VictorMylle/Thesis
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Added notebook to sample from predictions and load model

Browse Source
This commit is contained in:
Victor Mylle
2023-12-18 21:12:14 +00:00
parent c437c23566
commit fee948cc09
11 changed files with 1226 additions and 236 deletions
Download Patch File Download Diff File Expand all files Collapse all files

454
src/policies/policy_tester.ipynb Normal file
View File

File diff suppressed because one or more lines are too long

79
src/policies/simple_baseline.py
View File

@@ -17,32 +17,18 @@ class Battery:
self.power = power
self.charge_cycles = 0
self.charging = False
self.discharging = False
def simulate(self):
"""
Simulate the battery for one time step (one quarter of an hour)
"""
if self.charging:
return self.charge()
elif self.discharging:
return self.discharge()
return 0
def discharge(self):
"""
Discharge the battery by one time step (one quarter of an hour)
"""
if self.current_charge == 0:
return 0
self.discharging = True
self.current_charge -= self.power / 4
self.charge_cycles += 1/16
if self.current_charge <= 0:
self.current_charge = 0
self.discharging = False
self.charge_cycles += 1
return self.power / 4
@@ -52,12 +38,12 @@ class Battery:
"""
if self.current_charge == self.capacity:
return 0
self.charging = True
self.current_charge += self.power / 4
self.charge_cycles += 1/16
if self.current_charge >= self.capacity:
self.current_charge = self.capacity
self.charging = False
return self.power / 4
@@ -89,7 +75,7 @@ class BaselinePolicy():
imbalance_prices = imbalance_prices.loc[imbalance_prices['DateTime'].dt.year >= 2020]
else:
imbalance_prices = imbalance_prices.loc[imbalance_prices['DateTime'].dt.year == 2023]
imbalance_prices = imbalance_prices.sort_values(by=['DateTime'])
imbalance_prices = imbalance_prices.sort_values(by=['DateTime'], ascending=True)
return imbalance_prices
def get_train_score(self, charge_treshold, discharge_treshold):
@@ -98,42 +84,53 @@ class BaselinePolicy():
def get_test_score(self, charge_treshold, discharge_treshold):
return self.get_score(self.test_data, charge_treshold, discharge_treshold)
# if price is below treshold -> charge battery: total_profit -= charge * price
# if price is below charging treshold (cheap charging) -> charge battery: total_profit -= charge * price
# if price is above treshold -> discharge battery: total_profit += discharge * price
def get_score(self, df, charge_treshold, discharge_treshold):
self.battery.reset()
total_profit = 0
total_charging_cost = 0
total_discharging_profit = 0
mean_charging_price = 0
mean_discharging_price = 0
number_of_charges = 0
number_of_discharges = 0
for index, row in df.iterrows():
if self.battery.charging:
total_profit -= self.battery.simulate() * row['Positive imbalance price']
elif self.battery.discharging:
total_profit += self.battery.simulate() * row['Positive imbalance price']
else:
if row['Positive imbalance price'] < charge_treshold:
total_profit -= self.battery.charge() * row['Positive imbalance price']
elif row['Positive imbalance price'] > discharge_treshold:
total_profit += self.battery.discharge() * row['Positive imbalance price']
if row['Positive imbalance price'] < charge_treshold:
total_charging_cost += self.battery.charge() * row['Positive imbalance price']
mean_charging_price += row['Positive imbalance price']
number_of_charges += 1
elif row['Positive imbalance price'] > discharge_treshold:
total_discharging_profit += self.battery.discharge() * row['Positive imbalance price']
mean_discharging_price += row['Positive imbalance price']
number_of_discharges += 1
return total_profit, self.battery.charge_cycles
return total_charging_cost, total_discharging_profit, self.battery.charge_cycles, mean_charging_price / number_of_charges, mean_discharging_price / number_of_discharges
def treshold_scores(self, charge_tresholds, discharge_tresholds):
df = pd.DataFrame(columns=["Charge treshold", "Discharge treshold", "Total Profit", "Charge cycles"])
df = pd.DataFrame(columns=["Charge treshold", "Discharge treshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])
df_test = pd.DataFrame(columns=["Charge treshold", "Discharge treshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])
for charge_treshold, discharge_treshold in tqdm(itertools.product(charge_tresholds, discharge_tresholds)):
total_profit, charge_cycles = self.get_train_score(charge_treshold, discharge_treshold)
df = pd.concat([df, pd.DataFrame([[charge_treshold, discharge_treshold, total_profit, charge_cycles]], columns=["Charge treshold", "Discharge treshold", "Total Profit", "Charge cycles"])])
total_charging_cost, total_discharge_profit, charge_cycles, mean_charging_price, mean_discharging_price = self.get_train_score(charge_treshold, discharge_treshold)
df = pd.concat([df, pd.DataFrame([[charge_treshold, discharge_treshold, total_charging_cost, total_discharge_profit, total_discharge_profit - total_charging_cost, charge_cycles, mean_charging_price, mean_discharging_price]], columns=["Charge treshold", "Discharge treshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])])
total_charging_cost, total_discharge_profit, charge_cycles, mean_charging_price, mean_discharging_price = self.get_test_score(charge_treshold, discharge_treshold)
df_test = pd.concat([df_test, pd.DataFrame([[charge_treshold, discharge_treshold, total_charging_cost, total_discharge_profit, total_discharge_profit - total_charging_cost, charge_cycles, mean_charging_price, mean_discharging_price]], columns=["Charge treshold", "Discharge treshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])])
df = df.sort_values(by=['Total Profit'], ascending=False)
return df
return df, df_test
battery = Battery(2, 1)
policy = BaselinePolicy(battery)
# charge_tresholds = [0, 50, 100, 150, 200, 250, 300, 350]
# discharge_tresholds = [0, 50, 100, 150, 200, 250, 300, 350]
# df = policy.treshold_scores(charge_tresholds, discharge_tresholds)
# print(df.to_markdown())
charge_tresholds = np.arange(-100, 250, 50)
discharge_tresholds = np.arange(-100, 250, 50)
print(policy.get_test_score(150, 100))
df, df_test = policy.treshold_scores(charge_tresholds, discharge_tresholds)
print(df.to_markdown())
print(df_test.to_markdown())
# print(policy.get_test_score(150, 100))