{ "cells": [ { "cell_type": "markdown", "id": "metropolitan-humor", "metadata": {}, "source": [ "### Time series calculation with OPF" ] }, { "cell_type": "markdown", "id": "chinese-wilderness", "metadata": {}, "source": [ "This tutorial shows how a simple time series simulation with optimal power flow is performed with the timeseries and control module in pandapower. " ] }, { "cell_type": "code", "execution_count": 1, "id": "outer-scroll", "metadata": {}, "outputs": [], "source": [ "import pandapower as pp\n", "import numpy as np\n", "import os\n", "import pandas as pd\n", "import tempfile\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\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline " ] }, { "cell_type": "markdown", "id": "shared-state", "metadata": {}, "source": [ "We created a simple network and then set the constraints for buses, lines, and external grid. We set the costs for the external grid and sgen. The cost of sgen is kept negative to maximize the generation, sgen can be controlled with OPF but not the load." ] }, { "cell_type": "code", "execution_count": 2, "id": "negative-owner", "metadata": {}, "outputs": [], "source": [ "def simple_test_net():\n", " net = pp.create_empty_network()\n", "\n", " b0 = pp.create_bus(net, 110, min_vm_pu=0.98, max_vm_pu=1.05)\n", " b1 = pp.create_bus(net, 20, min_vm_pu=0.98, max_vm_pu=1.05)\n", " b2 = pp.create_bus(net, 20, min_vm_pu=0.98, max_vm_pu=1.05)\n", " b3 = pp.create_bus(net, 20, min_vm_pu=0.9, max_vm_pu=1.05)\n", " \n", " e=pp.create_ext_grid(net, b0, min_p_mw=-200, max_p_mw=200)\n", " costeg = pp.create_poly_cost(net, e, 'ext_grid', cp1_eur_per_mw=10)\n", " \n", " pp.create_line(net, b0, b1, 10, \"149-AL1/24-ST1A 110.0\", max_loading_percent=80)\n", " pp.create_line(net, b1, b2, 10, \"149-AL1/24-ST1A 110.0\", max_loading_percent=80)\n", " pp.create_line(net, b1, b3, 10, \"149-AL1/24-ST1A 110.0\", max_loading_percent=80)\n", "\n", " pp.create_load(net, b3, p_mw=10., q_mvar=-5., name='load1', controllable=False)\n", " g1=pp.create_sgen(net, b2, p_mw=0., q_mvar=-2, min_p_mw=0, max_p_mw=30, min_q_mvar=-3, max_q_mvar=3, name='sgen1', controllable=True)\n", " pp.create_poly_cost(net, g1, 'sgen1', cp1_eur_per_mw=-1)\n", " \n", " return net\n" ] }, { "cell_type": "markdown", "id": "generous-punishment", "metadata": {}, "source": [ "We created the datasource (which contains the time series P values) and defined the range of the load power values so that the line that connects it will not be overloaded because the load will not be controlled with the OPF." ] }, { "cell_type": "code", "execution_count": 3, "id": "under-lewis", "metadata": {}, "outputs": [], "source": [ "def create_data_source(n_timesteps=24):\n", " profiles = pd.DataFrame()\n", " profiles['load1_p'] = np.random.random(n_timesteps) * 10.\n", " profiles['sgen1_p'] = np.random.random(n_timesteps) * 20.\n", "\n", " ds = DFData(profiles)\n", "\n", " return profiles, ds" ] }, { "cell_type": "markdown", "id": "identical-madagascar", "metadata": {}, "source": [ "We created the controllers to update the P values of the load and the sgen\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "dutch-three", "metadata": {}, "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", "id": "patient-battle", "metadata": {}, "source": [ "Instead of saving the whole net (which takes a lot of time), we extract only predefined outputs. The variables of create_output_writer are saved to the hard drive after the time series loop.\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "under-console", "metadata": {}, "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", " ow.log_variable('res_sgen', '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", "id": "adjacent-adventure", "metadata": {}, "source": [ "Lets run the code for the timeseries simulation with OPF. Note that parameter 'run' is set to the function that runs OPF (run=pp.runopp)." ] }, { "cell_type": "code", "execution_count": 6, "id": "polyphonic-distributor", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Results can be found in your local temp folder: C:\\Users\\mmilovic\\AppData\\Local\\Temp\\time_series_example\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████████████████████████████████████████████████████████████████████████████| 24/24 [00:38<00:00, 1.59s/it]\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", " \n", "# create the network\n", "net = simple_test_net()\n", "\n", "# create (random) data source\n", "n_timesteps = 24\n", "profiles, ds = create_data_source(n_timesteps)\n", "\n", "# 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", "# 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", "# the main time series function with optimal power flow\n", "run_timeseries(net, time_steps, run=pp.runopp, delta=1e-16)" ] }, { "cell_type": "markdown", "id": "alternate-science", "metadata": {}, "source": [ "We can see that all of the bus voltages are in the defined constraint range according to the optimal power flow." ] }, { "cell_type": "code", "execution_count": 7, "id": "collected-marshall", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 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()" ] }, { "cell_type": "markdown", "id": "native-delicious", "metadata": {}, "source": [ "The loading_percent of the lines are also below 80% as defined the constraints for optimal power flow." ] }, { "cell_type": "code", "execution_count": 8, "id": "reasonable-tennis", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 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()\n", "plt.xlabel(\"time step\")\n", "plt.ylabel(\"line loading [%]\")\n", "plt.title(\"Line Loading\")\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "popular-shareware", "metadata": {}, "source": [ "Here we compared the sgen power generation before and after OPF." ] }, { "cell_type": "code", "execution_count": 9, "id": "suitable-indonesia", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# sgen results\n", "sgen_file = os.path.join(output_dir, \"res_sgen\", \"p_mw.xlsx\")\n", "sgen = pd.read_excel(sgen_file, index_col=0)\n", "ax=sgen[0].plot(label=\"sgen (after OPF)\")\n", "ds.df.sgen1_p.plot(ax=ax, label=\"sgen (original)\", linestyle='--')\n", "ax.legend()\n", "plt.xlabel(\"time step\")\n", "plt.ylabel(\"P [MW]\")\n", "plt.grid()\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 5 }