Inputs for calculate_devex and calculate_capex converted from lists to np...

Inputs for calculate_devex and calculate_capex converted from lists to np arrays within the methods.

docs/source/api.rst

@@ -10,6 +10,7 @@ API Reference
 
     api_reference/topfarmproblem
     api_reference/cost
+    api_reference/dtucost
     api_reference/constraints
     api_reference/drivers
     api_reference/plotcomp

docs/source/api_reference/dtucost.rst

+
+
+DTU Cost Model
+====================
+
+
+
+.. autoclass:: topfarm.cost_models.dtu_wind_cm.dtu_wind_cm_main.economic_evaluation
+    
+    .. autosummary::
+        __init__
+        calculate_npv
+        calculate_irr
+        calculate_cash_flow
+        calculate_expenditures
+        calculate_devex
+        calculate_capex
+        calculate_opex
+        calculate_abex
+        calculate_turbine
+        calculate_foundation
+        high_speed_drivetrain
+        medium_speed_drivetrain
+        direct_drive_drivetrain
+		

docs/source/conf.py

@@ -20,6 +20,8 @@ import os
 import sys
 sys.path.insert(0, os.path.join(os.path.abspath('.'), '..', '..'))
 
+nbsphinx_allow_errors = True
+
 from topfarm import __version__
 from topfarm import __release__
 

docs/source/user_guide.rst

@@ -38,7 +38,7 @@ Design Variables
 
     These are the variables that should be changed during the optimization. Common
     options include wind turbine positions and/or turbine types.
-
+	
 
 Cost Component
 ----------------
@@ -51,7 +51,19 @@ Cost Component
     ``topfarm.cost_models.cost_model_wrappers``. TOPFARM also contains a wrapper
     for PyWake's AEP calculator, which can be used with a variety of wake models.
 
+	
+DTU Cost Model
+----------------	
+
+	This is the DTU offshore cost model. The class includes methods for simple calculations
+	of the Internal Rate of Return (IRR) and Net Present Value (NPV). Further, it breaks up
+	the project costs into DEVEX, CAPEX, OPEX and ABEX within seperate methods, which may be
+	called individually. It generally relies on curve fitting using input parameters such as 
+	rated power or water depth, and was tuned using data obtained from the 
+	industry. It supports three types of drivetrains: high-speed, medium-speed and direct-drive.
+	It also supports two types of offshore foundations: monopile and jacket. 
 
+	
 Drivers
 ----------
 

examples/docs/example_4_integrated_optimization_aep_and_irr.py

@@ -37,7 +37,9 @@ def main():
             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()
+            my_irr = economic_evaluation(Drotor_vector, power_rated_vector, hub_height_vector, aep).calculate_irr()
+            print(my_irr)
+            return my_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)

examples/docs/example_5_integrated_opt_with_dtu_cost_model.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.dtu_wind_cm_main 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))*1e-6] * n_wt 
+        hub_height_vector = [windTurbines.hub_height()] * n_wt 
+        AEPCalc = AEPCalculator(wake_model)         
+        distance_from_shore = 10         # [km]
+        energy_price = 0.1              # [Euro/kWh] What we get per kWh
+        project_duration = 20            # [years]    
+        rated_rpm_array = [12] * n_wt    # [rpm]
+        water_depth_array = [15] * n_wt  # [m]
+
+        eco_eval = economic_evaluation(distance_from_shore, energy_price, project_duration)
+
+        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):
+            eco_eval.calculate_irr(rated_rpm_array, Drotor_vector, power_rated_vector, hub_height_vector, water_depth_array, aep)
+            print(eco_eval.IRR)
+            return eco_eval.IRR
+
+        aep_comp = CostModelComponent(input_keys=['x','y'], n_wt=n_wt, cost_function=aep_func, output_key="aep", output_unit="kWh", 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()

topfarm/tests/test_dtu_cost_model/test_dtu_cm.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Dec 13 13:20:09 2019
+
+Full set of dtu cost model tests
+"""
+
+from topfarm.cost_models.economic_models.dtu_wind_cm_main import economic_evaluation
+from topfarm.tests import npt
+import warnings
+
+
+def test_dtu_cm_capex():
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+
+        distance_from_shore = 10          # [km]
+        energy_price = 0.2                # [Euro/kWh]
+        project_duration = 20             # [years]
+
+        eco_eval = economic_evaluation(distance_from_shore, energy_price, project_duration)
+
+        number_of_turbines = 10
+        rated_rpm_vector = [10.0] * number_of_turbines        # [RPM]
+        rotor_diameter_vector = [100.0] * number_of_turbines  # [m]
+        rated_power_vector = [10.0] * number_of_turbines      # [MW]
+        hub_height_vector = [100.0] * number_of_turbines      # [m]
+        water_depth_vector = [15.0] * number_of_turbines      # [m]
+
+        eco_eval.calculate_capex(rated_rpm_vector, rotor_diameter_vector, rated_power_vector,
+                                 hub_height_vector, water_depth_vector)
+
+        npt.assert_almost_equal(eco_eval.project_costs_sums['CAPEX'] * 1.0E-6, 93.6938301)  # [M Euro]
+
+
+def test_dtu_cm_devex():
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+
+        distance_from_shore = 10          # [km]
+        energy_price = 0.2                # [Euro/kWh]
+        project_duration = 20             # [years]
+
+        eco_eval = economic_evaluation(distance_from_shore, energy_price, project_duration)
+
+        number_of_turbines = 10
+        rated_power_vector = [10.0] * number_of_turbines  # [MW]
+
+        eco_eval.calculate_devex(rated_power_vector)
+
+        npt.assert_almost_equal(eco_eval.project_costs_sums['DEVEX'] * 1.0E-6, 12.3461538)  # [M Euro]
+
+
+def test_dtu_cm_opex():
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+
+        distance_from_shore = 10          # [km]
+        energy_price = 0.2                # [Euro/kWh]
+        project_duration = 20             # [years]
+
+        eco_eval = economic_evaluation(distance_from_shore, energy_price, project_duration)
+
+        number_of_turbines = 10
+        rated_power_vector = [10.0] * number_of_turbines  # [MW]
+
+        eco_eval.calculate_opex(rated_power_vector)
+
+        npt.assert_almost_equal(eco_eval.project_costs_sums['OPEX'] * 1.0E-6, 6.465)  # [M Euro / year]
+
+
+def test_dtu_cm_irr():
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+
+        distance_from_shore = 10          # [km]
+        energy_price = 0.2                # [Euro/kWh]
+        project_duration = 20             # [years]
+
+        eco_eval = economic_evaluation(distance_from_shore, energy_price, project_duration)
+
+        number_of_turbines = 10
+        rated_rpm_vector = [10.0] * number_of_turbines        # [RPM]
+        rotor_diameter_vector = [100.0] * number_of_turbines  # [m]
+        rated_power_vector = [10.0] * number_of_turbines      # [MW]
+        hub_height_vector = [100.0] * number_of_turbines      # [m]
+        water_depth_vector = [15.0] * number_of_turbines      # [m]
+        aep_vector = [8.0e6] * number_of_turbines             # [kWh]
+
+        eco_eval.calculate_irr(rated_rpm_vector, rotor_diameter_vector, rated_power_vector,
+                               hub_height_vector, water_depth_vector, aep_vector)
+
+        npt.assert_almost_equal(eco_eval.IRR, 6.2860816)  # [%]
+
+
+def test_dtu_cm_npv():
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+
+        distance_from_shore = 10          # [km]
+        energy_price = 0.2                # [Euro/kWh]
+        project_duration = 20             # [years]
+        discount_rate = 0.062860816       # [-]
+
+        eco_eval = economic_evaluation(distance_from_shore, energy_price,
+                                       project_duration, discount_rate)
+
+        number_of_turbines = 10
+        rated_rpm_vector = [10.0] * number_of_turbines        # [RPM]
+        rotor_diameter_vector = [100.0] * number_of_turbines  # [m]
+        rated_power_vector = [10.0] * number_of_turbines      # [MW]
+        hub_height_vector = [100.0] * number_of_turbines      # [m]
+        water_depth_vector = [15.0] * number_of_turbines      # [m]
+        aep_vector = [8.0e6] * number_of_turbines             # [kWh]
+
+        eco_eval.calculate_npv(rated_rpm_vector, rotor_diameter_vector, rated_power_vector,
+                               hub_height_vector, water_depth_vector, aep_vector)
+
+        npt.assert_almost_equal(eco_eval.NPV, 0.0437686)  # [Euro]