{ "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, b2, 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=\".xls\", 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\\e2n011\\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": 9, "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.xls\")\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.xls\")\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.xls\")\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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 1 }