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Violinenplot zur Optimierung / Zielfunktionalen
Worst / Best Case im Vergleich (bei Erfüllung der Nebenbedingungen)
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@ -110,16 +110,17 @@ class optimization_problem:
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final_soc = ems.eauto.ladezustand_in_prozent() # Nimmt den SoC am Ende des Optimierungszeitraums
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strafe = 0.0
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if worst_case:
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strafe = abs(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) * self.strafe
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gesamtbilanz += strafe
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eauto_roi = max(0,(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) )
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individual.extra_data = (o["Gesamtbilanz_Euro"],o["Gesamt_Verluste"], eauto_roi )
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strafe = 0.0
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strafe = max(0,(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) * self.strafe )
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gesamtbilanz += strafe
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gesamtbilanz += o["Gesamt_Verluste"]/1000.0
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gesamtbilanz -= o["Gesamt_Verluste"]/1000.0
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else:
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strafe = max(0,(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) * self.strafe )
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gesamtbilanz += strafe
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gesamtbilanz += o["Gesamt_Verluste"]/1000.0
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return (gesamtbilanz,)
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@ -128,7 +129,7 @@ class optimization_problem:
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# Genetischer Algorithmus
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def optimize(self,start_solution=None):
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population = self.toolbox.population(n=100)
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population = self.toolbox.population(n=300)
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hof = tools.HallOfFame(1)
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stats = tools.Statistics(lambda ind: ind.fitness.values)
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@ -141,9 +142,19 @@ class optimization_problem:
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if start_solution is not None and start_solution != -1:
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population.insert(0, creator.Individual(start_solution))
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#algorithms.eaMuPlusLambda(population, self.toolbox, 100, 200, cxpb=0.3, mutpb=0.3, ngen=500, stats=stats, halloffame=hof, verbose=True)
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algorithms.eaSimple(population, self.toolbox, cxpb=0.8, mutpb=0.8, ngen=400, stats=stats, halloffame=hof, verbose=True)
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return hof[0]
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#algorithms.eaMuPlusLambda(population, self.toolbox, 100, 200, cxpb=0.4, mutpb=0.5, ngen=500, stats=stats, halloffame=hof, verbose=True)
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algorithms.eaSimple(population, self.toolbox, cxpb=0.4, mutpb=0.4, ngen=400, stats=stats, halloffame=hof, verbose=True)
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member = {"bilanz":[],"verluste":[],"nebenbedingung":[]}
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for ind in population:
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if hasattr(ind, 'extra_data'):
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extra_value1, extra_value2,extra_value3 = ind.extra_data
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member["bilanz"].append(extra_value1)
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member["verluste"].append(extra_value2)
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member["nebenbedingung"].append(extra_value3)
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return hof[0], member
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def optimierung_ems(self,parameter=None, start_hour=None,worst_case=False):
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@ -240,7 +251,7 @@ class optimization_problem:
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return self.evaluate(individual, ems, parameter,start_hour,worst_case)
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self.toolbox.register("evaluate", evaluate_wrapper)
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start_solution = self.optimize(start_params)
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start_solution, extra_data = self.optimize(start_params)
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best_solution = start_solution
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o = self.evaluate_inner(best_solution, ems,start_hour)
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eauto = ems.eauto.to_dict()
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@ -256,13 +267,8 @@ class optimization_problem:
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print(o)
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if worst_case:
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visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, self.prediction_hours,einspeiseverguetung_euro_pro_wh,filename="visualisierungsergebnisse_worst.pdf")
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os.system("scp visualisierungsergebnisse_worst.pdf andreas@192.168.1.135:")
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else:
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visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, self.prediction_hours,einspeiseverguetung_euro_pro_wh)
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os.system("scp visualisierungsergebnisse.pdf andreas@192.168.1.135:")
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visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, self.prediction_hours,einspeiseverguetung_euro_pro_wh,extra_data=extra_data)
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os.system("scp visualisierungsergebnisse.pdf andreas@192.168.1.135:")
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#print(eauto)
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return {"discharge_hours_bin":discharge_hours_bin, "eautocharge_hours_float":eautocharge_hours_float ,"result":o ,"eauto_obj":eauto,"start_solution":best_solution,"spuelstart":spuelstart_int}
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@ -9,7 +9,7 @@ from matplotlib.backends.backend_pdf import PdfPages
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from datetime import datetime
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def visualisiere_ergebnisse(gesamtlast, pv_forecast, strompreise, ergebnisse, discharge_hours, laden_moeglich, temperature, start_hour, prediction_hours,einspeiseverguetung_euro_pro_wh, filename="visualisierungsergebnisse.pdf"):
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def visualisiere_ergebnisse(gesamtlast, pv_forecast, strompreise, ergebnisse, discharge_hours, laden_moeglich, temperature, start_hour, prediction_hours,einspeiseverguetung_euro_pro_wh, filename="visualisierungsergebnisse.pdf", extra_data=None):
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#####################
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# 24h
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@ -118,6 +118,7 @@ def visualisiere_ergebnisse(gesamtlast, pv_forecast, strompreise, ergebnisse, d
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ax1 = plt.subplot(3, 2, 3)
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for hour, value in enumerate(discharge_hours):
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#if value == 1:
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print(hour)
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ax1.axvspan(hour, hour+1, color='red',ymax=value, alpha=0.3, label='Entlademöglichkeit' if hour == 0 else "")
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for hour, value in enumerate(laden_moeglich):
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#if value == 1:
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@ -168,6 +169,58 @@ def visualisiere_ergebnisse(gesamtlast, pv_forecast, strompreise, ergebnisse, d
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plt.close() # Schließt die Figure
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plt.figure(figsize=(14, 10))
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plt.subplot(1, 2, 1)
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f1 = np.array(extra_data["verluste"])
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f2 = np.array(extra_data["bilanz"])
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n1 = np.array(extra_data["nebenbedingung"])
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scatter = plt.scatter(f1, f2, c=n1, cmap='viridis')
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# Farblegende hinzufügen
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plt.colorbar(scatter, label='Nebenbedingung')
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pdf.savefig() # Speichert die komplette Figure im PDF
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plt.close() # Schließt die Figure
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plt.figure(figsize=(14, 10))
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filtered_verluste = np.array([v for v, n in zip(extra_data["verluste"], extra_data["nebenbedingung"]) if n < 0.01])
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filtered_bilanz = np.array([b for b, n in zip(extra_data["bilanz"], extra_data["nebenbedingung"]) if n< 0.01])
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beste_verluste = min(filtered_verluste)
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schlechteste_verluste = max(filtered_verluste)
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beste_bilanz = min(filtered_bilanz)
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schlechteste_bilanz = max(filtered_bilanz)
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data = [filtered_verluste, filtered_bilanz]
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labels = ['Verluste', 'Bilanz']
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# Plot-Erstellung
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fig, axs = plt.subplots(1, 2, figsize=(10, 6), sharey=False) # Zwei Subplots, getrennte y-Achsen
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# Erster Boxplot für Verluste
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#axs[0].boxplot(data[0])
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axs[0].violinplot(data[0],
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showmeans=True,
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showmedians=True)
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axs[0].set_title('Verluste')
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axs[0].set_xticklabels(['Verluste'])
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# Zweiter Boxplot für Bilanz
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axs[1].violinplot(data[1],
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showmeans=True,
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showmedians=True)
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axs[1].set_title('Bilanz')
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axs[1].set_xticklabels(['Bilanz'])
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# Feinabstimmung
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plt.tight_layout()
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pdf.savefig() # Speichert den aktuellen Figure-State im PDF
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plt.close()
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# plt.figure(figsize=(14, 10))
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# # Kosten und Einnahmen pro Stunde
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# plt.subplot(1, 2, 1)
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