Lastprofile angepasst, variable Zahl an Producer+Consumer

.gitignore

@@ -1 +1,2 @@
 __pycache__/
+production_profile.csv

Leitungssimulation.py

@@ -0,0 +1,22 @@
+import numpy as np
+import math
+import matplotlib as plt
+
+#Units are SI-Units except for cable cross-section
+
+def distance(x1,y1,x2,y2):
+    distance = math.sqrt(((x2-x1)^2)*((y2-y1)^2))
+    return distance
+
+def resistance(l,a,rho):
+    #Calculate the resistance between 2 coordinates
+    #Add 10% of length for various factors such as transitions and hanging lines
+    r = l*a*rho
+    return r
+
+def power_loss(resitance,voltage,power):
+    loss = (voltage^2)/resistance
+    return loss
+
+def run():
+    distance(1,2,3,4)

conversion.py

@@ -0,0 +1,18 @@
+import csv
+
+def process_csv(input_file, output_file):
+    n = -2
+    with open(input_file, mode='r', newline='') as infile:
+        reader = csv.reader(infile)
+        with open(output_file, mode='w', newline='') as outfile:
+            writer = csv.writer(outfile)
+            for row in reader:
+                n = n + 1
+                if n==4:
+                    writer.writerow(row)
+                    n = 0
+
+# Beispielhafte Verwendung
+input_file = 'Lastprofil_SWBT.csv'
+output_file = 'Lastprofil_final_H0.csv'
+process_csv(input_file, output_file)

main.py

@@ -1,17 +1,17 @@
 from neighborhood import Producer, Consumer, Neighborhood
 
-# Anzahl Haushalte ohne PV-Anlagen
+# Consumer-Instanzen anlegen
-num_consumer = 50
+standard_consumer = Consumer(quantity=10, profile_type='std')
-avg_consumption_per_consumer = 10
+wp_consumer = Consumer(quantity=0, profile_type='wp')
+ea_consumer = Consumer(quantity=0, profile_type='ea')
 
-# Anzahl Haushalte mit PV-Anlagen
-num_producer = 10
-avg_production_per_producer = 20
 
-# Instanzen für Erzeuger und Verbraucher anlegen
+# Producer-Instanz anlegen
-consumer = Consumer(num_consumer, avg_consumption_per_consumer)
+standard_producer = Producer(quantity=0, profile_type='std')
-producer = Producer(num_producer, avg_consumption_per_consumer, avg_production_per_producer)
+wp_producer = Producer(quantity=0, profile_type='wp')
+ea_producer = Producer(quantity=0, profile_type='ea')
+
 
 # Instanz für Nachbarschaft anlegen und Ergebnis plotten
-neighborhood = Neighborhood(producer, consumer)
+neighborhood = Neighborhood(producers=[standard_producer, wp_producer, ea_producer], consumers=[standard_consumer, wp_consumer, ea_consumer])
 neighborhood.plot_consumption()

neighborhood.py

@@ -1,90 +1,134 @@
 import numpy as np
 import matplotlib.pyplot as plt
-
+import pv_input as pv
-class Consumer:
+import pandas as pd
-    def __init__(self, quantity, average_consumption):
-        self.quantity = quantity
-        self.consumption_profile = self.create_consumption_profile()
-        self.average_consumption = average_consumption
-
-    def create_consumption_profile(self):
-        profile = np.zeros(24)
-        peak_hours = [12]
-        for hour in peak_hours:
-            profile[hour] = 1.5
-        for hour in range(24):
-            if hour not in peak_hours:
-                profile[hour] = 0.8
-        return profile
-    
-    def calculate_daily_consumption(self):
-        daily_consumption = self.quantity * self.average_consumption * self.consumption_profile
-        return daily_consumption
 
 class Producer:
-    def __init__(self, quantity, average_consumption, average_production):
+    def __init__(self, quantity, profile_type):
         self.quantity = quantity
+        self.profile_type = profile_type
         self.consumption_profile = self.create_consumption_profile()
-        self.average_consumption = average_consumption
         self.production_profile = self.create_production_profile()
-        self.average_production = average_production
+        self.final_consumption = self.calculate_final_consumption()
+        self.final_production = self.calculate_final_production()
 
+    # Vebrauchsprofile aus CSV-Datei in Dataframe einlesen und je nach profile_type die entsprechende Leistungs-Series extrahieren
     def create_consumption_profile(self):
-        profile = np.zeros(24)
+        if self.profile_type == 'std':
-        peak_hours = [8,12,13,18,19,20,21]
+            consumption_profile = pd.read_csv('Lastprofile_gesamt.csv',delimiter=';')
-        for hour in peak_hours:
+            return consumption_profile['Leistung']*1000
-            profile[hour] = 1.5
+        elif self.profile_type == 'wp':
-        for hour in range(24):
+            consumption_profile = pd.read_csv('Lastprofile_gesamt.csv',delimiter=';')
-            if hour not in peak_hours:
+            return consumption_profile['Leistung_WP']*1000
-                profile[hour] = 0.5
+        elif self.profile_type == 'ea':
-        return profile
+            consumption_profile = pd.read_csv('Lastprofile_gesamt.csv',delimiter=';')
+            return consumption_profile['Leistung_EA']*1000
     
+    # Verbrauchsprofil mit der Anzahl der entsprechenden Haushalte multiplizieren   
+    def calculate_final_consumption(self):
+        final_consumption = self.consumption_profile.mul(self.quantity)
+        return final_consumption
+    
+    # Erzegungsprofil über PV_Input.py erstelln, in .csv schreiben und von dort dann die Leistungs-Series extrahieren
     def create_production_profile(self):
-        profile = np.zeros(24)
+        production_profile = pv.create_production_profile()
-        peak_hours = [8,9,10,11,12,13,14,15,16,17,18,19]
+        production_profile.to_csv('production_profile.csv', sep=';',header=['Leistung'])
-        for hour in peak_hours:
+        production_profile = pd.read_csv('production_profile.csv',delimiter=';')
-            profile[hour] = 1.5
+        return production_profile['Leistung']
-        for hour in range(24):
+
-            if hour not in peak_hours:
+    # Erzeugungsprofil mit der Anzahl der entsprechenden Haushalte multiplizieren   
-                profile[hour] = 0
+    def calculate_final_production(self):
-        return profile
+        final_production = self.production_profile.mul(self.quantity)
+        return final_production
+    
+
+
+class Consumer:
+    def __init__(self, quantity, profile_type):
+        self.quantity = quantity
+        self.profile_type = profile_type
+        self.consumption_profile = self.create_consumption_profile()
+        self.final_consumption = self.calculate_final_consumption()
+
+    # Vebrauchsprofile aus CSV-Datei in Dataframe einlesen und je nach profile_type die entsprechende Leistungs-Series extrahieren
+    def create_consumption_profile(self):
+        if self.profile_type == 'std':
+            consumption_profile = pd.read_csv('Lastprofile_gesamt.csv',delimiter=';')
+            return consumption_profile['Leistung']*1000
+        elif self.profile_type == 'wp':
+            consumption_profile = pd.read_csv('Lastprofile_gesamt.csv',delimiter=';')
+            return consumption_profile['Leistung_WP']*1000
+        elif self.profile_type == 'ea':
+            consumption_profile = pd.read_csv('Lastprofile_gesamt.csv',delimiter=';')
+            return consumption_profile['Leistung_EA']*1000
+
+    
+    # Verbrauchsprofil mit der Anzahl der entsprechenden Haushalte multiplizieren
+    def calculate_final_consumption(self):
+        final_consumption = self.consumption_profile.mul(self.quantity)
+        return final_consumption
     
-    def calculate_daily_consumption(self):
-        daily_consumption = self.quantity * self.average_consumption * self.consumption_profile
-        return daily_consumption
 
-    def calculate_daily_production(self):
-        daily_production = self.quantity * self.average_production * self.production_profile
-        return daily_production
 
 class Neighborhood:
-    def __init__(self, producer, consumer):
+    def __init__(self, producers, consumers):
-        self.producer = producer
+        self.producers = producers if isinstance(producers, list) else [producers]
-        self.consumer = consumer
+        self.consumers = consumers if isinstance(consumers, list) else [consumers]
 
+    # Gesamterzeugung, Gesamtverbrauch und Nettoverbrauch plotten
     def plot_consumption(self):
-        total_consumption = -1*(self.consumer.calculate_daily_consumption() + self.producer.calculate_daily_consumption())
+        total_consumption = 0
-        total_production = self.producer.calculate_daily_production()
+        total_production = 0
+        # Verbrauch der Consumer subtrahieren
+        for consumer in self.consumers:
+            total_consumption -= consumer.final_consumption
+        # Zusätzlich Verbrauch der Producer subtrahieren
+        for producer in self.producers:
+            total_consumption -= producer.final_consumption
+        # Produktion der Producer addieren
+        for producer in self.producers:
+            total_production += producer.final_production
+        # Netto-Erzeugnis ausrechnen
         net_value = total_consumption + total_production
+        # Rollendes Mittel des netto-Erzeugnisses ausrechnen
+        net_value_mean = net_value.rolling(24).mean()
+        
+        
+        # X-Werte anlegen, einen Wert löschen da 8761 Werte vorhanden sind, aber nur 8760 benötigt werden
+        x = pd.date_range(start='2018-12-31', end ='2019-12-31', freq='1h')
+        x = x[:-1]
 
-        # X-Werte anlegen
-        x = range(24)
-        x_labels = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24']
         # Plot dimensionieren
-        plt.figure(figsize=(10, 6))
+        plt.figure(figsize=(15, 9))
-        # y-Werte den x-Werten zuordnen
+
-        plt.plot(x, total_consumption, '--', x, total_production, '--', x, net_value, '-')
+        # Barplots anlegen
-        # Titel und Achsenbeschriftungen anlegen
+        plt.bar(x, total_consumption)
-        plt.xlabel('Stunde')
+        plt.bar(x, total_production,color='orange')
-        plt.ylabel('Strom (kWh)')
+           
-        plt.title('Produktion/Verbrauch Nachbarschaft')
+        # Linienplot anlegen
-        # X-Werten die Labels zuordnen
+        #plt.plot(x, net_value,'-',color='darkgreen')
-        plt.xticks(x, x_labels)
+        plt.plot(x, net_value_mean,'-',color='darkgreen')
+
+        # Achsenbeschriftungen anlegen
+        plt.xlabel('Kalendertag')
+        plt.ylabel('Leistung (W)')
+
+        # Titel anlegen
+        #plt.title('Netto-leistung Nachbarschaft - Fall XXX')
+        plt.title('Erzeugung, Verbrauch und Netto-Leistung (Rollendes Mittel 24h) - Fall XXX')
+
+        # X-Ticks nur monatlich plotten
+        monthly_ticks = pd.date_range(start='2018-12-31', end ='2019-12-31', freq='1MS')
+        plt.xticks(monthly_ticks)
+
         # Legende anlegen
-        plt.legend(['Verbrauch', 'Produktion', 'Netto-Wert'], loc='upper right')
+        plt.legend(['Netto-Leistung', 'Leistung Verbrauch', 'Leistung Erzeugung'], loc='upper right')
+
         # X-Labels rotieren
         plt.xticks(rotation=45)
+
         # Grid anlegen
+        plt.axhline(linewidth=1, color='black')
         plt.grid(color='black', axis='y', linestyle='--', linewidth=0.5)
         plt.show()
 

pv_input.py

@@ -1,3 +1,13 @@
+# 3 Möglichkeiten für Ertragsberechnung: Clear Sky Modell, TMY oder POA-Data
+# Clear Sky: Strahlungsmodell, welches theoretische Strahlungsdaten an bestimmten Standpunkt enthält welche eine flache Oberfläche treffen  - vewerndet g(i)-Strahlungsdaten
+# TMY = Typical Meteorolical Year - reale Strahlungsdaten welche eine flache Oberfläche treffen  - vewerndet g(i)-Strahlungsdaten
+# POA = Plane of Array - was das PV-Modul (und dessen Ausrichtung) wirklich trifft - verwendet POA-Strahlungsdaten
+
+# G(i), poa_global = Global irradiance on inclined plane (W/m^2)
+# Gb(i), poa_direct = Beam (direct) irradiance on inclined plane (W/m^2)
+# Gd(i), poa_sky_diffuse = Diffuse irradiance on inclined plane (W/m^2)
+# Gr(i), poa_ground_diffuse = Reflected irradiance on inclined plane (W/m^2)
+
 import pvlib
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -6,22 +16,29 @@ from pvlib.location import Location
 from pvlib.pvsystem import PVSystem
 from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS
 
+# Daten Standort
 latitude = 47.2675
 longitude = 11.3910
 tz = 'Europe/Vienna'
-surface_tilt = 0
+year = 2019
-surface_azimuth = 180
 
+# Daten für Datenbank -> Module + WR
 database_module = pvlib.pvsystem.retrieve_sam('SandiaMod')
 database_inverter = pvlib.pvsystem.retrieve_sam('CECInverter')
-
 module = database_module['Canadian_Solar_CS5P_220M___2009_']
 inverter = database_inverter['ABB__PVI_4_2_OUTD_US__208V_']
+
+# PV-Anlage definieren
 modules_per_string = 10
 strings_per_inverter = 2
+surface_tilt = 20
+surface_azimuth = 150
 
+# Temperaturparameter definieren
 temperature_parameters = TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_glass']
 
+def create_production_profile(): 
+    # Location + PVSystem-Objekte anlegen und Modelchain damit füttern
     location = Location(latitude, longitude, tz)
     system = PVSystem(surface_tilt=surface_tilt, surface_azimuth=surface_azimuth, module_parameters=module, 
                     inverter_parameters=inverter, temperature_model_parameters=temperature_parameters, 
@@ -29,11 +46,39 @@ system = PVSystem(surface_tilt=surface_tilt, surface_azimuth=surface_azimuth, mo
 
     modelchain = ModelChain(system, location)
 
-times = pd.date_range(start='2021-07-01', end ='2021-07-07', freq='1min', tz=location.tz)
+    # Ertragssimulation mit Clear-Sky Modell
 
-clear_sky = location.get_clearsky(times)
+    # Index-Spalte mit Zeiten für clear-sky Dataset anlegen
-#clear_sky.plot(figsize=(16,9))
+    # times = pd.date_range(start='2020-01-01', end ='2020-12-31', freq='1h', tz=location.tz)
 
-modelchain.run_model(clear_sky)
+    # clear_sky = location.get_clearsky(times)
-modelchain.results.ac.plot(figsize=(16,9))
+    # #clear_sky.plot(figsize=(16,9))
-plt.show()
+
+    # modelchain.run_model(clear_sky)
+
+    # Ertragssimulation mit realen Strahlungsdaten aus Wetterjahr
+
+    # Hier ist Süden Azimuth = 0°, bei PVLib ist es 180°
+    poa_data, meta, inputs = pvlib.iotools.get_pvgis_hourly(latitude=latitude, longitude=longitude, start=year, end=year, raddatabase='PVGIS-SARAH3', components=True, surface_tilt=surface_tilt, 
+                                surface_azimuth=surface_azimuth-150, outputformat='json', usehorizon=True, userhorizon=None, pvcalculation=False, peakpower=None, 
+                                pvtechchoice='crystSi', mountingplace='free', loss=0, trackingtype=0, optimal_surface_tilt=False, optimalangles=False, 
+                                url='https://re.jrc.ec.europa.eu/api/', map_variables=True, timeout=30)
+
+    # Notwendige Spalten ausrechnen und hinzufügen, sodass Modelchain sie verwenden kann
+    poa_data['poa_diffuse'] = poa_data['poa_sky_diffuse'] + poa_data['poa_ground_diffuse']
+    poa_data['poa_global'] = poa_data['poa_diffuse'] + poa_data['poa_direct']
+
+    # Daten in csv exportieren
+    #poa_data.to_csv('poa_data.csv') 
+
+    # Index des Dataframe mit datetime-Index von Pandas überschreiben
+    poa_data.index = pd.to_datetime((poa_data.index))
+    # Diese Funktion benötigt POA-Daten anstatt g(i)-Daten -> DOKU
+    modelchain.run_model_from_poa(poa_data)
+
+    # Ergebnis plotten
+    #modelchain.results.ac.plot(figsize=(16,9))    
+    #plt.show()
+
+    # Rückgabewert
+    return modelchain.results.ac