Merge branch 'aep' into 'master'

added aep component

See merge request !1

src/SEAMAero/aep_unit.py

+__author__ = 's127504'
+
+# unit aep_unit
+
+from math import pow
+import numpy as np
+from scipy.optimize import minimize
+
+from openmdao.main.api import Component
+from openmdao.lib.datatypes.api import Float, Array, Bool
+
+#--------------------------------------------------------------------------------------------
+# Input Parameters
+class SEAM_AEP(Component):
+    NWspPC = Float(iotype = 'in', desc = 'number of sections')
+    MeanWSP = Float(iotype = 'in', units = 'm/s', desc = 'mean wind speed')    # [m/s]
+    AirDensity = Float(iotype = 'in', units = 'kg/m**3', desc = 'density of air') # [kg / m^3]
+    TurbulenceInt = Float(iotype = 'in', desc = '')     # Fraction
+    RatedPower = Float(iotype = 'in', units = 'MW', desc = 'rated power')          # [MW]
+    Diameter = Float(iotype = 'in', units = 'm', desc = 'diameter')          # [m]
+    MaxCp = Float(iotype = 'in', desc = 'max CP')            # Fraction
+    GearLossConst = Float(iotype = 'in', desc = '')    # Fraction
+    GearLossVar = Float(iotype = 'in', desc = '')     # Fraction
+    GenLoss = Float(iotype = 'in', desc = '')          # Fraction
+    ConvLoss = Float(iotype = 'in', desc = '')         # Fraction
+
+    WeibullInput = Float(iotype = 'in', desc = '')        # 1(true) or 0 (false) if true WeiA and WeiC overrules MeanWSP. If false MeanWSP is used with Rayleigh distribution
+    WeiA_input = Float(iotype = 'in', units = 'm/s', desc = '')       #[m/s]
+    WeiC_input = Float(iotype = 'in', desc = '')       #[-]
+
+    min_wsp = Float(0.0, iotype = 'in', units = 'm/s', desc = 'min wind speed')  #[m/s]
+    max_wsp = Float(iotype = 'in', units = 'm/s', desc = 'max wind speed') #[m/s]
+
+    NYears = Float(iotype = 'in', desc = 'Operating years')
+
+    AEP = Float(iotype = 'out', desc = 'Anual Electricity Output')
+    Total_AEP = Float(iotype = 'out', desc = 'AEP for total years of production')
+
+    def execute(self):
+
+        # Local Variables
+        # Procedure CalcPowerCurve
+        wsp = np.linspace(self.min_wsp, self.max_wsp, self.NWspPC)
+
+        P_aero = 0.5*self.AirDensity*(np.pi*(self.Diameter/2.)**2)*wsp**3*self.MaxCp/1000. #[kW]
+
+        P_gear = (P_aero-self.GearLossConst*self.RatedPower*1000-P_aero*self.GearLossVar)
+        for i, value in enumerate(P_gear):
+            if value < 0:
+                P_gear[i] = 0
+
+        P_gen = P_gear*(1-self.GenLoss)
+
+        P_conv = P_gen*(1-self.ConvLoss)
+
+        P_raw = np.zeros(P_conv.shape[0])
+        for i, value in enumerate(P_conv):
+            if value < self.RatedPower*1000:
+                P_raw[i] = P_conv[i]
+            else:
+                P_raw[i] = self.RatedPower*1000
+
+        #-------------------------------------------------------------------------------------------
+
+        def NDist(x, my, std):
+            NDist = (1./(np.sqrt(2*np.pi)*std))*np.exp(-(x-my)*(x-my)/(2.*std**2))
+            return NDist
+
+        #-------------------------------------------------------------------------------------------
+        # TurbulenceCorrection
+        NormDist = np.zeros((self.NWspPC-1,self.NWspPC))
+
+        sigma = wsp*self.TurbulenceInt
+
+        ProbSum = np.zeros(P_conv.shape[0])
+
+        for i in range(0,int(self.NWspPC)-1):
+         for j in range(0,int(self.NWspPC)):
+             NormDist[i][j] = NDist(wsp[j], wsp[i+1], sigma[i+1])   #Hvorfor Sigma med stort?
+             ProbSum[i] = ProbSum[i]+NormDist[i,j]
+
+        P_turb = np.zeros(P_conv.shape[0])
+        for i in range(0,int(self.NWspPC)):
+            P_turb[i] = 0
+        for i in range(1,int(self.NWspPC)):
+            for j in range(0,int(self.NWspPC)):
+                P_turb[i]=P_turb[i]+P_raw[j]*NormDist[i-1,j]/ProbSum[i-1]
+
+        #-------------------------------------------------------------------------------------------
+        # IntegratePC
+        if self.WeibullInput == 0: # False
+            self.WeibullC = 2.0
+            self.WeibullA = 1.1284*self.MeanWSP
+        elif self.WeibullInput == 1: # True
+            self.WeibullC = self.WeiC_input
+            self.WeibullA = self.WeiA_input
+
+        NHours = np.zeros(P_conv.shape[0])
+        NHours[0] = 8760*((1-np.exp(-np.exp(self.WeibullC*np.log((wsp[0]+0.5)/self.WeibullA)))))
+        for i in range(1,int(self.NWspPC)):
+            NHours[i] = 8760*((1-np.exp(-np.exp(self.WeibullC*np.log((wsp[i]+0.5)/self.WeibullA))))-(1-np.exp(-np.exp(self.WeibullC*np.log((wsp[i]-0.5)/self.WeibullA)))))
+        AEPprWSP = P_turb * NHours
+
+        AEPSum = np.sum(AEPprWSP/1000000.)
+        print 'AEP', AEPSum
+
+        Total_AEP = AEPSum*self.NYears
+        print 'Total AEP', Total_AEP, 'GWh'
+
+#Just to check if the script worked
+# if __name__ == '__main__':
+#
+#     top = SEAM_AEP()
+#     top.NWspPC = 26
+#     top.MeanWSP = 6             # [m/s]
+#     top.AirDensity = 1.225      # [kg / m^3]
+#     top.TurbulenceInt = 0.1     # Fraction
+#     top.RatedPower = 3          # [MW]
+#     top.Diameter = 101          # [m]
+#     top.MaxCp = 0.49            # Fraction
+#     top.GearLossConst = 0.01    # Fraction
+#     top.GearLossVar = 0.014     # Fraction
+#     top.GenLoss = 0.03          # Fraction
+#     top.ConvLoss = 0.03         # Fraction
+#
+#     top.WeibullInput = 1        # 1(true) or 0 (false) if true WeiA and WeiC overrules MeanWSP. If false MeanWSP is used with Rayleigh distribution
+#     top.WeiA_input = 11.       #[m/s]
+#     top.WeiC_input = 2.00       #[-]
+#
+#     top.min_wsp = 0  #[m/s]
+#     top.max_wsp = 25 #[m/s]
+#
+#     top.NYears = 20
+#
+#     top.execute()
+