|
| 1 | +""" |
| 2 | +The ``turbine_cluster_modelchain_example`` module shows how to calculate the |
| 3 | +power output of wind farms and wind turbine clusters with the windpowerlib. |
| 4 | +A cluster can be useful if you want to calculate the feed-in of a region for |
| 5 | +which you want to use one single weather data point. |
| 6 | +
|
| 7 | +Functions that are used in the ``modelchain_example``, like the initialization |
| 8 | +of wind turbines, are imported and used without further explanations. |
| 9 | +
|
| 10 | +SPDX-FileCopyrightText: 2019 oemof developer group <[email protected]> |
| 11 | +SPDX-License-Identifier: MIT |
| 12 | +""" |
| 13 | +import pandas as pd |
| 14 | +import requests |
| 15 | +import os |
| 16 | + |
| 17 | +from windpowerlib import WindFarm |
| 18 | +from windpowerlib import WindTurbine |
| 19 | +from windpowerlib import TurbineClusterModelChain |
| 20 | +from windpowerlib import WindTurbineCluster |
| 21 | + |
| 22 | +# You can use the logging package to get logging messages from the windpowerlib |
| 23 | +# Change the logging level if you want more or less messages |
| 24 | +import logging |
| 25 | + |
| 26 | +logging.getLogger().setLevel(logging.INFO) |
| 27 | + |
| 28 | + |
| 29 | +def get_weather_data(filename="weather.csv", **kwargs): |
| 30 | + r""" |
| 31 | + Imports weather data from a file. |
| 32 | +
|
| 33 | + The data include wind speed at two different heights in m/s, air |
| 34 | + temperature in two different heights in K, surface roughness length in m |
| 35 | + and air pressure in Pa. The height in m for which the data applies is |
| 36 | + specified in the second row. |
| 37 | + In case no weather data file exists, an example weather data file is |
| 38 | + automatically downloaded and stored in the same directory as this example. |
| 39 | +
|
| 40 | + Parameters |
| 41 | + ---------- |
| 42 | + filename : str |
| 43 | + Filename of the weather data file. Default: 'weather.csv'. |
| 44 | +
|
| 45 | + Other Parameters |
| 46 | + ---------------- |
| 47 | + datapath : str, optional |
| 48 | + Path where the weather data file is stored. |
| 49 | + Default is the same directory this example is stored in. |
| 50 | +
|
| 51 | + Returns |
| 52 | + ------- |
| 53 | + :pandas:`pandas.DataFrame<frame>` |
| 54 | + DataFrame with time series for wind speed `wind_speed` in m/s, |
| 55 | + temperature `temperature` in K, roughness length `roughness_length` |
| 56 | + in m, and pressure `pressure` in Pa. |
| 57 | + The columns of the DataFrame are a MultiIndex where the first level |
| 58 | + contains the variable name as string (e.g. 'wind_speed') and the |
| 59 | + second level contains the height as integer at which it applies |
| 60 | + (e.g. 10, if it was measured at a height of 10 m). The index is a |
| 61 | + DateTimeIndex. |
| 62 | +
|
| 63 | + """ |
| 64 | + |
| 65 | + if "datapath" not in kwargs: |
| 66 | + kwargs["datapath"] = os.path.dirname(__file__) |
| 67 | + |
| 68 | + file = os.path.join(kwargs["datapath"], filename) |
| 69 | + |
| 70 | + # download example weather data file in case it does not yet exist |
| 71 | + if not os.path.isfile(file): |
| 72 | + logging.debug("Download weather data for example.") |
| 73 | + req = requests.get("https://osf.io/59bqn/download") |
| 74 | + with open(file, "wb") as fout: |
| 75 | + fout.write(req.content) |
| 76 | + |
| 77 | + # read csv file |
| 78 | + weather_df = pd.read_csv( |
| 79 | + file, |
| 80 | + index_col=0, |
| 81 | + header=[0, 1], |
| 82 | + date_parser=lambda idx: pd.to_datetime(idx, utc=True), |
| 83 | + ) |
| 84 | + |
| 85 | + # change time zone |
| 86 | + weather_df.index = weather_df.index.tz_convert("Europe/Berlin") |
| 87 | + |
| 88 | + return weather_df |
| 89 | + |
| 90 | + |
| 91 | +def run_example(): |
| 92 | + r""" |
| 93 | + Runs the example. |
| 94 | +
|
| 95 | + """ |
| 96 | + weather = get_weather_data("weather.csv") |
| 97 | + e126 = WindTurbine( |
| 98 | + **{ |
| 99 | + "turbine_type": "E-126/4200", # turbine type as in register |
| 100 | + "hub_height": 135, # in m |
| 101 | + } |
| 102 | + ) |
| 103 | + v90 = WindTurbine( |
| 104 | + **{ |
| 105 | + "turbine_type": "V90/2000", # turbine name as in register |
| 106 | + "hub_height": 120, # in m |
| 107 | + } |
| 108 | + ) |
| 109 | + |
| 110 | + # specification of wind farm data (2) containing a wind farm efficiency |
| 111 | + # wind turbine fleet is provided using the to_group function |
| 112 | + example_farm = WindFarm( |
| 113 | + **{ |
| 114 | + "name": "example_farm_2", |
| 115 | + "wind_turbine_fleet": [ |
| 116 | + v90.to_group(number_turbines=6), |
| 117 | + e126.to_group(total_capacity=12.6e6), |
| 118 | + ], |
| 119 | + "efficiency": 0.5 |
| 120 | + } |
| 121 | + ) |
| 122 | + |
| 123 | + example_cluster = WindTurbineCluster(**{ |
| 124 | + "name": "Offshore_cluster", |
| 125 | + "wind_farms": [example_farm, example_farm], |
| 126 | + }) |
| 127 | + |
| 128 | + # ModelChain with wind farm |
| 129 | + mc_farm = TurbineClusterModelChain( |
| 130 | + example_farm, wake_losses_model=None, |
| 131 | + ).run_model(weather) |
| 132 | + flh_farm = mc_farm.power_output.sum() / example_farm.nominal_power |
| 133 | + |
| 134 | + # ModelChain with wind cluster |
| 135 | + mc_cluster = TurbineClusterModelChain( |
| 136 | + example_cluster, wake_losses_model=None, |
| 137 | + ).run_model(weather) |
| 138 | + flh_cluster = mc_cluster.power_output.sum() / example_cluster.nominal_power |
| 139 | + |
| 140 | + print("Full Load Hours of cluster:", flh_cluster) |
| 141 | + print("Full Load Hours of farm:", flh_farm) |
| 142 | + |
| 143 | + |
| 144 | +if __name__ == "__main__": |
| 145 | + run_example() |
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