Python/Seminar_Smartgrids
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Modell funktioniert, Jahr 2019 eingestellt

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patha
2025-01-01 12:08:07 +01:00
parent abe1c9d83d
commit eb130b6be2
5 changed files with 8863 additions and 8854 deletions
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1
.gitignore vendored
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__pycache__/
production_profile.csv

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Lastprofil_final_H0.csv
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11
main.py
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from neighborhood import Producer, Consumer, Neighborhood
# Anzahl Haushalte ohne PV-Anlagen
num_consumer = 50
avg_consumption_per_consumer = 10
num_consumer = 5
# Anzahl Haushalte mit PV-Anlagen
num_producer = 10
avg_production_per_producer = 20
num_producer = 5
# Instanzen für Erzeuger und Verbraucher anlegen
consumer = Consumer(num_consumer, avg_consumption_per_consumer)
producer = Producer(num_producer, avg_consumption_per_consumer, avg_production_per_producer)
producer = Producer(num_producer)
consumer = Consumer(num_consumer)
# Instanz für Nachbarschaft anlegen und Ergebnis plotten
neighborhood = Neighborhood(producer, consumer)

105
neighborhood.py
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@@ -1,85 +1,88 @@
import numpy as np
import matplotlib.pyplot as plt
import pv_input as pv
import pandas as pd
class Consumer:
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):
self.quantity = quantity
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()
# Vebrauchsprofil aus CSV-Datei in Dataframe einlesen und die Leistungs-Series extrahieren
def create_consumption_profile(self):
profile = np.zeros(24)
peak_hours = [8,12,13,18,19,20,21]
for hour in peak_hours:
profile[hour] = 1.5
for hour in range(24):
if hour not in peak_hours:
profile[hour] = 0.5
return profile
consumption_profile = pd.read_csv('Lastprofil_final_H0.csv',delimiter=';')
return consumption_profile['Leistung']
# Verbrauchsprofil mit der Anzahl der 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)
peak_hours = [8,9,10,11,12,13,14,15,16,17,18,19]
for hour in peak_hours:
profile[hour] = 1.5
for hour in range(24):
if hour not in peak_hours:
profile[hour] = 0
return profile
production_profile = pv.create_production_profile()
production_profile.to_csv('production_profile.csv', sep=';',header=['Leistung'])
production_profile = pd.read_csv('production_profile.csv',delimiter=';')
return production_profile['Leistung']
# Erzeugungsprofil mit der Anzahl der Haushalte multiplizieren
def calculate_final_production(self):
final_production = self.production_profile.mul(self.quantity)
return final_production
class Consumer:
def __init__(self, quantity):
self.quantity = quantity
self.consumption_profile = self.create_consumption_profile()
self.final_consumption = self.calculate_final_consumption()
# Vebrauchsprofil aus CSV-Datei in Dataframe einlesen und die Leistungs-Series extrahieren, Werte in Watt umrechnen und mit 4 multiplizieren, da Originalwerte 15-minütlich waren
def create_consumption_profile(self):
consumption_profile = pd.read_csv('Lastprofil_final_H0.csv',delimiter=';')
return consumption_profile['Leistung']*1000*4
# Verbrauchsprofil mit der Anzahl der 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):
self.producer = producer
self.consumer = consumer
# Gesamterzeugung, Gesamtverbrauch und Nettoverbrauch plotten
def plot_consumption(self):
total_consumption = -1*(self.consumer.calculate_daily_consumption() + self.producer.calculate_daily_consumption())
total_production = self.producer.calculate_daily_production()
total_consumption = -1*(self.consumer.final_consumption + self.producer.final_consumption)
total_production = self.producer.final_production
net_value = total_consumption + total_production
# 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']
# 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]
# Plot dimensionieren
plt.figure(figsize=(10, 6))
# y-Werte den x-Werten zuordnen
plt.plot(x, total_consumption, '--', x, total_production, '--', x, net_value, '-')
plt.bar(x, total_consumption)
plt.bar(x, total_production)
plt.bar(x, net_value)
#plt.plot(x, net_value, '-')
# Titel und Achsenbeschriftungen anlegen
plt.xlabel('Stunde')
plt.ylabel('Strom (kWh)')
plt.title('Produktion/Verbrauch Nachbarschaft')
# X-Werten die Labels zuordnen
plt.xticks(x, x_labels)
# 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')
# X-Labels rotieren

64
pv_input.py
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@@ -39,44 +39,48 @@ surface_azimuth = 180
# Temperaturparameter definieren
temperature_parameters = TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_glass']
# 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,
modules_per_string=modules_per_string, strings_per_inverter=strings_per_inverter)
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,
modules_per_string=modules_per_string, strings_per_inverter=strings_per_inverter)
modelchain = ModelChain(system, location)
modelchain = ModelChain(system, location)
# Ertragssimulation mit Clear-Sky Modell
# Ertragssimulation mit Clear-Sky Modell
# Index-Spalte mit Zeiten für clear-sky Dataset anlegen
# times = pd.date_range(start='2020-01-01', end ='2020-12-31', freq='1h', tz=location.tz)
# Index-Spalte mit Zeiten für clear-sky Dataset anlegen
# times = pd.date_range(start='2020-01-01', end ='2020-12-31', freq='1h', tz=location.tz)
# clear_sky = location.get_clearsky(times)
# #clear_sky.plot(figsize=(16,9))
# clear_sky = location.get_clearsky(times)
# #clear_sky.plot(figsize=(16,9))
# modelchain.run_model(clear_sky)
# modelchain.run_model(clear_sky)
# Ertragssimulation mit realen Strahlungsdaten aus Wetterjahr
# 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-180, 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)
# 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-180, 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']
# 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')
# 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)
# 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()
# Ergebnis plotten
#modelchain.results.ac.plot(figsize=(16,9))
#plt.show()
# Rückgabewert
return modelchain.results.ac