{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "8rOoC1NVjRcT" }, "source": [ "# Simple Time Series Example" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "bSEcFiPijRcb" }, "source": [ "This tutorial shows how a simple time series simulation is performed with the timeseries and control module in pandapower. A time series calculation requires the minimum following inputs:\n", "* pandapower net\n", "* the time series (in a pandas Dataframe for example)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "YquzqXkKjRcd" }, "source": [ "First we need some imports. Specific for this example are:\n", "* ConstControl -> \"constant\" controllers, which change the P and Q values of sgens and loads\n", "* DFData -> The Dataframe Datasource. This Dataframe holds the time series to be calculated\n", "* OutputWriter -> The output writer, which is required to write the outputs to the hard disk\n", "* run_timeseries -> the \"main\" time series function, which basically calls the controller functions (to update the P, Q of the ConstControllers) and runpp." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", "id": "Dx9ZKvbfjRcg" }, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "import tempfile\n", "\n", "import pandapower as pp\n", "from pandapower.timeseries import DFData\n", "from pandapower.timeseries import OutputWriter\n", "from pandapower.timeseries.run_time_series import run_timeseries\n", "from pandapower.control import ConstControl" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "NJj7q_srjRcs" }, "source": [ "First we look at the time series example function. It follows these steps:\n", "1. create a simple test net\n", "2. create the datasource (which contains the time series P values)\n", "3. create the controllers to update the P values of the load and the sgen\n", "4. define the output writer and desired variables to be saved\n", "5. call the main time series function to calculate the desired results" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": {}, "colab_type": "code", "id": "E5i2ezcDjRcw" }, "outputs": [], "source": [ "def timeseries_example(output_dir):\n", " # 1. create test net\n", " net = simple_test_net()\n", "\n", " # 2. create (random) data source\n", " n_timesteps = 24\n", " profiles, ds = create_data_source(n_timesteps)\n", " # 3. create controllers (to control P values of the load and the sgen)\n", " create_controllers(net, ds)\n", "\n", " # time steps to be calculated. Could also be a list with non-consecutive time steps\n", " time_steps = range(0, n_timesteps)\n", "\n", " # 4. the output writer with the desired results to be stored to files.\n", " ow = create_output_writer(net, time_steps, output_dir=output_dir)\n", "\n", " # 5. the main time series function\n", " run_timeseries(net, time_steps)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "xhzr8EE5jRc3" }, "source": [ "We start by creating a simple example pandapower net consisting of five buses, a transformer, three lines, a load and a sgen. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": {}, "colab_type": "code", "id": "x8hpgqhQjRc5" }, "outputs": [], "source": [ "def simple_test_net():\n", " \"\"\"\n", " simple net that looks like:\n", "\n", " ext_grid b0---b1 trafo(110/20) b2----b3 load\n", " |\n", " |\n", " b4 sgen\n", " \"\"\"\n", " net = pp.create_empty_network()\n", " pp.set_user_pf_options(net, init_vm_pu = \"flat\", init_va_degree = \"dc\", calculate_voltage_angles=True)\n", "\n", " b0 = pp.create_bus(net, 110)\n", " b1 = pp.create_bus(net, 110)\n", " b2 = pp.create_bus(net, 20)\n", " b3 = pp.create_bus(net, 20)\n", " b4 = pp.create_bus(net, 20)\n", "\n", " pp.create_ext_grid(net, b0)\n", " pp.create_line(net, b0, b1, 10, \"149-AL1/24-ST1A 110.0\")\n", " pp.create_transformer(net, b1, b2, \"25 MVA 110/20 kV\", name='tr1')\n", " pp.create_line(net, b2, b3, 10, \"184-AL1/30-ST1A 20.0\")\n", " pp.create_line(net, b2, b4, 10, \"184-AL1/30-ST1A 20.0\")\n", "\n", " pp.create_load(net, b3, p_mw=20., q_mvar=10., name='load1')\n", " pp.create_sgen(net, b4, p_mw=20., q_mvar=0.15, name='sgen1')\n", "\n", " return net" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "9CcyC2mgjRc_" }, "source": [ "The data source is a simple pandas DataFrame. It contains random values for the load and the sgen P values (\"profiles\"). Of course your time series values should be loaded from a file later on.\n", "Note that the profiles are identified by their column name (\"load1_p\", \"sgen1_p\"). You can choose here whatever you prefer.\n", "The DFData(profiles) converts the Dataframe to the required format for the controllers. Note that the controller" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": {}, "colab_type": "code", "id": "KVrcajmqjRdB" }, "outputs": [], "source": [ "def create_data_source(n_timesteps=24):\n", " profiles = pd.DataFrame()\n", " profiles['load1_p'] = np.random.random(n_timesteps) * 20.\n", " profiles['sgen1_p'] = np.random.random(n_timesteps) * 20.\n", "\n", " ds = DFData(profiles)\n", "\n", " return profiles, ds" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "_29FDhugjRdJ" }, "source": [ "create the controllers by telling the function which element_index belongs to which profile. In this case we map:\n", "* first load in dataframe (element_index=[0]) to the profile_name \"load1_p\"\n", "* first sgen in dataframe (element_index=[0]) to the profile_name \"sgen1_p\"" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": {}, "colab_type": "code", "id": "C7jNYZYhjRdN" }, "outputs": [], "source": [ "def create_controllers(net, ds):\n", " ConstControl(net, element='load', variable='p_mw', element_index=[0],\n", " data_source=ds, profile_name=[\"load1_p\"])\n", " ConstControl(net, element='sgen', variable='p_mw', element_index=[0],\n", " data_source=ds, profile_name=[\"sgen1_p\"])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "kDA6xHDWjRdT" }, "source": [ "create the output writer. Instead of saving the whole net (which takes a lot of time), we extract only pre defined outputs.\n", "In this case we:\n", "* save the results to \"../timeseries/tests/outputs\" \n", "* write the results to \".xls\" Excel files. (Possible are: .json, .p, .csv)\n", "* log the variables \"p_mw\" from \"res_load\", \"vm_pu\" from \"res_bus\" and two res_line values." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": {}, "colab_type": "code", "id": "GlttGzEHjRdV" }, "outputs": [], "source": [ "def create_output_writer(net, time_steps, output_dir):\n", " ow = OutputWriter(net, time_steps, output_path=output_dir, output_file_type=\".xlsx\", log_variables=list())\n", " # these variables are saved to the harddisk after / during the time series loop\n", " ow.log_variable('res_load', 'p_mw')\n", " ow.log_variable('res_bus', 'vm_pu')\n", " ow.log_variable('res_line', 'loading_percent')\n", " ow.log_variable('res_line', 'i_ka')\n", " return ow" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "DAIvq_bUjRdb" }, "source": [ "Now lets execute the code." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": {}, "colab_type": "code", "id": "wTlSALR0jRdd", "outputId": "959014ba-8db7-412b-d425-f2af199207f0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Results can be found in your local temp folder: C:\\Users\\ssnigdha\\AppData\\Local\\Temp\\time_series_example\n", "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n", "\n" ] } ], "source": [ "output_dir = os.path.join(tempfile.gettempdir(), \"time_series_example\")\n", "print(\"Results can be found in your local temp folder: {}\".format(output_dir))\n", "if not os.path.exists(output_dir):\n", " os.mkdir(output_dir)\n", "timeseries_example(output_dir)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "v8-ZYJFujRdk" }, "source": [ "If everything works you should have the desired results the output_folder, which is the temporary folder of your operating system (see print statement above)." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "EzTC_NnRjRdn" }, "source": [ "## Plot the result\n", "Let's read the result from the disk and plot it" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": {}, "colab_type": "code", "id": "DhVd7-3ejRdp", "outputId": "101d40f1-78fc-485d-eac9-70b90baf3abf" }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline \n", "\n", "# voltage results\n", "vm_pu_file = os.path.join(output_dir, \"res_bus\", \"vm_pu.xlsx\")\n", "vm_pu = pd.read_excel(vm_pu_file, index_col=0)\n", "vm_pu.plot(label=\"vm_pu\")\n", "plt.xlabel(\"time step\")\n", "plt.ylabel(\"voltage mag. [p.u.]\")\n", "plt.title(\"Voltage Magnitude\")\n", "plt.grid()\n", "plt.show()\n", "\n", "# line loading results\n", "ll_file = os.path.join(output_dir, \"res_line\", \"loading_percent.xlsx\")\n", "line_loading = pd.read_excel(ll_file, index_col=0)\n", "line_loading.plot(label=\"line_loading\")\n", "plt.xlabel(\"time step\")\n", "plt.ylabel(\"line loading [%]\")\n", "plt.title(\"Line Loading\")\n", "plt.grid()\n", "plt.show()\n", "\n", "# load results\n", "load_file = os.path.join(output_dir, \"res_load\", \"p_mw.xlsx\")\n", "load = pd.read_excel(load_file, index_col=0)\n", "load.plot(label=\"load\")\n", "plt.xlabel(\"time step\")\n", "plt.ylabel(\"P [MW]\")\n", "plt.grid()\n", "plt.show()" ] } ], "metadata": { "colab": { "name": "time_series.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 1 }