPCTivnwAAAThJREFUjNkeizfjfeAAhdUp5sAngM9tn4zlYuv0oHxKr9Ek27eBM8DzwHxJk19ANW3Pa0qzPwesiKvTc4FXgVM1xzQjkvrjAhOS+oENwJXOv1WEU8C22N4GfF1jLLNisimGlymoTnHx7zNgyPanLU8VWad2+RReo0WS5sf2w1Q3ogxRNf1X4mXT1qgRd+MAxK1Ue4E+4KDtPTWHNCOSnqA6m4fq6yO/KC0nSUeAtVQfxToG7AS+Ao4Bj1J9lPUW28Vc8GyT01qq8YCBYeDNlnn3/5qkNcB3wGXgfizvoJpzF1enDvlspdwaPUN1AbaP6gT9mO0PokcMAAuBH4HXbP/Z9jhNafYppZTaa8oYJ6WUUgfZ7FNKqQdks08ppR6QzT6llHpANvuUUuoB2exTSqkHZLNPKaUe8A+iHEgR+OJx/gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "net.storage\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "W-gSp6KzIUT6",
        "outputId": "5803cc88-c25e-4cf4-b405-425f1116405a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <div>\n",
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              "    .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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>bus</th>\n",
              "      <th>p_mw</th>\n",
              "      <th>q_mvar</th>\n",
              "      <th>sn_mva</th>\n",
              "      <th>soc_percent</th>\n",
              "      <th>min_e_mwh</th>\n",
              "      <th>max_e_mwh</th>\n",
              "      <th>scaling</th>\n",
              "      <th>in_service</th>\n",
              "      <th>type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>storage0</td>\n",
              "      <td>3</td>\n",
              "      <td>33.922378</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>True</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>storage1</td>\n",
              "      <td>4</td>\n",
              "      <td>-11.391009</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>True</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>storage2</td>\n",
              "      <td>5</td>\n",
              "      <td>-12.985853</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>True</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>storage3</td>\n",
              "      <td>6</td>\n",
              "      <td>-14.580649</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>True</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>storage4</td>\n",
              "      <td>7</td>\n",
              "      <td>-16.176435</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>True</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-483bdce1-4e55-43b9-9ba5-8620574aaa4c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-483bdce1-4e55-43b9-9ba5-8620574aaa4c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       name  bus       p_mw  q_mvar  ...  max_e_mwh  scaling  in_service  type\n",
              "0  storage0    3  33.922378     0.0  ...     1000.0    0.001        True  None\n",
              "1  storage1    4 -11.391009     0.0  ...     1000.0    0.001        True  None\n",
              "2  storage2    5 -12.985853     0.0  ...     1000.0    0.001        True  None\n",
              "3  storage3    6 -14.580649     0.0  ...     1000.0    0.001        True  None\n",
              "4  storage4    7 -16.176435     0.0  ...     1000.0    0.001        True  None\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "net.res_bus"
      ],
      "metadata": {
        "id": "TFs65PLNVPxe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# now with Qs... requires negative reactive power\n",
        "# Example only with active load\n",
        "net = create_net(5, f_l=0.66)\n",
        "pp.runpp(net)\n",
        "\n",
        "# Dataframe to save each iteration\n",
        "bus_res=pd.DataFrame()\n",
        "bus_res[0]=net.res_bus[\"vm_pu\"]\n",
        "k=1\n",
        "converge = False\n",
        "while not converge:\n",
        "  jacobian = net._ppc[\"internal\"][\"J\"].todense()\n",
        "  list_v = [0]*(len(net.res_bus[\"vm_pu\"])-1)\n",
        "\n",
        "  for v in net.res_bus[\"vm_pu\"][1:]:  # 0 is always the slack\n",
        "      if v < 0.95:\n",
        "          dv = (0.95 - v)*400\n",
        "      elif v > 1.05:\n",
        "          dv = (1.05 - v)*400\n",
        "      else:\n",
        "          dv = 0\n",
        "      list_v.append(dv)\n",
        "\n",
        "  deltas_mx = np.dot(jacobian, list_v)\n",
        "  for i in net.storage.index:\n",
        "    # here a limit can be implemented\n",
        "    lim = 200\n",
        "    cos_phi = 0.95\n",
        "    tan_phi=(1-cos_phi**2)**0.5/cos_phi\n",
        "    if net.storage.at[i,\"p_mw\"]-deltas_mx[0,i+2]>lim:\n",
        "      net.storage.at[i,\"p_mw\"]=lim\n",
        "    \n",
        "    elif net.storage.at[i,\"p_mw\"]-deltas_mx[0,i+2]<-lim:\n",
        "      net.storage.at[i,\"p_mw\"]=-lim\n",
        "    else:\n",
        "      net.storage.at[i,\"p_mw\"]-=deltas_mx[0,i+2]\n",
        "    \n",
        "    # What is the sign of the reactive power in the node?\n",
        "    net.storage.at[i,\"q_mvar\"]-=deltas_mx[0,i+2+7]\n",
        "    if abs(net.storage.at[i,\"q_mvar\"])>abs(net.storage.at[i,\"p_mw\"]*tan_phi):\n",
        "      net.storage.at[i,\"q_mvar\"] = net.storage.at[i,\"p_mw\"]*tan_phi\n",
        "  pp.runpp(net)\n",
        "  net.res_bus\n",
        "  bus_res[k]=net.res_bus[\"vm_pu\"]\n",
        "\n",
        "  if min(bus_res)>0.95 and max(bus_res)<1.05:\n",
        "      converge = True\n",
        "  \n",
        "  if not converge:\n",
        "    diff =bus_res[k]-bus_res[k-1]\n",
        "    if max(diff)<1/40000:\n",
        "      converge = True\n",
        "    else:\n",
        "      k+=1\n",
        "  \n",
        "\n",
        "bus_res.T.plot()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "0vsSk-lRJK4L",
        "outputId": "22913c2b-8535-4292-d39d-ca93265734dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6c9dbfb110>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "net.storage\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ssw_HK2gKvLb",
        "outputId": "73e058ff-2d84-4160-c72a-5fcc42e89b28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>bus</th>\n",
              "      <th>p_mw</th>\n",
              "      <th>q_mvar</th>\n",
              "      <th>sn_mva</th>\n",
              "      <th>soc_percent</th>\n",
              "      <th>min_e_mwh</th>\n",
              "      <th>max_e_mwh</th>\n",
              "      <th>scaling</th>\n",
              "      <th>in_service</th>\n",
              "      <th>type</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>storage0</td>\n",
              "      <td>3</td>\n",
              "      <td>23.598981</td>\n",
              "      <td>7.756610</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>True</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>storage1</td>\n",
              "      <td>4</td>\n",
              "      <td>-8.851095</td>\n",
              "      <td>-2.898463</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>True</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>storage2</td>\n",
              "      <td>5</td>\n",
              "      <td>-10.011803</td>\n",
              "      <td>-3.290720</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>True</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>storage3</td>\n",
              "      <td>6</td>\n",
              "      <td>-11.166236</td>\n",
              "      <td>-3.670164</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>True</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>storage4</td>\n",
              "      <td>7</td>\n",
              "      <td>-12.347432</td>\n",
              "      <td>-4.058404</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>True</td>\n",
              "      <td>None</td>\n",
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              "          document.querySelector('#df-44aa3b86-e0a3-4f61-95d6-d6724314d31b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-44aa3b86-e0a3-4f61-95d6-d6724314d31b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       name  bus       p_mw    q_mvar  ...  max_e_mwh  scaling  in_service  type\n",
              "0  storage0    3  23.598981  7.756610  ...     1000.0    0.001        True  None\n",
              "1  storage1    4  -8.851095 -2.898463  ...     1000.0    0.001        True  None\n",
              "2  storage2    5 -10.011803 -3.290720  ...     1000.0    0.001        True  None\n",
              "3  storage3    6 -11.166236 -3.670164  ...     1000.0    0.001        True  None\n",
              "4  storage4    7 -12.347432 -4.058404  ...     1000.0    0.001        True  None\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "net.res_bus"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "hnSm8r7dKxgC",
        "outputId": "b7dfd730-8e68-44da-853b-9a446a43c3a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>vm_pu</th>\n",
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              "      <th>2</th>\n",
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              "      <td>0.000000</td>\n",
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              "    <tr>\n",
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              "      <td>0.002549</td>\n",
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              "      <td>0.002988</td>\n",
              "      <td>-0.003291</td>\n",
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              "      <td>0.003434</td>\n",
              "      <td>-0.003670</td>\n",
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              "    <tr>\n",
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              "      <td>-3.593850</td>\n",
              "      <td>0.003853</td>\n",
              "      <td>-0.004058</td>\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bb09f754-f2ef-40e2-b13b-454f6d3603ca button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bb09f754-f2ef-40e2-b13b-454f6d3603ca');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      vm_pu  va_degree      p_mw    q_mvar\n",
              "0  1.000000   0.000000 -0.050108  0.004597\n",
              "1  0.999920  -0.002504  0.000000  0.000000\n",
              "2  0.998716  -0.279798  0.000000  0.000000\n",
              "3  0.950016  -1.914045  0.033399  0.007757\n",
              "4  0.949985  -2.547148  0.002549 -0.002898\n",
              "5  0.949970  -3.051224  0.002988 -0.003291\n",
              "6  0.949957  -3.406617  0.003434 -0.003670\n",
              "7  0.949949  -3.593850  0.003853 -0.004058"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "jFgdRYrrSh1r"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}