Seminar_Smartgrids/neighborhood.py

import numpy as np
import matplotlib.pyplot as plt
import pv_input as pv
import pandas as pd

class Producer:
    def __init__(self, quantity, profile_type):
        self.quantity = quantity
        self.profile_type = profile_type
        self.consumption_profile = self.create_consumption_profile()
        self.production_profile = self.create_production_profile()
        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):
        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
    
    # Erzegungsprofil über PV_Input.py erstelln, in .csv schreiben und von dort dann die Leistungs-Series extrahieren
    def create_production_profile(self):
        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 entsprechenden Haushalte multiplizieren   
    def calculate_final_production(self):
        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
    


class Neighborhood:
    def __init__(self, producers, consumers):
        self.producers = producers if isinstance(producers, list) else [producers]
        self.consumers = consumers if isinstance(consumers, list) else [consumers]

    # Gesamterzeugung, Gesamtverbrauch und Nettoverbrauch plotten
    def plot_consumption(self):
        total_consumption = 0
        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]

        # Plot dimensionieren
        plt.figure(figsize=(15, 9))

        # Barplots anlegen
        plt.bar(x, total_consumption)
        plt.bar(x, total_production,color='orange')
           
        # Linienplot anlegen
        #plt.plot(x, net_value,'-',color='darkgreen')
        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(['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()