Wind sector management¶

In this example we switch off, derate or boost turbines according to wind conditions. Here are our imports:

In [1]:

%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt

import foxes
import foxes.variables as FV

Let’s initialize the engine:

In [2]:

engine = foxes.Engine.new("default")
engine.initialize()

Our wind conditions come from the Bremen wind rose (part of foxes’ static data):

In [3]:

states = foxes.input.states.StatesTable(
    data_source="wind_rose_bremen.csv",
    output_vars=[FV.WS, FV.WD, FV.TI, FV.RHO],
    var2col={FV.WS: "ws", FV.WD: "wd", FV.WEIGHT: "weight"},
    fixed_vars={FV.RHO: 1.225, FV.TI: 0.05},
)

o = foxes.output.StatesRosePlotOutput(states, point=[0.0, 0.0, 100.0])
o.get_figure(16, FV.AMB_WS, [0, 3.5, 6, 10, 15, 20], figsize=(6, 6))
plt.show()

DefaultEngine: Selecting engine 'single'
SingleChunkEngine: Calculating 216 states for 1 turbines
SingleChunkEngine: Running single chunk calculation for 216 states

The rules of wind sector management can either be given by a pandas readable file like .csv, or directly by a pandas.DataFrame object. Here we are persuing the latter option, for a wind farm that consists of two turbines, labelled T0 and T1, where turbine T1 is located east of turbine T0. This creates the table of rules:

In [4]:

rules = pd.DataFrame(columns=["tname", "WD_min", "WD_max", "WS_min", "WS_max", "MAXP"])
rules.index.name = "rule"
rules.loc[0] = ["T0", 170, 191, 3, 15, 500]
rules.loc[1] = ["T0", 250, 290, 9, 99, 0]
rules.loc[2] = ["T0", 340, 50, 0, 99, 0]
rules.loc[3] = ["T1", 70, 110, 3, 99, 0]
rules.loc[4] = ["T1", 250, 290, 9, 99, 6000]
rules

Out[4]:

Note that we can add multiple rules per turbine. Additional conditions on ranges of other variables can be added in addition to WD and WS by simply adding the corresponsing columns with min and max data.

The final column MAXP defines the values of the state-turbine variable that should be set if the current wind conditions fulfill the formulated range conditions. It is possible to add more variables by adding the corresponding columns to the table.

Let’s setup the model book, featuring the SectorManagement turbine model based on the above rules:

In [5]:

mbook = foxes.models.ModelBook()
ttype = foxes.models.turbine_types.PCtFile("NREL-5MW-D126-H90.csv")
mbook.turbine_types[ttype.name] = ttype

mbook.turbine_models["sector_rules"] = foxes.models.turbine_models.SectorManagement(
    data_source=rules,
    col_tnames="tname",
    range_vars=[FV.WD, FV.REWS],
    target_vars=[FV.MAX_P],
    colmap={"REWS_min": "WS_min", "REWS_max": "WS_max"},
)

Due to the stated `range_vars the model will look for columns WD_min, WD_max, REWS_min, REWS_max. The latter two have slightly different names in the DataFrame, hence the entries in the colmap dict. Notice that also target variable names could be mapped to other column names, if needed.

Now we create the wind farm with the two wind turbines in west-east orientation:

In [6]:

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=2,
    turbine_models=[ttype.name, "sector_rules", "PMask"],
)

ax = foxes.output.FarmLayoutOutput(farm).get_figure(figsize=(4, 3))
plt.show(ax.get_figure())

Turbine 0, T0: xy=(0.00, 0.00), NREL-5MW, sector_rules, PMask
Turbine 1, T1: xy=(600.00, 0.00), NREL-5MW, sector_rules, PMask

The turbine model PMask was added in order to evaluate the variable MAXP that was set by the sector_rules model. It is responsible for switching off, derating or boosting turbines, cf. example Power mask.

Now let’s create an algorithm object and run the case:

In [7]:

algo = foxes.algorithms.Downwind(
    farm,
    states=states,
    rotor_model="centre",
    wake_models=["Bastankhah2014_linear_k002"],
    mbook=mbook,
    verbosity=0,
)

In [8]:

farm_results = algo.calc_farm()

DefaultEngine: Selecting engine 'single'
SingleChunkEngine: Calculating 216 states for 2 turbines
SingleChunkEngine: Running single chunk calculation for 216 states

The effect of the rules is best visualized in rose plots for the two turbines:

In [9]:

fig = plt.figure(figsize=(12, 4))
ax1 = fig.add_subplot(121, polar=True)
ax2 = fig.add_subplot(122, polar=True)

o = foxes.output.RosePlotOutput(farm_results)
o.get_figure(
    16,
    FV.P,
    [100, 1000, 2000, 4000, 5001, 7000],
    turbine=0,
    title="Power turbine 0",
    fig=fig,
    ax=ax1,
)

o = foxes.output.RosePlotOutput(farm_results)
o.get_figure(
    16,
    FV.P,
    [100, 1000, 2000, 4000, 5001, 7000],
    turbine=1,
    title="Power turbine 1",
    fig=fig,
    ax=ax2,
)
plt.show()

The wind sector management has the following effects (compare to the rules table above):

• Turbine 0 switches off at high wind speeds for westerly wind directions

• Turbine 1 is boosted for such winds

• Turbine 1 switches off completely for wind from east

• Turbine 0 is derated for wind from south

• Turbine 0 switches off for wind from north

After all work is done, we can shutdown the engine:

In [10]:

engine.finalize()