example_4_integrated_optimization_aep_and_irr.py

import numpy as np
from openmdao.api import view_model
from py_wake.examples.data.iea37._iea37 import IEA37_WindTurbines, IEA37Site
from py_wake.wake_models.gaussian import IEA37SimpleBastankhahGaussian
from py_wake.aep_calculator import AEPCalculator
from topfarm.cost_models.economic_models.turbine_cost import economic_evaluation
from topfarm.cost_models.cost_model_wrappers import CostModelComponent
from topfarm import TopFarmGroup, TopFarmProblem
from topfarm.easy_drivers import EasyRandomSearchDriver
from topfarm.drivers.random_search_driver import RandomizeTurbinePosition_Circle
from topfarm.constraint_components.boundary import CircleBoundaryConstraint
from topfarm.plotting import XYPlotComp, NoPlot
from topfarm.constraint_components.spacing import SpacingConstraint


def main():
    if __name__ == '__main__':
        try:
            import matplotlib.pyplot as plt
            plt.gcf()
            plot_comp = XYPlotComp()
            plot = True
        except RuntimeError:
            plot_comp = NoPlot()
            plot = False

        n_wt = 16
        site = IEA37Site(n_wt)
        windTurbines = IEA37_WindTurbines()
        wake_model = IEA37SimpleBastankhahGaussian(site, windTurbines)
        Drotor_vector = [windTurbines.diameter()] * n_wt 
        power_rated_vector = [float(windTurbines.power(20)/1000)] * n_wt 
        hub_height_vector = [windTurbines.hub_height()] * n_wt 
        AEPCalc = AEPCalculator(wake_model)         

        def aep_func(x, y, **kwargs):
            return AEPCalc.calculate_AEP(x_i=x, y_i=y).sum(-1).sum(-1)*10**6
        
        def irr_func(aep, **kwargs):
            return economic_evaluation(Drotor_vector, power_rated_vector, hub_height_vector, aep).calculate_irr()
        
        aep_comp = CostModelComponent(input_keys=['x','y'], n_wt=n_wt, cost_function=aep_func, output_key="aep", output_unit="GWh", objective=False, output_val=np.zeros(n_wt))
        irr_comp = CostModelComponent(input_keys=['aep'],   n_wt=n_wt, cost_function=irr_func, output_key="irr", output_unit="%",   objective=True, income_model=True)
        group = TopFarmGroup([aep_comp, irr_comp])
        problem = TopFarmProblem(
                design_vars=dict(zip('xy', site.initial_position.T)),
                cost_comp=group,
                driver=EasyRandomSearchDriver(randomize_func=RandomizeTurbinePosition_Circle(), max_iter=50),
                constraints=[SpacingConstraint(200),
                             CircleBoundaryConstraint([0, 0], 1300.1)],
                plot_comp=plot_comp)
        cost, state, recorder = problem.optimize()
        #        problem.evaluate()
#        view_model(problem, outfile='example_4_integrated_optimization_aep_and_irr_n2.html', show_browser=False)

main()