example/turbine_cluster_modelchain_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SPDX-FileCopyrightText: 2019 oemof developer group <contact@oemof.org>\n",
    "\n",
    "SPDX-License-Identifier: MIT\n",
    "\n",
    "SPDX-License-Identifier: CC-BY-4.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TurbineClusterModelChain example\n",
    "\n",
    "This example shows you how to calculate the power output of wind farms and wind turbine clusters using the windpowerlib. A cluster can be useful if you want to calculate the feed-in of a region for which you want to use one single weather data point.\n",
    "\n",
    "Functions that are used in the ModelChain example, like the initialization of wind turbines, are imported and used without further explanation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports and initialization of wind turbines\n",
    "\n",
    "The import of weather data and the initialization of wind turbines is done as in the ``modelchain_example``. Be aware that currently for wind farm and wind cluster calculations wind turbines need to have a power curve as some calculations do not work with the power coefficient curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import modelchain_example as mc_e\n",
    "from windpowerlib import TurbineClusterModelChain, WindTurbineCluster, WindFarm\n",
    "\n",
    "import logging\n",
    "logging.getLogger().setLevel(logging.DEBUG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variable_name             wind_speed          temperature         pressure\n",
      "height                            10       80           2      10        0\n",
      "2010-01-01 00:00:00+01:00    5.32697  7.80697      267.60  267.57  98405.7\n",
      "2010-01-01 01:00:00+01:00    5.46199  7.86199      267.60  267.55  98382.7\n",
      "2010-01-01 02:00:00+01:00    5.67899  8.59899      267.61  267.54  98362.9\n",
      "\n",
      "nominal power of my_turbine: 3000000.0\n"
     ]
    }
   ],
   "source": [
    "# Get weather data\n",
    "weather = mc_e.get_weather_data('weather.csv')\n",
    "print(weather[['wind_speed', 'temperature', 'pressure']][0:3])\n",
    "\n",
    "# Initialize wind turbines\n",
    "my_turbine, e126, my_turbine2 = mc_e.initialize_wind_turbines()\n",
    "print()\n",
    "print('nominal power of my_turbine: {}'.format(my_turbine.nominal_power))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize wind farm\n",
    "\n",
    "To initialize a specific wind farm you need to provide a wind turbine fleet specifying the wind turbines and their number or total installed capacity (in Watt) in the farm. Optionally, you can specify a wind farm efficiency and a name as an identifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# specification of wind farm data where turbine fleet is provided in a\n",
    "# pandas.DataFrame\n",
    "# for each turbine type you can either specify the number of turbines of\n",
    "# that type in the wind farm (float values are possible as well) or the\n",
    "# total installed capacity of that turbine type in W\n",
    "wind_turbine_fleet = pd.DataFrame(\n",
    "        {'wind_turbine': [my_turbine, e126],  # as windpowerlib.WindTurbine\n",
    "         'number_of_turbines': [6, None],\n",
    "         'total_capacity': [None, 12.6e6]}\n",
    "    )\n",
    "# initialize WindFarm object\n",
    "example_farm = WindFarm(name='example_farm',\n",
    "                        wind_turbine_fleet=wind_turbine_fleet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following, a wind farm with a constant efficiency is defined. A wind farm efficiency can also be dependent on the wind speed in which case it needs to be provided as a dataframe with 'wind_speed' and 'efficiency' columns containing wind speeds in m/s and the corresponding dimensionless wind farm efficiency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nominal power of first turbine type of example_farm_2: 3000000.0\n"
     ]
    }
   ],
   "source": [
    "# specification of wind farm data (2) containing a wind farm efficiency\n",
    "# wind turbine fleet is provided using the to_group function\n",
    "example_farm_2_data = {\n",
    "    'name': 'example_farm_2',\n",
    "    'wind_turbine_fleet': [my_turbine.to_group(6),\n",
    "                           e126.to_group(total_capacity=12.6e6)],\n",
    "    'efficiency': 0.9}\n",
    "\n",
    "# initialize WindFarm object\n",
    "example_farm_2 = WindFarm(**example_farm_2_data)\n",
    "\n",
    "print('nominal power of first turbine type of example_farm_2: {}'.format(\n",
    "    example_farm_2.wind_turbine_fleet.loc[0, 'wind_turbine'].nominal_power))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize wind turbine cluster\n",
    "\n",
    "Like for a wind farm for the initialization of a wind turbine cluster you can use a dictionary that contains the basic parameters. A wind turbine cluster is defined by its wind farms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# specification of cluster data\n",
    "example_cluster_data = {\n",
    "    'name': 'example_cluster',\n",
    "    'wind_farms': [example_farm, example_farm_2]}\n",
    "\n",
    "# initialize WindTurbineCluster object\n",
    "example_cluster = WindTurbineCluster(**example_cluster_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use the TurbineClusterModelChain to calculate power output\n",
    "\n",
    "The TurbineClusterModelChain is a class that provides all necessary steps to calculate the power output of a wind farm or wind turbine cluster. \n",
    "\n",
    "Like the ModelChain (see [basic example](modelchain_example_notebook.ipynb)) you can use the TurbineClusterModelChain with default parameters as shown in this example for the wind farm or specify custom parameters as done here for the cluster.\n",
    "If you use the 'run_model' method first the aggregated power curve and the mean hub height of the wind farm/cluster is calculated, then inherited functions of the ModelChain are used to calculate the wind speed and density (if necessary) at hub height. After that, depending on the parameters, wake losses are applied and at last the power output is calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG:root:Wake losses considered by dena_mean wind efficiency curve.\n",
      "DEBUG:root:Aggregated power curve not smoothed.\n",
      "DEBUG:root:Calculating wind speed using logarithmic wind profile.\n",
      "DEBUG:root:Calculating power output using power curve.\n"
     ]
    }
   ],
   "source": [
    "# power output calculation for example_farm\n",
    "# initialize TurbineClusterModelChain with default parameters and use\n",
    "# run_model method to calculate power output\n",
    "mc_example_farm = TurbineClusterModelChain(example_farm).run_model(weather)\n",
    "# write power output time series to WindFarm object\n",
    "example_farm.power_output = mc_example_farm.power_output "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG:root:Wake losses considered with wind_farm_efficiency.\n",
      "DEBUG:root:Aggregated power curve smoothed by method: Staffell_Pfenninger\n",
      "DEBUG:root:Calculating wind speed using logarithmic wind profile.\n",
      "DEBUG:root:Calculating temperature using temperature gradient.\n",
      "DEBUG:root:Calculating density using ideal gas equation.\n",
      "DEBUG:root:Calculating power output using power curve.\n"
     ]
    }
   ],
   "source": [
    "# set efficiency of example_farm to apply wake losses\n",
    "example_farm.efficiency = 0.9\n",
    "\n",
    "# power output calculation for turbine_cluster\n",
    "# own specifications for TurbineClusterModelChain setup\n",
    "modelchain_data = {\n",
    "        'wake_losses_model': 'wind_farm_efficiency',  #\n",
    "                                           # 'dena_mean' (default), None,\n",
    "                                           # 'wind_farm_efficiency' or name\n",
    "                                           #  of another wind efficiency curve\n",
    "                #  see :py:func:`~.wake_losses.get_wind_efficiency_curve`\n",
    "        'smoothing': True,  # False (default) or True\n",
    "        'block_width': 0.5,  # default: 0.5\n",
    "        'standard_deviation_method': 'Staffell_Pfenninger',  #\n",
    "                                            # 'turbulence_intensity' (default)\n",
    "                                            # or 'Staffell_Pfenninger'\n",
    "        'smoothing_order': 'wind_farm_power_curves',  #\n",
    "                                        # 'wind_farm_power_curves' (default) or\n",
    "                                        # 'turbine_power_curves'\n",
    "        'wind_speed_model': 'logarithmic',  # 'logarithmic' (default),\n",
    "                                            # 'hellman' or\n",
    "                                            # 'interpolation_extrapolation'\n",
    "        'density_model': 'ideal_gas',  # 'barometric' (default), 'ideal_gas' or\n",
    "                                       # 'interpolation_extrapolation'\n",
    "        'temperature_model': 'linear_gradient',  # 'linear_gradient' (def.) or\n",
    "                                                 # 'interpolation_extrapolation'\n",
    "        'power_output_model': 'power_curve',  # 'power_curve' (default) or\n",
    "                                              # 'power_coefficient_curve'\n",
    "        'density_correction': True,  # False (default) or True\n",
    "        'obstacle_height': 0,  # default: 0\n",
    "        'hellman_exp': None}  # None (default) or None\n",
    "\n",
    "# initialize TurbineClusterModelChain with own specifications and use\n",
    "# run_model method to calculate power output\n",
    "mc_example_cluster = TurbineClusterModelChain(\n",
    "        example_cluster, **modelchain_data).run_model(weather)\n",
    "# write power output time series to WindTurbineCluster object\n",
    "example_cluster.power_output = mc_example_cluster.power_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results\n",
    "\n",
    "If you have matplotlib installed you can visualize the calculated power output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try to import matplotlib\n",
    "logging.getLogger().setLevel(logging.WARNING)\n",
    "try:\n",
    "    from matplotlib import pyplot as plt\n",
    "    # matplotlib inline needed in notebook to plot inline\n",
    "    %matplotlib inline \n",
    "except ImportError:\n",
    "    plt = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot turbine power output\n",
    "if plt:\n",
    "    example_cluster.power_output.plot(legend=True, label='example cluster')\n",
    "    example_farm.power_output.plot(legend=True, label='example farm')\n",
    "    plt.xlabel('Wind speed in m/s')\n",
    "    plt.ylabel('Power in W')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot aggregated (and smoothed) power curve of example_cluster\n",
    "if plt:\n",
    "    example_cluster.power_curve.plot(\n",
    "        x='wind_speed', y='value', style='*')\n",
    "    plt.xlabel('Wind speed in m/s')\n",
    "    plt.ylabel('Power in W')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}