* Long term simulation of the field, #33

adding python scripts for calculating trajectory and long term temperature into field
adjusting objective function for long term information

pyDMPC/ControlFramework/Heatdemand.py

@@ -4,6 +4,7 @@
 import xlrd
 import scipy
 from scipy.integrate import simps
+import os
 
 DATA_FILE = "C:/mst/Grafana/Grafana3.xlsx"
 
@@ -26,24 +27,38 @@ def read_excel_file(Grafana3):
 
 #calculation of heatflow in timestep
 dT = np.subtract(T_return, T_flow)
-Q_flow = np.asarray(cp_water*rho_water*(vol_flow/60000)*dT) #in kW
+Q_flow_geo = np.asarray(cp_water*rho_water*(vol_flow/60000)*dT) #in kW
+#COP = np.asarray((Q_flow + Pel)/Pel) #if COP(T)
+#COP = 3.5 #if COP = konst
+#
+#if Q_flow_geo >0:
+# Q_flow = np.asarray((COP/(COP-1))*Q_flow_geo)
+#else:
+# Q_flow = Q_flow_geo
 
 ##Plotting Q(time)
 #plt.plot(time, Q, lw=2)
 #plt.axis([0, 26768640, -7, 7])
 #plt.show()
 
-#calculating heating budget [kWh/year] of field
-Q_flow_heating = Q_flow.clip(min=0)
-Q_heating = scipy.integrate.simps(Q_flow_heating, time)/3600
-print("The heating budget for one year is", Q_heating, "kWh")
+np.savetxt("C:\mst\Grafana\Q_flow.csv", np.c_([time], [Q_flow_geo]), delimiter=",")
 
-#calculating cooling budget [kWh/year] of field
-Q_flow_cooling = Q_flow.clip(max=0)
-Q_cooling = scipy.integrate.simps(Q_flow_cooling, time)/3600
-print("The cooling budget for one year is", Q_cooling, "kWh")
 
-##Calculating field temperature
-#u = #heat transfer parameters borehole
-#T_field = np.sum(T_rohr, np.divide(Q_flow, u))
 
+#
+#
+#
+##calculating heating budget [kWh/year] of field
+#Q_flow_heating = Q_flow.clip(min=0)
+#Q_heating = scipy.integrate.simps(Q_flow_heating, time)/3600
+#print("The heating budget for one year is", Q_heating, "kWh")
+#
+##calculating cooling budget [kWh/year] of field
+#Q_flow_cooling = Q_flow.clip(max=0)
+#Q_cooling = scipy.integrate.simps(Q_flow_cooling, time)/3600
+#print("The cooling budget for one year is", Q_cooling, "kWh")
+#
+###Calculating field temperature
+##u = #heat transfer parameters borehole
+##T_field = np.sum(T_rohr, np.divide(Q_flow, u))
+#

pyDMPC/ControlFramework/Objective_Function.py

@@ -5,6 +5,7 @@
 
 import Init
 import numpy as np
+import pickle
 
 '''Global variables used for simulation handling'''
 # Variable indicating if the subsystem model is compiled
@@ -209,8 +210,11 @@ def Obj(values_DVs, BC, s):
  print("cost_total: " + str(cost_total))
  print("output: " + str(tout))
  else:
+ pickle_in = open("T_set.pickle","rb")
+ T_set = pickle.load(pickle_in)
+
  for tout in output_traj[0]:
- cost_total += Init.cost_factor*(values_DVs - s.Q_set)**2 + Init.cost_factor*(tout - s.T_set)**2
+ cost_total += Init.cost_factor*(values_DVs - s.Q_set)**2 + Init.cost_factor*(tout - T_set)**2
  #cost_total += max(0.01,cost_par[k])*Init.cost_factor + 100*(max(abs(tout-273-Init.set_point[0])-Init.tolerance,0))**2
  k += 1
 
@@ -219,6 +223,8 @@ def Obj(values_DVs, BC, s):
  print("cost_total: " + str(cost_total))
  print("output: " + str(tout))
 
+ pickle_out
+
  '''Temporary objective function value'''
  obj_fnc_vals = [1]
 

pyDMPC/ControlFramework/field_long.py

@@ -0,0 +1,12 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import xlrd
+import scipy
+from scipy.integrate import simps
+import pickle
+
+#field temperature regarding the longterm set temperature = trajectory
+T_set = 285*np.ones(3*365*24) #Vorlauftemperatur ins Feld (Soll)
+pickle_out = open("T_set.pickle","wb")
+pickle.dump(T_set, pickle_out)

pyDMPC/ControlFramework/trajectory.py

@@ -0,0 +1,30 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import xlrd
+import scipy
+from scipy.integrate import simps
+
+#Gebäudebedarf
+Q_need_heat = np.array([500, 600, 300, 100, 50, 10, 0, 5, 80, 250, 300, 450]) #in kWh/Monat
+Q_need_cold = np.array([25, 25, 110, 200, 400, 600, 650, 600, 450, 300, 150, 50]) #in kWh/Monat
+days = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]) #Tage im Monat: Jan 31, Feb 28, ...
+cp_water = 4.186 #in kJ/(kg K)
+m_flow = 0.5 #kg/s
+
+#Wärmebedarf
+#Annahme: jeder Tag in einem Monat x hat das selbe Lastprofil und hat denselben Energiebedarf (Q_flow=konst)
+day_heatneed = np.array(Q_need_heat/days) #Wärmebedarf eines Tages des Monats x
+Q_heatflow_day = np.array(day_heatneed/24) #benötigter übertragener Wärmestrom an Warmwasserkreislauf[kW]
+#welche Temperaturdifferenz ist für den benötigten Wärmestrom nötig
+dT_heat = np.array(Q_heatflow_day/(m_flow*cp_water))
+
+#Kältebedarf
+day_coldneed = np.array(Q_need_cold/days) #Wärmebedarf eines Tages des Monats x
+Q_coldflow_day = np.array(day_coldneed/24) #benötigter übertragener Wärmestrom an Warmwasserkreislauf[kW]
+#welche Temperaturdifferenz ist für den benötigten Kältestrom nötig
+dT_cold = np.array(Q_coldflow_day/(m_flow*cp_water))
+
+#effektiver Wärme/Kältebedarf eines Tages eines Monats
+dQ_flow_day = np.array(Q_heatflow_day - Q_coldflow_day)
+dT = np.array(dQ_flow_day/(m_flow*cp_water))