tutorials/pandamodels_reactive power optimization.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Reactive Power Optimization with PandaModels.jl\n",
    "\n",
    "This tutorial describes how to solve some optimization problems regarding reactive power redispatch using **PandaModels**. The reactive power provision of generators, e.g., net.sgen, can be optimized for  \n",
    "\n",
    "1) maintaining voltage setpoints  \n",
    "2) maintaining reactive power setpoints  \n",
    "3) (active) power loss reduction \\\n",
    "4) branch loading reduction\n",
    "\n",
    "\n",
    "For more details on the corresponding models, see:\n",
    "* [maintaining voltage setpoints](https://github.com/e2nIEE/PandaModels.jl/blob/develop/src/models/vstab.jl)\n",
    "* [maintaining reactive power setpoints](https://github.com/e2nIEE/PandaModels.jl/blob/develop/src/models/qflex.jl)\n",
    "* [power loss reduction](https://github.com/e2nIEE/PandaModels.jl/blob/develop/src/models/ploss.jl)\n",
    "* [branch loading reduction](https://github.com/e2nIEE/PandaModels.jl/blob/develop/src/models/loading.jl)\n",
    "\n",
    "**Note**:\n",
    "\n",
    "- **PandaModels** is still in the development stage, and the developed models for reactive power optimizations are tested with limited grids. It is possible that errors may arise when facing different grids.\n",
    "- It is suggested to use **net.sn_mva=1.0** to start the optimization. Using other sn_mva values may introduce not converged OPF  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Maintaining Voltage Setpoints\n",
    "\n",
    "Grid operators want to maintain a constant voltage profile across all or a part of the system buses. Reactive power optimization, in this case, helps them identify the preferable provision of reactive power from generators. The objective is to minimize the deviation of the voltages of certain buses (here, buses of DER) from a setpoint, e.g.,$V_{\\rm setpoint}\n",
    "= 1.03 p.u.$ As a result, the reactive power setpoint for each generator is calculated. The mathematical formulation of this objective function can have the following forms:\n",
    "\n",
    "\\begin{align}\n",
    "& \\underset{\\mathcal{X} = [q, ...]}{\\text{minimize}}\n",
    "& &  \\sum_{i\\in \\mathcal{BI}} (V_i-V_{\\rm setpoint})^2 \\\\\n",
    "& \\text{subject to}\n",
    "& & g(\\mathcal{X})=0 \\\\\n",
    "& & & h(\\mathcal{X}) \\leq 0\n",
    "\\end{align}\n",
    "\n",
    "where $V_{i}$ is the voltage variable of bus $i$ in $\\mathcal{BI}$ which denotes the set of buses of DER. The $g(\\mathcal{X})$ and $h(\\mathcal{X})$, denote equality and inequality constraints, respectively. The $\\mathcal{X}$ denotes the set of variables decisions, such as reactive power $q$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-1: Create test grid\n",
    "\n",
    "How does it work? Let's first create a cigre grid with DERs(distribution energy resource) from the pandapower's network database. After running the power flow calculation, we can see that there are nine DERs (net.sgen) in the grid, and they don't provide reactive power for normal operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>p_mw</th>\n",
       "      <th>q_mvar</th>\n",
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      "text/plain": [
       "   p_mw  q_mvar\n",
       "0  0.02     0.0\n",
       "1  0.02     0.0\n",
       "2  0.03     0.0\n",
       "3  0.03     0.0\n",
       "4  0.03     0.0\n",
       "5  0.03     0.0\n",
       "6  0.04     0.0\n",
       "7  0.01     0.0\n",
       "8  1.50     0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandapower as pp\n",
    "import pandapower.networks as nw\n",
    "from copy import deepcopy\n",
    "\n",
    "net = nw.create_cigre_network_mv(with_der=\"pv_wind\")\n",
    "pp.runpp(net) # run power flow calculation\n",
    "net_org = deepcopy(net) # copy of the grid for further comparison\n",
    "display(net.res_sgen) # show power flow results for DER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(You can use the pandapower plotting module to visualize the grid model.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandapower.plotting as plot\n",
    "%matplotlib inline\n",
    "plot.simple_plot(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 1-2: Parametrization for optimization\n",
    "Before starting the optimization, we need to configure some parameters. \n",
    "In PandaModels, the uncontrollable loads and DER (net.sgen) are converted to load, and the controllable power elements are converted to generators. Accordingly, we neet to set loads as uncontrollable and set DER (net.sgen) as controllable elements. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "net_opt = deepcopy(net) # copy of the grid for further comparison \n",
    "net_opt.load['controllable'] = False\n",
    "net_opt.sgen['controllable'] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Now, let's set the constraints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# lower and upper bounds for buses\n",
    "net_opt.bus[\"max_vm_pu\"] = 1.1\n",
    "net_opt.bus[\"min_vm_pu\"] = 0.9\n",
    "\n",
    "# lower and upper bounds for external grid\n",
    "net_opt.ext_grid[\"max_q_mvar\"] = 10000.0\n",
    "net_opt.ext_grid[\"min_q_mvar\"] = -10000.0\n",
    "net_opt.ext_grid[\"max_p_mw\"] = 10000.0\n",
    "net_opt.ext_grid[\"min_p_mw\"] = -10000.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We keep the active power for DER constant to the calculated value from the power flow and define a constraint for the reactive power. (In this grid, there is no generator (net.gen))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>name</th>\n",
       "      <th>bus</th>\n",
       "      <th>p_mw</th>\n",
       "      <th>q_mvar</th>\n",
       "      <th>sn_mva</th>\n",
       "      <th>scaling</th>\n",
       "      <th>in_service</th>\n",
       "      <th>type</th>\n",
       "      <th>current_source</th>\n",
       "      <th>controllable</th>\n",
       "      <th>max_p_mw</th>\n",
       "      <th>min_p_mw</th>\n",
       "      <th>max_q_mvar</th>\n",
       "      <th>min_q_mvar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PV 3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00656</td>\n",
       "      <td>-0.00656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PV 4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00656</td>\n",
       "      <td>-0.00656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PV 5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00984</td>\n",
       "      <td>-0.00984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PV 6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00984</td>\n",
       "      <td>-0.00984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PV 8</td>\n",
       "      <td>8</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00984</td>\n",
       "      <td>-0.00984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PV 9</td>\n",
       "      <td>9</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00984</td>\n",
       "      <td>-0.00984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PV 10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01312</td>\n",
       "      <td>-0.01312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PV 11</td>\n",
       "      <td>11</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00328</td>\n",
       "      <td>-0.00328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WKA 7</td>\n",
       "      <td>7</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>WP</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.49200</td>\n",
       "      <td>-0.49200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  bus  p_mw  q_mvar  sn_mva  scaling  in_service type  current_source  \\\n",
       "0   PV 3    3  0.02     0.0    0.02      1.0        True   PV            True   \n",
       "1   PV 4    4  0.02     0.0    0.02      1.0        True   PV            True   \n",
       "2   PV 5    5  0.03     0.0    0.03      1.0        True   PV            True   \n",
       "3   PV 6    6  0.03     0.0    0.03      1.0        True   PV            True   \n",
       "4   PV 8    8  0.03     0.0    0.03      1.0        True   PV            True   \n",
       "5   PV 9    9  0.03     0.0    0.03      1.0        True   PV            True   \n",
       "6  PV 10   10  0.04     0.0    0.04      1.0        True   PV            True   \n",
       "7  PV 11   11  0.01     0.0    0.01      1.0        True   PV            True   \n",
       "8  WKA 7    7  1.50     0.0    1.50      1.0        True   WP            True   \n",
       "\n",
       "   controllable  max_p_mw  min_p_mw  max_q_mvar  min_q_mvar  \n",
       "0          True      0.02      0.02     0.00656    -0.00656  \n",
       "1          True      0.02      0.02     0.00656    -0.00656  \n",
       "2          True      0.03      0.03     0.00984    -0.00984  \n",
       "3          True      0.03      0.03     0.00984    -0.00984  \n",
       "4          True      0.03      0.03     0.00984    -0.00984  \n",
       "5          True      0.03      0.03     0.00984    -0.00984  \n",
       "6          True      0.04      0.04     0.01312    -0.01312  \n",
       "7          True      0.01      0.01     0.00328    -0.00328  \n",
       "8          True      1.50      1.50     0.49200    -0.49200  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lower and upper bounds for DERs\n",
    "net_opt.sgen[\"max_p_mw\"] = net_opt.sgen.p_mw.values\n",
    "net_opt.sgen[\"min_p_mw\"] = net_opt.sgen.p_mw.values\n",
    "net_opt.sgen[\"max_q_mvar\"] = net_opt.sgen.p_mw.values * 0.328\n",
    "net_opt.sgen[\"min_q_mvar\"] = -net_opt.sgen.p_mw.values * 0.328\n",
    "display(net_opt.sgen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Subsequently, let's set a high upper bound for lines and transformers to avoid congestion issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# lower and upper bounds for lines\n",
    "net_opt.trafo[\"max_loading_percent\"] = 100.0\n",
    "net_opt.line[\"max_loading_percent\"] = 100.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Finally, we need to let PandaModels know which buses are of interest and what is user-defined voltage setpoint. To do this, we can add extra column called **\"pm_param/setpoint_v\"** in net.bus and set values for buses connecting DER (net.sgen)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "net_opt.bus[\"pm_param/setpoint_v\"] = None # add extra column\n",
    "net_opt.bus[\"pm_param/setpoint_v\"].loc[net_opt.sgen.bus] = 0.96 # voltage setpoints for theses buses are 0.96"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-3: Start optimization\n",
    "Now let's call the function ***pandapower.runpm_vstab()*** to start the optimizaiton for maintaining voltage setpoints. \n",
    "(Note that the first time the function is called, Julia is started in the background, which may take some time.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 2.29 seconds:\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    pp.runpm_vstab(net_opt)\n",
    "except Exception as err:\n",
    "    print(err)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Also, the following parameters and options can be modified as inputs while calling the optimization from PandaModles. For this case, we can keep the default values.\n",
    "\n",
    "| parameter | description | type | default |\n",
    "| :--- | :--- | :---: | :--- |\n",
    "| correct_pm_network_data | checks if network data is correct. If not tries to correct it | bool | True |\n",
    "| silence | Suppresses information and warning messages output by PowerModels | bool | True |\n",
    "| pm_model | PowerModels.jl model to use | str | \"ACPPowerModel\" |\n",
    "| pm_solver | \"main\" solver| str | \"ipopt\" |\n",
    "| pm_mip_solver | mixed integer solver| str | \"cbc\" |\n",
    "| pm_nl_solver | nonlinear solver| str | \"ipopt\" |\n",
    "| pm_tol | default desired convergence tolerance for solver to use | float | 1e-8 |\n",
    "| pm_log_level | solver log level in power models | int | 0 |\n",
    "| delete_buffer_file | If True, the .json file used by PandaModels will be deleted after optimization. | bool | True |\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-4: Results comparison\n",
    "Let's check the results for voltage and reactive power at buses connecting DER before and after the optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     0.951848\n",
       "4     0.950094\n",
       "5     0.948893\n",
       "6     0.947488\n",
       "8     0.949187\n",
       "9     0.948264\n",
       "10    0.947100\n",
       "11    0.946916\n",
       "7     0.952199\n",
       "Name: vm_pu, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- deviation from setpionts v=0.96 ---\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0010976683852842502"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# results from power flow\n",
    "display(net_org.res_bus.vm_pu[net_org.sgen.bus])\n",
    "v_org = net_org.res_bus.vm_pu[net_org.sgen.bus].values\n",
    "print(\"--- deviation from setpionts v=0.96 ---\")\n",
    "display(sum((v_org-0.96)**2)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     0.961792\n",
       "4     0.960087\n",
       "5     0.958920\n",
       "6     0.957559\n",
       "8     0.960321\n",
       "9     0.959425\n",
       "10    0.958299\n",
       "11    0.958119\n",
       "7     0.964668\n",
       "Name: vm_pu, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- deviation from setpionts v=0.96 ---\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.900054027594424e-05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# results from optimization\n",
    "display(net_opt.res_bus.vm_pu[net.sgen.bus])\n",
    "v_opt = net_opt.res_bus.vm_pu[net_opt.sgen.bus].values\n",
    "print(\"--- deviation from setpionts v=0.96 ---\")\n",
    "display(sum((v_opt-0.96)**2)) # devition from setpoints v=0.96 p.u."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As exected, the bus voltages after optimization are close to the defined setpoints. It is caused by the provision of reactive power of DER."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p_mw</th>\n",
       "      <th>q_mvar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.02</td>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   p_mw  q_mvar\n",
       "0  0.02     0.0\n",
       "1  0.02     0.0\n",
       "2  0.03     0.0\n",
       "3  0.03     0.0\n",
       "4  0.03     0.0\n",
       "5  0.03     0.0\n",
       "6  0.04     0.0\n",
       "7  0.01     0.0\n",
       "8  1.50     0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# results from power flow\n",
    "display(net_org.res_sgen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p_mw</th>\n",
       "      <th>q_mvar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.006482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.006497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.009787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.009799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.009752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.009765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.013063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.003228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0.441400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   p_mw    q_mvar\n",
       "0  0.02  0.006482\n",
       "1  0.02  0.006497\n",
       "2  0.03  0.009787\n",
       "3  0.03  0.009799\n",
       "4  0.03  0.009752\n",
       "5  0.03  0.009765\n",
       "6  0.04  0.013063\n",
       "7  0.01  0.003228\n",
       "8  1.50  0.441400"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# results from optimization\n",
    "display(net_opt.res_sgen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-5: Saving reactive power with multi-objectives\n",
    "In some situations, users want to improve the voltage profiles with limited reactive power provisions. For this, the objective function is modified by minimizing the reactive power provision. As the formulation below shows, the two sub-objectives for **maintaining voltage setpoints** and **saving reactive power provisions** are connected through coefficients $c_{\\rm v}$ and $c_{\\rm q}$. \n",
    "\n",
    "\\begin{align}\n",
    "& \\underset{\\mathcal{X} = [q, ...]}{\\text{minimize}}\n",
    "& &  \\sum_{i\\in \\mathcal{BI}} c_{\\rm v}*(V_i-V_{\\rm setpoint})^2 + c_{\\rm q}*(q_i-0)^2 \\\\\n",
    "\\end{align}\n",
    "\n",
    "We can call the objective function with different coefficient combinations, e.g., $c_{\\rm v}=0.99$ and $c_{\\rm q}=0.01$.\n",
    "(In default, $c_{\\rm v}=1.0$ and $c_{\\rm q}=0.0$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.06 seconds:\n"
     ]
    }
   ],
   "source": [
    "net_optlim = deepcopy(net_opt)\n",
    "net_optlim[\"obj_factors\"] = [0.99, 0.01] # set coefficients for the multi-objective function\n",
    "try:\n",
    "    pp.runpm_vstab(net_optlim)\n",
    "except:\n",
    "    print(\"[WinError 3] The system cannot find the path specified to the python39.dll\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the results below show, with $c_{\\rm v}=0.99$ and $c_{\\rm q}=0.01$, the provided reactive powers are reduced, and the corresponding voltages are increased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- optimized reactive power without limitation Cv = 1.0, Cq = 0. ----\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>p_mw</th>\n",
       "      <th>q_mvar</th>\n",
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       "      <td>0.006497</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.009787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.009799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.009752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.009765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.013063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.003228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0.441400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   p_mw    q_mvar\n",
       "0  0.02  0.006482\n",
       "1  0.02  0.006497\n",
       "2  0.03  0.009787\n",
       "3  0.03  0.009799\n",
       "4  0.03  0.009752\n",
       "5  0.03  0.009765\n",
       "6  0.04  0.013063\n",
       "7  0.01  0.003228\n",
       "8  1.50  0.441400"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- optimized reactive power with limitation Cv = 0.99, Cq = 0.01 ----\n"
     ]
    },
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       "      <th>q_mvar</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.006559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.006559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.009839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.009839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.009839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.009839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.013119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.003279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0.127064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   p_mw    q_mvar\n",
       "0  0.02  0.006559\n",
       "1  0.02  0.006559\n",
       "2  0.03  0.009839\n",
       "3  0.03  0.009839\n",
       "4  0.03  0.009839\n",
       "5  0.03  0.009839\n",
       "6  0.04  0.013119\n",
       "7  0.01  0.003279\n",
       "8  1.50  0.127064"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- deviation from setpoints without limitation Cv = 1.0, Cq = 0. ----\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.900054027594424e-05"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- deviation from setpoints with limitation Cv = 0.99, Cq = 0.01 ----\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.00043781176800306845"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print (\"--- optimized reactive power without limitation Cv = 1.0, Cq = 0. ----\")\n",
    "display(net_opt.res_sgen) # show optimized reactive power without limitation\n",
    "print (\"--- optimized reactive power with limitation Cv = 0.99, Cq = 0.01 ----\")\n",
    "display(net_optlim.res_sgen) # show optimized reactive power with limitation\n",
    "\n",
    "print (\"--- deviation from setpoints without limitation Cv = 1.0, Cq = 0. ----\")\n",
    "display(sum((v_opt - 0.96) ** 2)) # show optimized reactive power without limitation\n",
    "print (\"--- deviation from setpoints with limitation Cv = 0.99, Cq = 0.01 ----\")\n",
    "v_optlim = net_optlim.res_bus.vm_pu[net_optlim.sgen.bus].values\n",
    "display(sum((v_optlim - 0.96) ** 2)) # show optimized bus voltages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coefficient combination is grid-dependent. For example, the picture below shows the result for one of our case studies with a realistic grid. The user can find the most suitable combination setting through several attempts.\n",
    "<img src=\"pics/coef_combination.png\" alt=\"ALT\" width=\"600\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-6: Time series optimizations\n",
    "There are two ways to perform time series optimizations. \n",
    "The first one is optimization in a loop, i.e., updating the grid condition for each time step and repeatedly calling optimization. The second one uses the \"multi-timestep\" optimization. PowerModels can solve the multi-grid optimization problem. This feature is used in PandaModels to deal with time series optimization for on specific grid by replacing the \"multi-grid\" with \"multi-timestep\" of the grid. \n",
    "\n",
    "Next, you will see an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's load the time series data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestep</th>\n",
       "      <th>pv</th>\n",
       "      <th>wind</th>\n",
       "      <th>residential</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.006143</td>\n",
       "      <td>0.622600</td>\n",
       "      <td>0.256022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.005877</td>\n",
       "      <td>0.623585</td>\n",
       "      <td>0.248835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.005505</td>\n",
       "      <td>0.625056</td>\n",
       "      <td>0.211973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.005293</td>\n",
       "      <td>0.625897</td>\n",
       "      <td>0.201825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.004921</td>\n",
       "      <td>0.627369</td>\n",
       "      <td>0.209164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.004619</td>\n",
       "      <td>0.645138</td>\n",
       "      <td>0.198600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.004286</td>\n",
       "      <td>0.685128</td>\n",
       "      <td>0.189050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.003929</td>\n",
       "      <td>0.713267</td>\n",
       "      <td>0.186924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.003674</td>\n",
       "      <td>0.719497</td>\n",
       "      <td>0.187007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.003392</td>\n",
       "      <td>0.686113</td>\n",
       "      <td>0.183658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.003029</td>\n",
       "      <td>0.650800</td>\n",
       "      <td>0.181048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.002747</td>\n",
       "      <td>0.630733</td>\n",
       "      <td>0.185567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.002571</td>\n",
       "      <td>0.639400</td>\n",
       "      <td>0.182282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.002286</td>\n",
       "      <td>0.682600</td>\n",
       "      <td>0.192295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.001995</td>\n",
       "      <td>0.728373</td>\n",
       "      <td>0.199360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.001733</td>\n",
       "      <td>0.777240</td>\n",
       "      <td>0.182287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.001491</td>\n",
       "      <td>0.807959</td>\n",
       "      <td>0.186546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.001260</td>\n",
       "      <td>0.749338</td>\n",
       "      <td>0.192088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.001056</td>\n",
       "      <td>0.699656</td>\n",
       "      <td>0.203122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.660600</td>\n",
       "      <td>0.228030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>0.629356</td>\n",
       "      <td>0.244633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.000531</td>\n",
       "      <td>0.597195</td>\n",
       "      <td>0.240875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.564118</td>\n",
       "      <td>0.262490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.540456</td>\n",
       "      <td>0.331813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.535359</td>\n",
       "      <td>0.436350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.553113</td>\n",
       "      <td>0.501545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.567908</td>\n",
       "      <td>0.526120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.586867</td>\n",
       "      <td>0.537150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.607229</td>\n",
       "      <td>0.614585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.588314</td>\n",
       "      <td>0.631053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.575705</td>\n",
       "      <td>0.608927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>0.000653</td>\n",
       "      <td>0.552257</td>\n",
       "      <td>0.607860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>0.001034</td>\n",
       "      <td>0.531790</td>\n",
       "      <td>0.624715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>0.001706</td>\n",
       "      <td>0.551367</td>\n",
       "      <td>0.670312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>0.002437</td>\n",
       "      <td>0.572300</td>\n",
       "      <td>0.690940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>0.022095</td>\n",
       "      <td>0.603033</td>\n",
       "      <td>0.640480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>0.053810</td>\n",
       "      <td>0.620800</td>\n",
       "      <td>0.639200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>0.123429</td>\n",
       "      <td>0.630467</td>\n",
       "      <td>0.636695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>0.146629</td>\n",
       "      <td>0.637587</td>\n",
       "      <td>0.652735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>0.319167</td>\n",
       "      <td>0.645700</td>\n",
       "      <td>0.656870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>0.430714</td>\n",
       "      <td>0.627600</td>\n",
       "      <td>0.678112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>0.531333</td>\n",
       "      <td>0.591889</td>\n",
       "      <td>0.648757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>0.588714</td>\n",
       "      <td>0.565567</td>\n",
       "      <td>0.611575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>0.653937</td>\n",
       "      <td>0.533422</td>\n",
       "      <td>0.629150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>0.589571</td>\n",
       "      <td>0.533267</td>\n",
       "      <td>0.604570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>0.569302</td>\n",
       "      <td>0.589044</td>\n",
       "      <td>0.614053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>0.423933</td>\n",
       "      <td>0.784267</td>\n",
       "      <td>0.702965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>0.311917</td>\n",
       "      <td>0.923333</td>\n",
       "      <td>0.726462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>0.335000</td>\n",
       "      <td>1.005217</td>\n",
       "      <td>0.727100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>0.442314</td>\n",
       "      <td>1.011813</td>\n",
       "      <td>0.730600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>50</td>\n",
       "      <td>0.468524</td>\n",
       "      <td>0.979733</td>\n",
       "      <td>0.718670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>51</td>\n",
       "      <td>0.483262</td>\n",
       "      <td>0.941833</td>\n",
       "      <td>0.694337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>52</td>\n",
       "      <td>0.454295</td>\n",
       "      <td>0.901480</td>\n",
       "      <td>0.650027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>53</td>\n",
       "      <td>0.438943</td>\n",
       "      <td>0.878333</td>\n",
       "      <td>0.632864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>54</td>\n",
       "      <td>0.242460</td>\n",
       "      <td>0.838356</td>\n",
       "      <td>0.570937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>55</td>\n",
       "      <td>0.148248</td>\n",
       "      <td>0.816720</td>\n",
       "      <td>0.564928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>56</td>\n",
       "      <td>0.180262</td>\n",
       "      <td>0.797300</td>\n",
       "      <td>0.565415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>57</td>\n",
       "      <td>0.139762</td>\n",
       "      <td>0.788044</td>\n",
       "      <td>0.523873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>58</td>\n",
       "      <td>0.108638</td>\n",
       "      <td>0.788400</td>\n",
       "      <td>0.492570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>59</td>\n",
       "      <td>0.106751</td>\n",
       "      <td>0.793954</td>\n",
       "      <td>0.493360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>60</td>\n",
       "      <td>0.109315</td>\n",
       "      <td>0.802856</td>\n",
       "      <td>0.494898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>61</td>\n",
       "      <td>0.092679</td>\n",
       "      <td>0.808650</td>\n",
       "      <td>0.481850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>62</td>\n",
       "      <td>0.056399</td>\n",
       "      <td>0.812608</td>\n",
       "      <td>0.463050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>63</td>\n",
       "      <td>0.035314</td>\n",
       "      <td>0.812680</td>\n",
       "      <td>0.449260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>64</td>\n",
       "      <td>0.025476</td>\n",
       "      <td>0.809433</td>\n",
       "      <td>0.458250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>65</td>\n",
       "      <td>0.023270</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.508357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>66</td>\n",
       "      <td>0.022214</td>\n",
       "      <td>0.707667</td>\n",
       "      <td>0.517530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>67</td>\n",
       "      <td>0.021159</td>\n",
       "      <td>0.652778</td>\n",
       "      <td>0.574923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>68</td>\n",
       "      <td>0.020365</td>\n",
       "      <td>0.611889</td>\n",
       "      <td>0.631390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>69</td>\n",
       "      <td>0.019571</td>\n",
       "      <td>0.571000</td>\n",
       "      <td>0.695165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>70</td>\n",
       "      <td>0.018976</td>\n",
       "      <td>0.540333</td>\n",
       "      <td>0.723720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>71</td>\n",
       "      <td>0.017587</td>\n",
       "      <td>0.468778</td>\n",
       "      <td>0.776485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>72</td>\n",
       "      <td>0.016833</td>\n",
       "      <td>0.488950</td>\n",
       "      <td>0.812940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>73</td>\n",
       "      <td>0.016238</td>\n",
       "      <td>0.556644</td>\n",
       "      <td>0.859579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>74</td>\n",
       "      <td>0.015452</td>\n",
       "      <td>0.661067</td>\n",
       "      <td>0.868860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>75</td>\n",
       "      <td>0.014833</td>\n",
       "      <td>0.761600</td>\n",
       "      <td>0.902426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>76</td>\n",
       "      <td>0.014184</td>\n",
       "      <td>0.754000</td>\n",
       "      <td>0.914777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>77</td>\n",
       "      <td>0.013844</td>\n",
       "      <td>0.696133</td>\n",
       "      <td>0.904512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>78</td>\n",
       "      <td>0.013103</td>\n",
       "      <td>0.582000</td>\n",
       "      <td>0.884404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>79</td>\n",
       "      <td>0.012528</td>\n",
       "      <td>0.513667</td>\n",
       "      <td>0.829733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>80</td>\n",
       "      <td>0.011836</td>\n",
       "      <td>0.463541</td>\n",
       "      <td>0.820933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>81</td>\n",
       "      <td>0.011370</td>\n",
       "      <td>0.536370</td>\n",
       "      <td>0.795723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>82</td>\n",
       "      <td>0.010671</td>\n",
       "      <td>0.647515</td>\n",
       "      <td>0.738732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>83</td>\n",
       "      <td>0.010087</td>\n",
       "      <td>0.743303</td>\n",
       "      <td>0.700090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>84</td>\n",
       "      <td>0.009736</td>\n",
       "      <td>0.800776</td>\n",
       "      <td>0.689043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>85</td>\n",
       "      <td>0.009086</td>\n",
       "      <td>0.850387</td>\n",
       "      <td>0.677730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>86</td>\n",
       "      <td>0.008686</td>\n",
       "      <td>0.873227</td>\n",
       "      <td>0.646210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>87</td>\n",
       "      <td>0.008152</td>\n",
       "      <td>0.903680</td>\n",
       "      <td>0.613804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>88</td>\n",
       "      <td>0.007345</td>\n",
       "      <td>0.932173</td>\n",
       "      <td>0.540327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>89</td>\n",
       "      <td>0.006796</td>\n",
       "      <td>0.928251</td>\n",
       "      <td>0.499037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>90</td>\n",
       "      <td>0.006384</td>\n",
       "      <td>0.925310</td>\n",
       "      <td>0.469209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>91</td>\n",
       "      <td>0.005835</td>\n",
       "      <td>0.921388</td>\n",
       "      <td>0.435660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>92</td>\n",
       "      <td>0.005423</td>\n",
       "      <td>0.918447</td>\n",
       "      <td>0.410870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>93</td>\n",
       "      <td>0.004704</td>\n",
       "      <td>0.886837</td>\n",
       "      <td>0.344715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>94</td>\n",
       "      <td>0.004143</td>\n",
       "      <td>0.858339</td>\n",
       "      <td>0.304026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>95</td>\n",
       "      <td>0.003667</td>\n",
       "      <td>0.838370</td>\n",
       "      <td>0.286328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    timestep        pv      wind  residential\n",
       "0          0  0.006143  0.622600     0.256022\n",
       "1          1  0.005877  0.623585     0.248835\n",
       "2          2  0.005505  0.625056     0.211973\n",
       "3          3  0.005293  0.625897     0.201825\n",
       "4          4  0.004921  0.627369     0.209164\n",
       "5          5  0.004619  0.645138     0.198600\n",
       "6          6  0.004286  0.685128     0.189050\n",
       "7          7  0.003929  0.713267     0.186924\n",
       "8          8  0.003674  0.719497     0.187007\n",
       "9          9  0.003392  0.686113     0.183658\n",
       "10        10  0.003029  0.650800     0.181048\n",
       "11        11  0.002747  0.630733     0.185567\n",
       "12        12  0.002571  0.639400     0.182282\n",
       "13        13  0.002286  0.682600     0.192295\n",
       "14        14  0.001995  0.728373     0.199360\n",
       "15        15  0.001733  0.777240     0.182287\n",
       "16        16  0.001491  0.807959     0.186546\n",
       "17        17  0.001260  0.749338     0.192088\n",
       "18        18  0.001056  0.699656     0.203122\n",
       "19        19  0.000857  0.660600     0.228030\n",
       "20        20  0.000698  0.629356     0.244633\n",
       "21        21  0.000531  0.597195     0.240875\n",
       "22        22  0.000355  0.564118     0.262490\n",
       "23        23  0.000222  0.540456     0.331813\n",
       "24        24  0.000132  0.535359     0.436350\n",
       "25        25  0.000066  0.553113     0.501545\n",
       "26        26  0.000011  0.567908     0.526120\n",
       "27        27  0.000000  0.586867     0.537150\n",
       "28        28  0.000061  0.607229     0.614585\n",
       "29        29  0.000245  0.588314     0.631053\n",
       "30        30  0.000367  0.575705     0.608927\n",
       "31        31  0.000653  0.552257     0.607860\n",
       "32        32  0.001034  0.531790     0.624715\n",
       "33        33  0.001706  0.551367     0.670312\n",
       "34        34  0.002437  0.572300     0.690940\n",
       "35        35  0.022095  0.603033     0.640480\n",
       "36        36  0.053810  0.620800     0.639200\n",
       "37        37  0.123429  0.630467     0.636695\n",
       "38        38  0.146629  0.637587     0.652735\n",
       "39        39  0.319167  0.645700     0.656870\n",
       "40        40  0.430714  0.627600     0.678112\n",
       "41        41  0.531333  0.591889     0.648757\n",
       "42        42  0.588714  0.565567     0.611575\n",
       "43        43  0.653937  0.533422     0.629150\n",
       "44        44  0.589571  0.533267     0.604570\n",
       "45        45  0.569302  0.589044     0.614053\n",
       "46        46  0.423933  0.784267     0.702965\n",
       "47        47  0.311917  0.923333     0.726462\n",
       "48        48  0.335000  1.005217     0.727100\n",
       "49        49  0.442314  1.011813     0.730600\n",
       "50        50  0.468524  0.979733     0.718670\n",
       "51        51  0.483262  0.941833     0.694337\n",
       "52        52  0.454295  0.901480     0.650027\n",
       "53        53  0.438943  0.878333     0.632864\n",
       "54        54  0.242460  0.838356     0.570937\n",
       "55        55  0.148248  0.816720     0.564928\n",
       "56        56  0.180262  0.797300     0.565415\n",
       "57        57  0.139762  0.788044     0.523873\n",
       "58        58  0.108638  0.788400     0.492570\n",
       "59        59  0.106751  0.793954     0.493360\n",
       "60        60  0.109315  0.802856     0.494898\n",
       "61        61  0.092679  0.808650     0.481850\n",
       "62        62  0.056399  0.812608     0.463050\n",
       "63        63  0.035314  0.812680     0.449260\n",
       "64        64  0.025476  0.809433     0.458250\n",
       "65        65  0.023270  0.750000     0.508357\n",
       "66        66  0.022214  0.707667     0.517530\n",
       "67        67  0.021159  0.652778     0.574923\n",
       "68        68  0.020365  0.611889     0.631390\n",
       "69        69  0.019571  0.571000     0.695165\n",
       "70        70  0.018976  0.540333     0.723720\n",
       "71        71  0.017587  0.468778     0.776485\n",
       "72        72  0.016833  0.488950     0.812940\n",
       "73        73  0.016238  0.556644     0.859579\n",
       "74        74  0.015452  0.661067     0.868860\n",
       "75        75  0.014833  0.761600     0.902426\n",
       "76        76  0.014184  0.754000     0.914777\n",
       "77        77  0.013844  0.696133     0.904512\n",
       "78        78  0.013103  0.582000     0.884404\n",
       "79        79  0.012528  0.513667     0.829733\n",
       "80        80  0.011836  0.463541     0.820933\n",
       "81        81  0.011370  0.536370     0.795723\n",
       "82        82  0.010671  0.647515     0.738732\n",
       "83        83  0.010087  0.743303     0.700090\n",
       "84        84  0.009736  0.800776     0.689043\n",
       "85        85  0.009086  0.850387     0.677730\n",
       "86        86  0.008686  0.873227     0.646210\n",
       "87        87  0.008152  0.903680     0.613804\n",
       "88        88  0.007345  0.932173     0.540327\n",
       "89        89  0.006796  0.928251     0.499037\n",
       "90        90  0.006384  0.925310     0.469209\n",
       "91        91  0.005835  0.921388     0.435660\n",
       "92        92  0.005423  0.918447     0.410870\n",
       "93        93  0.004704  0.886837     0.344715\n",
       "94        94  0.004143  0.858339     0.304026\n",
       "95        95  0.003667  0.838370     0.286328"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# load time series data for 96 time steps\n",
    "time_series = pd.read_json(\"cigre_timeseries_15min.json\")\n",
    "time_series.sort_index(inplace=True)\n",
    "\n",
    "# show time series\n",
    "display(time_series) \n",
    "\n",
    "# set load type and sgen type in the cigre grid\n",
    "net[\"load\"].loc[:, \"type\"] = \"residential\"\n",
    "net.sgen.loc[:, \"type\"] = \"pv\"\n",
    "net.sgen.loc[8, \"type\"] = \"wind\"\n",
    "\n",
    "# get rated power\n",
    "load_p = net[\"load\"].loc[:, \"p_mw\"].values\n",
    "sgen_p = net[\"sgen\"].loc[:7, \"p_mw\"].values\n",
    "wind_p = net[\"sgen\"].loc[8, \"p_mw\"]\n",
    "\n",
    "# create dataframe for loads and sgens\n",
    "load_ts = pd.DataFrame(index=time_series.index.tolist(), columns=net.load.index.tolist())\n",
    "sgen_ts = pd.DataFrame(index=time_series.index.tolist(), columns=net.sgen.index.tolist())\n",
    "for t in range(96):\n",
    "    load_ts.loc[t] = load_p * time_series.at[t, \"residential\"]\n",
    "    sgen_ts.loc[t][:8] = sgen_p * time_series.at[t, \"pv\"]\n",
    "    sgen_ts.loc[t][8] = wind_p * time_series.at[t, \"wind\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we perform the time series optimizations in a loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.02 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.02 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.02 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.06 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.06 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.06 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.06 seconds:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.05 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.04 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.02 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_vstab') is finished in 0.03 seconds:\n"
     ]
    }
   ],
   "source": [
    "########################################################################\n",
    "####################### Optimization in a loop #########################\n",
    "########################################################################\n",
    "\n",
    "y_loop = []\n",
    "for t in range(96):\n",
    "    \n",
    "    # Update operating points for each time step\n",
    "    net_opt.load.p_mw = load_ts.loc[t]\n",
    "    net_opt.sgen.p_mw = sgen_ts.loc[t]\n",
    "    \n",
    "    # If required, we also need to update the constraints, e.g., Qmax- and Qmin-value, for each time step. \n",
    "    net_opt.sgen.max_p_mw = sgen_ts.loc[t]   # for load: max_p_mw == min_p_mw, because they are not controllable.\n",
    "    net_opt.sgen.min_p_mw = sgen_ts.loc[t]\n",
    "    net_opt.sgen.max_q_mvar = net_opt.sgen.max_q_mvar\n",
    "    net_opt.sgen.min_q_mvar = net_opt.sgen.min_q_mvar\n",
    "    \n",
    "    # run optimization for time step t\n",
    "    try:\n",
    "        pp.runpm_vstab(net_opt)\n",
    "    except:\n",
    "        print(\"[WinError 3] The system cannot find the path specified to the python39.dll\")\n",
    "    \n",
    "    # save the mean voltage value for each time step\n",
    "    y_loop.append(net_opt.res_bus.vm_pu[net_opt.sgen.bus].values.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can do it using multi-timestep optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_multi_vstab') is finished in 5.87 seconds:\n"
     ]
    }
   ],
   "source": [
    "########################################################################\n",
    "####################### multi-timestep optimization ####################\n",
    "########################################################################\n",
    "\n",
    "from pandapower.control import ConstControl\n",
    "from pandapower.timeseries import DFData\n",
    "\n",
    "# For this method, we need to add the time series data into the pandapower.controller.\n",
    "net_opt.controller.drop(net.controller.index, inplace=True) # remove the existing controllers\n",
    "\n",
    "# Add time series for load and sgen.\n",
    "ConstControl(net_opt, element=\"load\", variable=\"p_mw\",\n",
    "             element_index=net.load.index.tolist(), profile_name=net.load.index.tolist(),\n",
    "             data_source=DFData(load_ts))\n",
    "ConstControl(net_opt, element=\"sgen\", variable=\"p_mw\",\n",
    "             element_index=net.sgen.index.tolist(), profile_name=net.sgen.index.tolist(),\n",
    "             data_source=DFData(sgen_ts))\n",
    "\n",
    "# If required, we also need to creat a time series for the constraints, e.g., Qmax- and Qmin-value, for each time step. \n",
    "df_qmax, df_qmin = sgen_ts.copy(), sgen_ts.copy()\n",
    "df_qmax[df_qmax.columns] = net_opt.sgen.max_q_mvar\n",
    "df_qmin[df_qmin.columns] = net_opt.sgen.min_q_mvar\n",
    "\n",
    "# Then create controllers for them.\n",
    "ConstControl(net_opt, element=\"sgen\", variable=\"max_p_mw\",\n",
    "              element_index=net.sgen.index.tolist(), profile_name=net.sgen.index.tolist(),\n",
    "              data_source=DFData(sgen_ts))\n",
    "ConstControl(net_opt, element=\"sgen\", variable=\"min_p_mw\",\n",
    "              element_index=net.sgen.index.tolist(), profile_name=net.sgen.index.tolist(),\n",
    "              data_source=DFData(sgen_ts))\n",
    "ConstControl(net_opt, element=\"sgen\", variable=\"max_q_mvar\",\n",
    "              element_index=net.sgen.index.tolist(), profile_name=net.sgen.index.tolist(),\n",
    "              data_source=DFData(df_qmax))\n",
    "ConstControl(net_opt, element=\"sgen\", variable=\"min_q_mvar\",\n",
    "              element_index=net.sgen.index.tolist(), profile_name=net.sgen.index.tolist(),\n",
    "              data_source=DFData(df_qmin))\n",
    "\n",
    "# For the multi-timestep optimization, we need to call another PandaModels-function.\n",
    "try:\n",
    "    pp.runpm_multi_vstab(net_opt, from_time_step=0, to_time_step=96)\n",
    "except:\n",
    "    print(\"[WinError 3] The system cannot find the path specified to the python39.dll\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note that**, currently, the function ***pp.runpm_multi_vstab()*** doesn't support using of the multi-objective function for saving reactive power.\n",
    "\n",
    "The results for each time step are saved in **net.res_ts_opt**. Let's get them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_multi = []\n",
    "for t in range(96):\n",
    "    y_multi.append(net_opt.res_ts_opt[str(t)].res_bus.vm_pu[net_opt.sgen.bus].values.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mean voltage value for DER-buses [p.u.]')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)\n",
    "ax1.plot(range(96), y_loop)\n",
    "ax1.set_title(\"results for \\n optimization in a loop\")\n",
    "ax2.plot(range(96), y_multi)\n",
    "ax2.set_title(\"results for \\n multi-timestep optimization\")\n",
    "\n",
    "for ax in ax1, ax2:\n",
    "    ax.grid(True)\n",
    "    ax.label_outer()\n",
    "    ax.set_xlabel(\"time step [15min]\")\n",
    "ax1.set_ylabel(\"mean voltage value for DER-buses [p.u.]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the plot above shows, both methods have the same results.\n",
    "\n",
    "**For a long-term time series optimization (>500 timesteps), we suggest using the first method, optimization in a loop.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Maintaining Reactive Power Setpoints\n",
    "\n",
    "The motivation for this objective is to minimize the deviations of reactive power injection at the TSO-DSO interface from a target value, e.g., $Q_{\\rm setpoint}= 2 \\rm MVar$. It is formulated by the following equation:\n",
    "\n",
    "\\begin{align}\n",
    "& \\underset{\\mathcal{X} = [q, ...]}{\\text{minimize}}\n",
    "& &  \\sum_{i\\in \\mathcal{interfaces}} (Q_i-Q_{\\rm setpoint})^2 \\\\\n",
    "\\end{align}\n",
    "\n",
    "We use the same grid model to introduce the example. As the picture low shows, the marked bus (or interface) is observed.\n",
    "\n",
    "\n",
    "<img src=\"pics/qflex.png\" alt=\"ALT\" width=\"300\">\n",
    "\n",
    "First, let's create the grid and check the reactive power injection at this interface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5.941540318632734"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandapower as pp\n",
    "import pandapower.networks as nw\n",
    "from copy import deepcopy\n",
    "\n",
    "net = nw.create_cigre_network_mv(with_der=\"pv_wind\")\n",
    "pp.runpp(net)\n",
    "net_org = deepcopy(net)\n",
    "\n",
    "display(net.res_trafo.q_lv_mvar[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without optimization, the reactive power injection at this interface is -5.942 MVAr.\n",
    "\n",
    "Let's configure optimization parameters and define a target value for this interface, $Q_{\\rm setpoint}= -5 \\rm MVAr$, i.e., we optimize the reactive power provision of DER to achieve this target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>name</th>\n",
       "      <th>bus</th>\n",
       "      <th>p_mw</th>\n",
       "      <th>q_mvar</th>\n",
       "      <th>sn_mva</th>\n",
       "      <th>scaling</th>\n",
       "      <th>in_service</th>\n",
       "      <th>type</th>\n",
       "      <th>current_source</th>\n",
       "      <th>controllable</th>\n",
       "      <th>max_p_mw</th>\n",
       "      <th>min_p_mw</th>\n",
       "      <th>max_q_mvar</th>\n",
       "      <th>min_q_mvar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PV 3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00656</td>\n",
       "      <td>-0.00656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PV 4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00656</td>\n",
       "      <td>-0.00656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PV 5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00984</td>\n",
       "      <td>-0.00984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PV 6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00984</td>\n",
       "      <td>-0.00984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PV 8</td>\n",
       "      <td>8</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00984</td>\n",
       "      <td>-0.00984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PV 9</td>\n",
       "      <td>9</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00984</td>\n",
       "      <td>-0.00984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PV 10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01312</td>\n",
       "      <td>-0.01312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PV 11</td>\n",
       "      <td>11</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>PV</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00328</td>\n",
       "      <td>-0.00328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WKA 7</td>\n",
       "      <td>7</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>WP</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.49200</td>\n",
       "      <td>-0.49200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  bus  p_mw  q_mvar  sn_mva  scaling  in_service type  current_source  \\\n",
       "0   PV 3    3  0.02     0.0    0.02      1.0        True   PV            True   \n",
       "1   PV 4    4  0.02     0.0    0.02      1.0        True   PV            True   \n",
       "2   PV 5    5  0.03     0.0    0.03      1.0        True   PV            True   \n",
       "3   PV 6    6  0.03     0.0    0.03      1.0        True   PV            True   \n",
       "4   PV 8    8  0.03     0.0    0.03      1.0        True   PV            True   \n",
       "5   PV 9    9  0.03     0.0    0.03      1.0        True   PV            True   \n",
       "6  PV 10   10  0.04     0.0    0.04      1.0        True   PV            True   \n",
       "7  PV 11   11  0.01     0.0    0.01      1.0        True   PV            True   \n",
       "8  WKA 7    7  1.50     0.0    1.50      1.0        True   WP            True   \n",
       "\n",
       "   controllable  max_p_mw  min_p_mw  max_q_mvar  min_q_mvar  \n",
       "0          True      0.02      0.02     0.00656    -0.00656  \n",
       "1          True      0.02      0.02     0.00656    -0.00656  \n",
       "2          True      0.03      0.03     0.00984    -0.00984  \n",
       "3          True      0.03      0.03     0.00984    -0.00984  \n",
       "4          True      0.03      0.03     0.00984    -0.00984  \n",
       "5          True      0.03      0.03     0.00984    -0.00984  \n",
       "6          True      0.04      0.04     0.01312    -0.01312  \n",
       "7          True      0.01      0.01     0.00328    -0.00328  \n",
       "8          True      1.50      1.50     0.49200    -0.49200  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net_opt = deepcopy(net) # copy of the grid for further comparison \n",
    "net_opt.load['controllable'] = False\n",
    "net_opt.sgen['controllable'] = True\n",
    "\n",
    "# lower and upper bounds for buses\n",
    "net_opt.bus[\"max_vm_pu\"] = 1.1\n",
    "net_opt.bus[\"min_vm_pu\"] = 0.9\n",
    "\n",
    "# lower and upper bounds for external grid\n",
    "net_opt.ext_grid[\"max_q_mvar\"] = 10000.0\n",
    "net_opt.ext_grid[\"min_q_mvar\"] = -10000.0\n",
    "net_opt.ext_grid[\"max_p_mw\"] = 10000.0\n",
    "net_opt.ext_grid[\"min_p_mw\"] = -10000.0\n",
    "\n",
    "# lower and upper bounds for DERs\n",
    "net_opt.sgen[\"max_p_mw\"] = net_opt.sgen.p_mw.values\n",
    "net_opt.sgen[\"min_p_mw\"] = net_opt.sgen.p_mw.values\n",
    "net_opt.sgen[\"max_q_mvar\"] = net_opt.sgen.p_mw.values * 0.328\n",
    "net_opt.sgen[\"min_q_mvar\"] = -net_opt.sgen.p_mw.values * 0.328\n",
    "display(net_opt.sgen)\n",
    "\n",
    "# lower and upper bounds for lines\n",
    "net_opt.trafo[\"max_loading_percent\"] = 100.0\n",
    "net_opt.line[\"max_loading_percent\"] = 100.0\n",
    "\n",
    "######################################################\n",
    "####################### Attention ####################\n",
    "######################################################\n",
    "\n",
    "# We need to let PandaModels know which interface is of interest and what is user-defined setpoint. \n",
    "# So, we need to add extra column called **\"pm_param/setpoint_q\"** in net.trafo and set target value. \n",
    "\n",
    "net_opt.trafo[\"pm_param/setpoint_q\"] = None # add extra column\n",
    "net_opt.trafo[\"pm_param/setpoint_q\"][0] = -5 #  reactive power setpoint for trafo 0 is -5 MVAR\n",
    "\n",
    "# Also, we need to tell PandaModels which side is observed.\n",
    "net_opt.trafo[\"pm_param/side\"] = None\n",
    "net_opt.trafo[\"pm_param/side\"][0] = \"lv\" # the observed side is the low-voltage side\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After parametrization, we can start the optimization using ***pp.runpm_qflex()***."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_qflex') is finished in 0.03 seconds:\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    pp.runpm_qflex(net_opt)\n",
    "except:\n",
    "    print(\"[WinError 3] The system cannot find the path specified to the python39.dll\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the optimization results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5.356245826038442"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>p_mw</th>\n",
       "      <th>q_mvar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0.49200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   p_mw   q_mvar\n",
       "0  0.02  0.00656\n",
       "1  0.02  0.00656\n",
       "2  0.03  0.00984\n",
       "3  0.03  0.00984\n",
       "4  0.03  0.00984\n",
       "5  0.03  0.00984\n",
       "6  0.04  0.01312\n",
       "7  0.01  0.00328\n",
       "8  1.50  0.49200"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net_opt.res_trafo.q_lv_mvar[0])  # show Q-injection at the interface after optimization\n",
    "display(net_opt.res_sgen) # show optimized reactiv power provision of DER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the results above, we can see that the Q-injection at the interface is closer to the setpoint after the optimization but not achieved. The reason is that the Q-maximum is limited. We can modify the Q-maximum and try again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_qflex') is finished in 0.02 seconds:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-5.0000000238509115"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>p_mw</th>\n",
       "      <th>q_mvar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.001931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.001945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.004184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.004198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.004140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.004168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.006959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0.881054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   p_mw    q_mvar\n",
       "0  0.02  0.001931\n",
       "1  0.02  0.001945\n",
       "2  0.03  0.004184\n",
       "3  0.03  0.004198\n",
       "4  0.03  0.004140\n",
       "5  0.03  0.004168\n",
       "6  0.04  0.006959\n",
       "7  0.01  0.000483\n",
       "8  1.50  0.881054"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net_opt.sgen[\"max_q_mvar\"] = net_opt.sgen.p_mw.values * 0.628\n",
    "net_opt.sgen[\"min_q_mvar\"] = -net_opt.sgen.p_mw.values * 0.628\n",
    "try:\n",
    "    pp.runpm_qflex(net_opt)\n",
    "except:\n",
    "    print(\"[WinError 3] The system cannot find the path specified to the python39.dll\")\n",
    "display(net_opt.res_trafo.q_lv_mvar[0])  # show Q-injection at the interface after optimization\n",
    "display(net_opt.res_sgen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the Q-injection at the interface is optimized to -5 MVAr. \n",
    "\n",
    "Similar to the **maintaining voltage setpoints**, if you are interested in time series optimizations, you can do it through a loop or use ***pp.runpm_multi_qflex()***."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Power Loss Reduction and Branch Loading Reduction\n",
    "\n",
    "The corresponding objective functions are formulated as follows: \n",
    "\n",
    "- loss reduction\n",
    "\\begin{align}\n",
    "& \\underset{\\mathcal{X} = [q, ...]}{\\text{minimize}}\n",
    "& &  \\sum_{(i, j)\\in \\mathcal{N}} (S_{ij}+S_{ji})^2 \\\\\n",
    "\\end{align}\n",
    "\n",
    "\n",
    "- loading reduction\n",
    "\\begin{align}\n",
    "& \\underset{\\mathcal{X} = [q, ...]}{\\text{minimize}}\n",
    "& &  \\sum_{(i, j)\\in \\mathcal{N}} (i_{i->j, target}-0)^2 \\\\\n",
    "\\end{align}\n",
    "\n",
    "We still use the cigre medium voltage grid to introduce the case studies. First, let's create the grid, add constraints, and define the observed branches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandapower as pp\n",
    "import pandapower.networks as nw\n",
    "from copy import deepcopy\n",
    "\n",
    "net = nw.create_cigre_network_mv(with_der=\"pv_wind\")\n",
    "net.load['controllable'] = False\n",
    "net.sgen['controllable'] = True\n",
    "net.sgen[\"max_p_mw\"] = net.sgen.p_mw.values\n",
    "net.sgen[\"min_p_mw\"] = net.sgen.p_mw.values\n",
    "net.sgen[\"max_q_mvar\"] = net.sgen.p_mw.values * 0.328\n",
    "net.sgen[\"min_q_mvar\"] = -net.sgen.p_mw.values * 0.328\n",
    "net.bus[\"max_vm_pu\"] = 1.1\n",
    "net.bus[\"min_vm_pu\"] = 0.9\n",
    "net.ext_grid[\"max_q_mvar\"] = 10000.0\n",
    "net.ext_grid[\"min_q_mvar\"] = -10000.0\n",
    "net.ext_grid[\"max_p_mw\"] = 10000.0\n",
    "net.ext_grid[\"min_p_mw\"] = -10000.0\n",
    "net.trafo[\"max_loading_percent\"] = 100.0\n",
    "net.line[\"max_loading_percent\"] = 100.0\n",
    "\n",
    "# add an additional column named \"pm_param/target_branch\" to let PandaModels know the observed branches.\n",
    "net.line[\"pm_param/target_branch\"] = True  \n",
    "net.switch.loc[:, \"closed\"] = True\n",
    "\n",
    "# run power flow calculation\n",
    "pp.runpp(net)\n",
    "net_loss = deepcopy(net)\n",
    "net_loading = deepcopy(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you see, the power flow results without optimization are stored in the grid ***net***, and the grids ***net_loss*** and ***net_loading*** are going to used for optimization. Now we call the functions ***pp.runpm_ploss*** and ***pp.runpm_loading*** to reduce the power loss and line loading percent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_ploss') is finished in 2.19 seconds:\n",
      "hp.pandapower.opf.make_objective - WARNING: no costs are given - overall generated power is minimized\n",
      "hp.pandapower.opf.run_pandamodels - INFO: Optimization ('run_pandamodels_loading') is finished in 0.07 seconds:\n"
     ]
    }
   ],
   "source": [
    "pp.runpm_ploss(net_loss)\n",
    "pp.runpm_loading(net_loading)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss without optimization: 0.070711475427468\n",
      "loss with optimization: 0.06241656797117738\n",
      "++++++++++++++++++++++++++++++++++++++++++++++\n",
      "loading without optimization: 260.54100720602156\n",
      "loading with optimization: 247.549588132589\n"
     ]
    }
   ],
   "source": [
    "print(\"loss without optimization:\", net.res_line.pl_mw.values.sum())\n",
    "print(\"loss with optimization:\", net_loss.res_line.pl_mw.values.sum())\n",
    "print(\"++++++++++++++++++++++++++++++++++++++++++++++\")\n",
    "print(\"loading without optimization:\", net.res_line.loading_percent.values.sum())\n",
    "print(\"loading with optimization:\", net_loading.res_line.loading_percent.values.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, we can reduce loss or loading percent of transformers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "no costs are given - overall generated power is minimized\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss without optimization: 0.06354567913302844\n",
      "loss with optimization: 0.0629529981193997\n"
     ]
    }
   ],
   "source": [
    "net_loss.line.drop(columns=[\"pm_param/target_branch\"], inplace=True)\n",
    "net_loss.trafo[\"pm_param/target_branch\"] = True\n",
    "pp.runpm_ploss(net_loss)\n",
    "print(\"loss without optimization:\", net.res_trafo.pl_mw.values.sum())\n",
    "print(\"loss with optimization:\", net_loss.res_trafo.pl_mw.values.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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