Multi-height wind data

In this example we explore the calculation of multi-height wind data, as for example obtained from WRF results or downloaded from the NEWA website at a single point.

Here we will use the static data file WRF-Timeseries-4464.csv.gz that is part of the foxes static data. It has the following data structure:

Time,WS-50,WS-75,...,WS-500,WD-50,WD-75,...,WD-500,TKE-50,TKE-75,...,TKE-500,RHO
2009-01-01 00:00:00,7.37214,7.42685,...,1.28838
...
2009-01-31 23:50:00,10.27767,10.36368,...,1.30095

The time stamp column marks one month in 10 minute steps, and the wind speed (WS), wind direction (WD) and turbulent kinetic energy (TKE) are provided at 8 heights between 50 and 500 m. The air density (RHO) does not have height dependency but varies with time.

The basic assumption of this example is that we can calculate our wind farm results based on this data, i.e., that the horizontal variation can be neglected (for completely heterogeneous inflow data, see the corresponding example).

These are the imports for this example:

In [1]:
%matplotlib inline
import matplotlib.pyplot as plt

import foxes
import foxes.variables as FV
import foxes.constants as FC

First, we setup the model book and the wind farm. We choose 5 turbines in a row:

In [2]:
# create wind farm, a single row of turbines:
farm = foxes.WindFarm()
foxes.input.farm_layout.add_row(
    farm=farm,
    xy_base=[0.0, 0.0],
    xy_step=[600.0, 0.0],
    n_turbines=5,
    turbine_models=["NREL5MW"],
    H=200.,
    verbosity=0,
)

ax = foxes.output.FarmLayoutOutput(farm).get_figure(figsize=(5,3))
plt.show()
../_images/notebooks_multi_height_5_0.png

Note that we manually change the hub height from 90 m to 200 m here. Next, we create the states based on the static data file WRF-Timeseries-4464.csv.gz:

In [3]:
states = foxes.input.states.MultiHeightTimeseries(
    data_source="WRF-Timeseries-4464.csv.gz",
    output_vars=[FV.WS, FV.WD, FV.TI, FV.RHO],
    var2col={},
    heights=[50, 75, 90, 100, 150, 200, 250, 500],
    fixed_vars={FV.TI: 0.05},
)

o = foxes.output.StatesRosePlotOutput(states, point=[0., 0., 100.])
fig = o.get_figure(16, FV.AMB_WS, [0, 3.5, 6, 10, 15, 20], figsize=(6, 6))
plt.show()
../_images/notebooks_multi_height_7_0.png

Our file has already the default column names as expected by foxes. However, otherwise you can use the var2col option as a mapping from the expected to the actual column names, if needed. Note that the heights are searched for all output variables that are neither mentioned in fixed_vars not appear as height independent column names (e.g. RHO instead of RHO-50, etc.).

Let’s next setup our algorithm. Notice that we include the z-sensitive rotor model level10, with 10 points on a vertical line (also the grid models would be an option). The partial wakes choice None represents default settings for all wake models. It is important that we do not select rotor_points together with the level10 rotor, since averaging over a vertical line of points does not make much sense.

In [4]:
algo = foxes.algorithms.Downwind(
    farm,
    states,
    rotor_model="level10",
    wake_models=["Bastankhah2014_linear_k002"],
    partial_wakes=None,
    chunks={FC.STATE: 1000},
    verbosity=0,
)

Our next goal is the visualization of the vertical wind profile. For that we select a certain time step where the wind direction is approximately from the west. We can do this by initializing the states using the states_loc option:

In [5]:
states.reset(states_loc=["2009-01-06 13:50:00"])

We now calculate this single state and create the vertical flow figure:

In [6]:
farm_results = algo.calc_farm()

o = foxes.output.FlowPlots2D(algo, farm_results)
g = o.gen_states_fig_xz(FV.AMB_WS, resolution=10, x_direction=270,
        xmin=0., xmax=1000., zmin=50., zmax=500., figsize=(8,6))
fig = next(g)
plt.show()
../_images/notebooks_multi_height_13_0.png

For the full calculation of all 4464 states, we now undo our earlier states selection:

In [7]:
states.reset(states_loc=None)

We can now calculate the full states results:

In [8]:
farm_results = algo.calc_farm()

fr = farm_results.to_dataframe()
print(fr[[FV.WD, FV.REWS, FV.P]])

o = foxes.output.FarmLayoutOutput(farm, farm_results)
o.get_figure(color_by="mean_REWS", title="Mean REWS [m/s]", s=150, annotate=0)
plt.show()

o = foxes.output.FarmResultsEval(farm_results)
P0 = o.calc_mean_farm_power(ambient=True)
P = o.calc_mean_farm_power()
print(f"\nFarm power        : {P/1000:.1f} MW")
print(f"Farm ambient power: {P0/1000:.1f} MW")
print(f"Farm efficiency   : {o.calc_farm_efficiency()*100:.2f} %")
print(f"Annual farm yield : {o.calc_farm_yield(algo=algo):.2f} GWh")
                                    WD       REWS            P
state               turbine
2009-01-01 00:00:00 0        340.14377   7.593007  1459.644365
                    1        340.14377   7.593007  1459.644365
                    2        340.14377   7.593007  1459.644365
                    3        340.14377   7.593007  1459.644365
                    4        340.14377   7.593007  1459.644365
...                                ...        ...          ...
2009-01-31 23:50:00 0         86.83636   6.659619   974.752275
                    1         86.83636   6.738231  1009.394326
                    2         86.83636   6.879601  1071.692139
                    3         86.83636   7.344344  1303.163047
                    4         86.83636  10.531895  3808.095778

[22320 rows x 3 columns]
../_images/notebooks_multi_height_17_1.png

Farm power        : 14.8 MW
Farm ambient power: 15.2 MW
Farm efficiency   : 97.87 %
Annual farm yield : 129.96 GWh