Optimization of wind farm including neighbouring turbines
In Topfarm you have the option to include neighbouring wind turbines in your optimization. This means that the wakes from these turbines will be considered when calulating the optimal AEP, but their positions are not considered design variables. Note that the accuaracy of this is dependent on the distance to the neighbouring wind turbines and your wind farm models ability to predict long distance wakes.
[1]:
# Install TopFarm if needed
import importlib
if not importlib.util.find_spec("topfarm"):
%pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/TopFarm2.git
[2]:
import numpy as np
import matplotlib.pyplot as plt
import topfarm
from topfarm import TopFarmProblem
from topfarm.constraint_components.boundary import XYBoundaryConstraint
from topfarm.constraint_components.spacing import SpacingConstraint
from topfarm.cost_models.py_wake_wrapper import PyWakeAEPCostModelComponentAdditionalTurbines
from topfarm.easy_drivers import EasyScipyOptimizeDriver
from topfarm.plotting import XYPlotComp
from topfarm.cost_models.cost_model_wrappers import AEPCostModelComponent
from py_wake.deficit_models.gaussian import BastankhahGaussian
from py_wake.utils.gradients import autograd
from py_wake.validation.lillgrund import wt_x, wt_y, LillgrundSite, ct_curve, power_curve
from py_wake.wind_turbines import WindTurbine
from py_wake.wind_turbines.power_ct_functions import PowerCtTabular
[3]:
x2 = np.array([363089.20620581, 362841.19815026, 363024.53745529, 363202.56273871,
362627.89297065, 363352.67353534, 362882.00280241, 362774.92495544,
363488.38307778, 362491.87485306, 363244.96531305, 363181.03168444,
362454.34857131, 363640.1295227 , 362609.32998744, 362909.74450436,
363554.08869674, 362254.36079251, 363543.89240439, 362963.61115875,
362482.47585633, 363819.8209275 , 362305.36330315, 363189.81567612,
363439.03577114, 362141.72363739, 363833.70922823, 362638.81518275,
362677.64959042, 363857.74204276, 362047.26220207, 363541.54387668,
363172.26863347, 362183.59639605, 364044.33022603, 362278.14662334,
363005.25808714, 363734.05077116, 361897.72732477, 363893.16789162,
362796.78418716, 362387.86536213, 364121.72384201, 361953.11392421,
363413.71283633, 363453.74796523, 361900.61873674, 364175.02685267,
362371.77324208])
y2 = np.array([6154000. , 6153854.5244973 , 6154279.59164709,
6153735.79277733, 6154065.82049407, 6154224.34203495,
6153561.05630649, 6154433.36830217, 6153821.6432833 ,
6153790.2533259 , 6154523.47664532, 6153422.84246344,
6154316.21615621, 6154140.73067986, 6153444.31210837,
6154696.49543065, 6153533.01345979, 6153969.1654778 ,
6154541.2455867 , 6153213.05775872, 6154620.16688662,
6153889.69425372, 6153516.6898791 , 6154843.74971121,
6153233.82403924, 6154273.81356891, 6154385.19480965,
6153136.96734381, 6154896.21625677, 6153549.19482153,
6153748.86137406, 6154842.22515422, 6152997.6181212 ,
6154632.26883894, 6154086.52062451, 6153219.69765135,
6155077.84457572, 6153190.81616147, 6154102.15838379,
6154677.88258593, 6152882.94617467, 6154972.74316899,
6153692.58200013, 6153462.7553504 , 6155115.91934059,
6152885.37941837, 6154521.01729715, 6154362.27333719,
6152928.04241305])
wind_turbines = WindTurbine('SWT2p3_93_65', 92.6, 65, PowerCtTabular(ct_curve[:,0],power_curve[:,1], 'kW', ct_curve[:,1]))
n_wt = len(wt_x)
site = LillgrundSite()
wf_model = BastankhahGaussian(site, wind_turbines)
constraint_comp = XYBoundaryConstraint(np.asarray([wt_x, wt_y]).T)
cost_comp = PyWakeAEPCostModelComponentAdditionalTurbines(windFarmModel=wf_model,
n_wt=n_wt,
add_wt_x=x2,
add_wt_y=y2,
grad_method=autograd)
plot_comp = XYPlotComp()
problem = TopFarmProblem(design_vars={'x': wt_x, 'y': wt_y},
constraints=[constraint_comp, SpacingConstraint(min_spacing=wind_turbines.diameter() * 2)],
cost_comp=cost_comp,
driver=EasyScipyOptimizeDriver(optimizer='SLSQP', maxiter=5),
plot_comp=plot_comp)
cost, state, recorder = problem.optimize(disp=True)
plt.figure()
plt.plot(np.concatenate((wt_x, x2)), np.concatenate((wt_y, y2)), '2k')
/builds/TOPFARM/TopFarm2/.pixi/envs/default/lib/python3.12/site-packages/py_wake/deficit_models/gaussian.py:124: UserWarning: The BastankhahGaussian model is not representative of the setup used in the literature. For this, use py_wake.literature.gaussian_models.Bastankhah_PorteAgel_2014 instead
DeprecatedModel.__init__(self, 'py_wake.literature.gaussian_models.Bastankhah_PorteAgel_2014')
Iteration limit reached (Exit mode 9)
Current function value: -36310.51158482283
Iterations: 5
Function evaluations: 6
Gradient evaluations: 6
Optimization FAILED.
Iteration limit reached
-----------------------------------
Optimized in 82.292s
[3]:
[<matplotlib.lines.Line2D at 0x78449727bf80>]
