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Code Issues Actions 4 Packages Projects Releases Wiki Activity

AC Charge Bug, Price Cache On/Off

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This commit is contained in:
Andreas
2024-10-20 18:18:06 +02:00
committed by Andreas
parent 7adf3d336a
commit ad550c874b
5 changed files with 61 additions and 33 deletions
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23
src/akkudoktoreos/class_ems.py
View File

@@ -95,21 +95,13 @@ class EnergieManagementSystem:
haushaltsgeraet_wh_pro_stunde[stunde_since_now] = ha_load
# E-Auto handling
if self.eauto:
if self.eauto and self.ev_charge_hours[stunde]>0:
geladene_menge_eauto, verluste_eauto = self.eauto.energie_laden(None, stunde, relative_power=self.ev_charge_hours[stunde])
# if self.ev_charge_hours[stunde] > 0.0:
# print(self.ev_charge_hours[stunde], " ", geladene_menge_eauto," ", self.eauto.ladezustand_in_prozent())
verbrauch += geladene_menge_eauto
verluste_wh_pro_stunde[stunde_since_now] += verluste_eauto
if self.eauto:
eauto_soc_pro_stunde[stunde_since_now] = self.eauto.ladezustand_in_prozent()
# AC PV Battery Charge
if self.ac_charge_hours[stunde] > 0.0:
self.akku.set_charge_allowed_for_hour(self.ac_charge_hours[stunde],stunde)
geladene_menge, verluste_wh = self.akku.energie_laden(None,stunde,relative_power=self.ac_charge_hours[stunde])
verbrauch += geladene_menge
verluste_wh_pro_stunde[stunde_since_now] += verluste_wh
# Process inverter logic
erzeugung = self.pv_prognose_wh[stunde]
self.akku.set_charge_allowed_for_hour(self.dc_charge_hours[stunde],stunde)
@@ -117,7 +109,14 @@ class EnergieManagementSystem:
self.wechselrichter.energie_verarbeiten(erzeugung, verbrauch, stunde)
)
# AC PV Battery Charge
if self.ac_charge_hours[stunde] > 0.0:
self.akku.set_charge_allowed_for_hour(1,stunde)
geladene_menge, verluste_wh = self.akku.energie_laden(None,stunde,relative_power=self.ac_charge_hours[stunde])
#print(stunde, " ", geladene_menge, " ",self.ac_charge_hours[stunde]," ",self.akku.ladezustand_in_prozent())
verbrauch += geladene_menge
netzbezug +=geladene_menge
verluste_wh_pro_stunde[stunde_since_now] += verluste_wh
netzeinspeisung_wh_pro_stunde[stunde_since_now] = netzeinspeisung
netzbezug_wh_pro_stunde[stunde_since_now] = netzbezug

43
src/akkudoktoreos/class_optimize.py
View File

@@ -31,6 +31,7 @@ class optimization_problem:
self.verbose = verbose
self.fix_seed = fixed_seed
self.optimize_ev = True
self.optimize_dc_charge = False
# Set a fixed seed for random operations if provided
if fixed_seed is not None:
@@ -63,7 +64,12 @@ class optimization_problem:
ac_charge = ac_charge / 5.0 # Normalize AC charge to range between 0 and 1
# Create dc_charge array: 7 = Not allowed (mapped to 0), 8 = Allowed (mapped to 1)
dc_charge = np.where(discharge_hours_bin == 8, 1, 0)
# Create dc_charge array: Only if DC charge optimization is enabled
if self.optimize_dc_charge:
dc_charge = np.where(discharge_hours_bin == 8, 1, 0)
else:
dc_charge = np.ones_like(discharge_hours_bin) # Set DC charge to 0 if optimization is disabled
# Create discharge array: Only consider value 1 (Discharge), set the rest to 0 (binary output)
discharge = np.where(discharge_hours_bin == 1, 1, 0)
@@ -95,7 +101,10 @@ class optimization_problem:
charge_discharge_mutated, = self.toolbox.mutate_charge_discharge(charge_discharge_part)
# Ensure that no invalid states are introduced during mutation (valid values: 0-8)
charge_discharge_mutated = np.clip(charge_discharge_mutated, 0, 8)
if self.optimize_dc_charge:
charge_discharge_mutated = np.clip(charge_discharge_mutated, 0, 8)
else:
charge_discharge_mutated = np.clip(charge_discharge_mutated, 0, 6)
# Use split_charge_discharge to split the mutated array into AC charge, DC charge, and discharge components
#ac_charge, dc_charge, discharge = self.split_charge_discharge(charge_discharge_mutated)
@@ -190,7 +199,11 @@ class optimization_problem:
# Initialize toolbox with attributes and operations
self.toolbox = base.Toolbox()
self.toolbox.register("attr_discharge_state", random.randint, 0,11)
if self.optimize_dc_charge:
self.toolbox.register("attr_discharge_state", random.randint, 0,8)
else:
self.toolbox.register("attr_discharge_state", random.randint, 0,6)
if self.optimize_ev:
self.toolbox.register("attr_ev_charge_index", random.randint, 0, len(possible_ev_charge_currents) - 1)
self.toolbox.register("attr_int", random.randint, start_hour, 23)
@@ -205,7 +218,10 @@ class optimization_problem:
#self.toolbox.register("mutate", tools.mutFlipBit, indpb=0.1)
# Register separate mutation functions for each type of value:
# - Discharge state mutation (-5, 0, 1)
self.toolbox.register("mutate_charge_discharge", tools.mutUniformInt, low=0, up=8, indpb=0.1)
if self.optimize_dc_charge:
self.toolbox.register("mutate_charge_discharge", tools.mutUniformInt, low=0, up=8, indpb=0.1)
else:
self.toolbox.register("mutate_charge_discharge", tools.mutUniformInt, low=0, up=6, indpb=0.1)
# - Float mutation for EV charging values
self.toolbox.register("mutate_ev_charge_index", tools.mutUniformInt, low=0, up=len(possible_ev_charge_currents) - 1, indpb=0.1)
# - Start hour mutation for household devices
@@ -234,7 +250,9 @@ class optimization_problem:
ems.set_akku_discharge_hours(discharge)
ems.set_akku_dc_charge_hours(dc)
# Set DC charge hours only if DC optimization is enabled
if self.optimize_dc_charge:
ems.set_akku_dc_charge_hours(dc)
ems.set_akku_ac_charge_hours(ac)
if self.optimize_ev:
@@ -242,8 +260,8 @@ class optimization_problem:
possible_ev_charge_currents[i] for i in eautocharge_hours_index
]
ems.set_ev_charge_hours(eautocharge_hours_float)
#else:
# ems.set_ev_charge_hours(np.full(self.prediction_hours, 0 ))
else:
ems.set_ev_charge_hours(np.full(self.prediction_hours, 0 ))
return ems.simuliere(start_hour)
def evaluate(
@@ -284,8 +302,11 @@ class optimization_problem:
)
# Adjust total balance with battery value and penalties for unmet SOC
restwert_akku = ems.akku.aktueller_energieinhalt() * parameter["preis_euro_pro_wh_akku"]
#print(ems.akku.aktueller_energieinhalt()," * ", parameter["preis_euro_pro_wh_akku"] , " ", restwert_akku, " ", gesamtbilanz)
gesamtbilanz += -restwert_akku
#print(gesamtbilanz)
if self.optimize_ev:
gesamtbilanz += max(
0,
@@ -318,8 +339,8 @@ class optimization_problem:
self.toolbox,
mu=100,
lambda_=150,
cxpb=0.7,
mutpb=0.3,
cxpb=0.6,
mutpb=0.4,
ngen=ngen,
stats=stats,
halloffame=hof,
@@ -412,8 +433,8 @@ class optimization_problem:
discharge_hours_bin, eautocharge_hours_float, spuelstart_int = self.split_individual(
start_solution
)
if self.optimize_ev:
eautocharge_hours_float = [possible_ev_charge_currents[i] for i in eautocharge_hours_float]
ac_charge, dc_charge, discharge = self.decode_charge_discharge(discharge_hours_bin)
# Visualize the results

21
src/akkudoktoreos/class_soc_calc.py
View File

@@ -5,7 +5,6 @@ import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
class BatteryDataProcessor:
def __init__(
self,
@@ -235,7 +234,7 @@ class BatteryDataProcessor:
marker="o",
label="100% SoC Points",
)
# plt.scatter(last_points_0_df['timestamp'], last_points_0_df['battery_voltage'], color='red', marker='x', label='0% SoC Points')
plt.scatter(last_points_0_df['timestamp'], last_points_0_df['battery_voltage'], color='red', marker='x', label='0% SoC Points')
plt.xlabel("Timestamp")
plt.ylabel("Voltage (V)")
plt.legend()
@@ -255,7 +254,7 @@ class BatteryDataProcessor:
marker="o",
label="100% SoC Points",
)
# plt.scatter(last_points_0_df['timestamp'], last_points_0_df['battery_current'], color='red', marker='x', label='0% SoC Points')
plt.scatter(last_points_0_df['timestamp'], last_points_0_df['battery_current'], color='red', marker='x', label='0% SoC Points')
plt.xlabel("Timestamp")
plt.ylabel("Current (A)")
plt.legend()
@@ -281,17 +280,23 @@ class BatteryDataProcessor:
if __name__ == "__main__":
# MariaDB Verbindungsdetails
config = {}
config = {
'user': 'soc',
'password': 'Rayoflight123!',
'host': '192.168.1.135',
'database': 'sensor'
}
# Parameter festlegen
voltage_high_threshold = 55.4 # 100% SoC
voltage_low_threshold = 46.5 # 0% SoC
voltage_low_threshold = 48 # 0% SoC
current_low_threshold = 2 # Niedriger Strom für beide Zustände
gap = 30 # Zeitlücke in Minuten zum Gruppieren von Maxima/Minima
bat_capacity = 33 * 1000 / 48
bat_capacity = 0.8*33 * 1000 / 48
# Zeitpunkt X definieren
zeitpunkt_x = (datetime.now() - timedelta(weeks=100)).strftime("%Y-%m-%d %H:%M:%S")
zeitpunkt_x = (datetime.now() - timedelta(weeks=4)).strftime("%Y-%m-%d %H:%M:%S")
# BatteryDataProcessor instanziieren und verwenden
processor = BatteryDataProcessor(
@@ -310,7 +315,7 @@ if __name__ == "__main__":
last_points_100_df, last_points_0_df
)
# soh_df = processor.calculate_soh(integration_results)
processor.update_database_with_soc(soc_df)
#processor.update_database_with_soc(soc_df)
processor.plot_data(last_points_100_df, last_points_0_df, soc_df)

6
src/akkudoktoreos/class_strompreis.py
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@@ -22,18 +22,20 @@ def repeat_to_shape(array, target_shape):
class HourlyElectricityPriceForecast:
def __init__(self, source, cache_dir="cache", charges=0.000228, prediction_hours=24): # 228
def __init__(self, source, cache_dir="cache", charges=0.000228, prediction_hours=24, cache=True): # 228
self.cache_dir = cache_dir
self.cache=cache
os.makedirs(self.cache_dir, exist_ok=True)
self.cache_time_file = os.path.join(self.cache_dir, "cache_timestamp.txt")
self.prices = self.load_data(source)
self.charges = charges
self.prediction_hours = prediction_hours
def load_data(self, source):
cache_filename = self.get_cache_filename(source)
if source.startswith("http"):
if os.path.exists(cache_filename) and not self.is_cache_expired():
if os.path.exists(cache_filename) and not self.is_cache_expired() and self.cache==True:
print("Loading data from cache...")
with open(cache_filename, "r") as file:
json_data = json.load(file)