tuned params, vis

v2/architecture.py

@@ -3,12 +3,15 @@ import pandas as pd
 import copy
 import time
 from enum import IntEnum
+import matplotlib.pyplot as plt
 
 # it's really just a network pricing model
 from supplier import Supplier
 from data_processing import read_datasets, add_production_field, interpolate_and_join, SolarParameters
 
 
+DO_VIS = False
+
 STEPS_PER_HOUR = 12
 
 
@@ -95,9 +98,10 @@ class Decision(IntEnum):
 # mock class as usual
 # output_window_size is not yet used, always decides one timestep.
 class Decider:
-    def __init__(self):
+    def __init__(self, supplier):
         self.input_window_size = STEPS_PER_HOUR * 24 # day long window.
         self.random_seed = 0
+        self.supplier = supplier
 
     # prod_cons_pred is a dataframe starting at now, containing
     # fields Production and Consumption.
@@ -105,6 +109,17 @@ class Decider:
     # battery_model is just queried for capacity and current soc.
     # the method returns a pd.Series of Decisions as integers.
     def decide(self, prod_pred, cons_pred, battery_model):
+        return Decision.DISCHARGE
+        next_prod = prod_pred[0]
+        next_cons = cons_pred[0]
+        deficit = next_cons - next_prod
+        soc = battery_model.soc
+        if soc > 0.9:
+            return Decision.DISCHARGE
+        elif soc < 0.1 and OLCSO:
+            return Decision.PASSIVE
+        return 
+
         # assert len(prod_pred) == len(cons_pred) == self.input_window_size
         self.random_seed += 1
         self.random_seed %= 3 # dummy rotates between Decisions
@@ -232,11 +247,11 @@ def simulator(battery_model, supplier, prod_cons, decider):
         consumption_from_network_pandas_series,
         provide_detail=True)
     print(f"All in all we have paid {total_charge} to network.")
-    fifteen_minute_peaks = consumption_from_network_pandas_series.resample('15T').sum()
-    demand_charges = pd.Series(demand_charges, index=fifteen_minute_peaks.index)
-    demand_charges.plot()
-    import matplotlib.pyplot as plt
-    plt.show()
+
+    if DO_VIS:
+        demand_charges.plot()
+        plt.title("demand_charges")
+        plt.show()
 
     results = pd.DataFrame({'soc_series': soc_series, 'consumption_from_solar': consumption_from_solar_series,
                             'consumption_from_network': consumption_from_network_series,
@@ -256,7 +271,7 @@ def main():
     supplier.set_price_for_interval(9, 17, 150) # nine-to-five increased price.
     # peak_demand dimension is kWh, but it's interpreted as the full consumption
     # during a 15 minute timestep.
-    supplier.set_demand_charge(peak_demand=25, surcharge_per_kwh=200) # kWh in a 15 minutes interval, Ft/kWh
+    supplier.set_demand_charge(peak_demand=25, surcharge_per_kwh=500) # kWh in a 15 minutes interval, Ft/kWh
 
     parameters = SolarParameters()
 
@@ -270,15 +285,16 @@ def main():
     time_interval_h = time_interval_min / 60
     battery_model = BatteryModel(capacity_Ah=600, time_interval_h=time_interval_h)
 
-    decider = Decider()
+    decider = Decider(supplier)
 
     t = time.perf_counter()
     results = simulator(battery_model, supplier, all_data_with_predictions, decider)
     print("Simulation runtime", time.perf_counter() - t, "seconds.")
 
-    import matplotlib.pyplot as plt
-    results['soc_series'].plot()
-    plt.show()
+    if DO_VIS:
+        results['soc_series'].plot()
+        plt.title('soc_series')
+        plt.show()
 
 
 if __name__ == '__main__':

v2/supplier.py

@@ -71,7 +71,7 @@ class Supplier:
         time_steps_per_demand_charge_evaluation = 15 // demand_series.index.freq.n
         # fifteen_minute_peaks [kW] tells the maximum demand in a 15 minutes timeframe:
         fifteen_minute_demands_in_kwh = demand_series.resample('15T').sum() * step_in_hour
-        demand_charges = [self.demand_charge(demand_in_kwh) for demand_in_kwh in fifteen_minute_demands_in_kwh]
+        demand_charges = pd.Series([self.demand_charge(demand_in_kwh) for demand_in_kwh in fifteen_minute_demands_in_kwh], index=fifteen_minute_demands_in_kwh.index)
         total_demand_charge = sum(demand_charges)
         total_charge = consumption_charge + total_demand_charge
         if provide_detail: