wind_power_timeseries 0.5.0

Package for downloading timeseries data and convert to generated power

Timeseries Methods for Wind Power

This is a Python package to download timeseries data from the NORA3 reanalysis, and to convert to power time series for wind farms.

Quick-start

• Installl as a Python package in your preferred way, for example using poetry
• Copy and adjust the example code below.

Example

import pandas as pd
import pathlib
import timeseries_methods as tm

# Specify windfarms
windfarms = pd.DataFrame([
    {"id":"windfarm_1", "lat":55, "lon": 9, "orientation":None, "shape":None, "turbine_height": 150},
    {"id":"windfarm_2", "lat":60, "lon": 7, "orientation":None, "shape":None, "turbine_height": 150},
]).set_index("id")

# Download wind speed data from NORA3 and save to CSV file in specified folder
time_start = "2022-05-01"
time_end = "2022-05-05"
data_path = pathlib.Path("downloaded_nora3").mkdir(parents=True,exist_ok=True)

# Download from server and save to files
wind_data = tm.download.retrieve_nora3(
    windfarms,time_start,time_end,use_cache=True,data_path=data_path)

# Specify function for wind speed to wind power conversion, using a pre-defined function:
my_power_function = tm.compute.func_ninja_compute_power
my_args = {"turbine_power_curve": tm.compute.get_power_curve(name="VestasV80")}

# Compute wind farm power from wind speed data
windpower = tm.compute.compute_power(windfarms,wind_data,
        power_function=my_power_function,power_function_args=my_args)

# Save to file
windpower.to_csv("windpower.csv")

This creates a windpower dataframe

                     windfarm_1  windfarm_2
time
2022-05-01 00:00:00    0.053922    0.105866
2022-05-01 01:00:00    0.065940    0.037178
2022-05-01 02:00:00    0.107095    0.009000
2022-05-01 03:00:00    0.056030    0.006256
2022-05-01 04:00:00    0.041206    0.003573
...

Model description

The package consists of two modules

• download.py - for downloading timeseries
• compute.py - for processing timeseries and computing generated power

The main inputs are the specifications of

• wind farms
• Wind power functions

Wind data

The package relies on the metocean_api Python package for retrieving data from the Reanalysis servers (NORA3 or ERA5)

Data retrieval is time-consuming and downloaded NETCDF files are automatically saved in a local cache that may be re-used.

Downloaded data is also saved in CSV files for easy access later on.

Regarding wind direction:

• wind direction angle is defined as the direction the wind is blowing from. 0 degrees for wind from north, 90 degrees for wind from east
• u,v wind components: u-component is wind blowing towards the east, v-component is wind blowing towards the north
• Example: (u,v) = (1,1) corresponds to direction towards north-east, ie from south-west, direction = 255 (=45+180) degrees.

Wind farms

Wind farms are specified with the following information

• id (name)
• latitude
• longitude
• turbine height
• orientation
• shape (aspect ratio)

Wind power function

Generated wind power is computed via compute_power:

windpower = tm.compute.compute_power(windfarms, wind_data, power_function,power_function_args)

This takes as arguments

• wind farms specified in a pandas dataframe
• wind data: Reanalysis wind data from NORA3
• a power function (reference)
• extra power function arguments specified as a dictionary.

The wind power function is what converts wind data to generated power. It acts on the wind data and is a function of

• wind speed at hub height
• wind direction at hub height
• wind farm orientation
• wind farm shape
• possibly additional arguments supplied via the power_function_args dictionary

There are three main alternatives for specifying the power function:

ALTERNATIVE 1: From power curve. The package includes some pre-defined wind turbine power curves, that can be used to define a power function. In this case, the power function is only a function of the wind speed (at hub height).

pc_V80 = tm.compute.get_power_curve(name="VestasV80")
my_power_function = tm.compute.func_power_curve(pc_V80)

ALTERNATIVE 2: Pre-defined function. The package also includes som pre-defined power functions. Shown below is a function that reproduces the method used by Renewables.ninja, which is based on gauss-filtering a single turbine curve with a wake loss factor included.

power_function = tm.compute.func_ninja_compute_power
power_function_args = {
    "turbine_power_curve": tm.compute.get_power_curve(name="VestasV80"),
    "sigma": 2*1.17, 
    "wakeloss", 0.71,
    }

ALTERNATIVE 3: Explicitly define function. Another alternative is to define the power conversion function explictily, for example like this:

def power_function(
        windspeed_at_hub,
        winddirection_at_hub,
        windfarm_orientation,
        windfarm_shape,
        **kwargs):
    windpower =  ...
    return windpower
power_function_args = dict()

References

Science

• H Hersbach et al., "The ERA5 global Reanalysis", https://doi.org/10.1002/qj.3803
• H Haakenstad et al., "NORA3: A Nonhydrostatic High-Resolution Hindcast of the North Sea, the Norwegian Sea, and the Barents Sea", https://doi.org/10.1175/JAMC-D-21-0029.1
• IM Solbrekke, A Sorteberg, H Haakenstad, "The 3 km Norwegian reanalysis (NORA3) – a validation of offshore wind resources in the North Sea and the Norwegian Sea", https://doi.org/10.5194/wes-6-1501-2021
• I Stafell, S Pfenninger, "Using bias-corrected reanalysis to simulate current and future wind power output", https://doi.org/10.1016/j.energy.2016.08.068, https://www.renewables.ninja/documentation/science

Data and APIs

• metocean-api documentation: https://metocean-api.readthedocs.io/en/latest/index.html
• ECMWF Reanalysis v5 ERA5, https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5
• WindPRO NORA3 page, https://help.emd.dk/mediawiki/index.php/NORA3
• WindPRO ERA5 page, https://help.emd.dk/mediawiki/index.php/ERA5_Gaussian_Grid