soc predictor wip

Utilities/BESS.py

@@ -26,5 +26,19 @@ def discharge_bess(bess, site_name, dt, discharge_power):
         if unit["site"] == site_name:
             new_soc = unit["SoC"] - (dt * discharge_energy) / unit["capacity_kWh"]
             new_soc = 0 if new_soc < 0 else new_soc
+        else:
+            new_soc = unit["SoC"]
+
+        # update SoC
         bess["units"][index]["SoC"] = new_soc
     return bess
+
+
+def predict_swap_time(bess_soc_for_cycle):
+    """Predict the swap time for each BESS unit based on its SoC history."""
+    swap_times = {}
+    for unit_name, df in bess_soc_for_cycle.items():
+        # Find the timestamp when SoC reaches 0
+        swap_time = df[df["SoC"] == 0]["Timestamp"].min()
+        swap_times[unit_name] = swap_time
+    return swap_times

YAMLs/BESS.yml

@@ -14,3 +14,9 @@ units:
   - name: MBESS 5
     capacity_kWh: 2096
     c-rate: 0.5
+  - name: MBESS 6
+    capacity_kWh: 2096
+    c-rate: 0.5
+buffer:
+  unit: percentage of buffer
+  min: 0.1

main.py

@@ -2,11 +2,17 @@ import numpy as np
 import yaml
 from Utilities.Time import get_start_time
 from Utilities.LoadProfile import get_load_profiles
-from Utilities.BESS import initialise_SoC, initial_site_assignment, discharge_bess
+from Utilities.BESS import (
+    initialise_SoC,
+    initial_site_assignment,
+    discharge_bess,
+    predict_swap_time,
+)
 import matplotlib.pyplot as pl
 import pandas as pd
 from concurrent.futures import ThreadPoolExecutor
 
+
 # read config file
 c = yaml.safe_load(open("YAMLs/config.yml"))
 
@@ -41,7 +47,13 @@ def generate_and_cache_profiles(c, dt):
 bess_data = initialise_SoC(bess_data)
 bess_data = initial_site_assignment(c, bess_data)
 # bess SoC dataframe
-bess_soc = pd.DataFrame(columns=[unit["name"] for unit in bess_data["units"]])
+bess_soc_since_start = pd.DataFrame(
+    columns=[unit["name"] for unit in bess_data["units"]]
+)
+# bess SoC dictionary, meant to track SoC progress over each cycle.
+# resets after each charging cycle. This is for predicting swap times.
+init_df = pd.DataFrame(columns=["Timestamp", "SoC"])
+bess_soc_for_cycle = {unit["name"]: init_df for unit in bess_data["units"]}
 
 # get initial load profiles
 cumulative_load_profiles = get_load_profiles(
@@ -71,16 +83,38 @@ with ThreadPoolExecutor() as executor:
         temp_soc = [unit["SoC"] for unit in bess_data["units"]]
 
         # append SoC to dataframe
-        bess_soc = pd.concat(
+        bess_soc_since_start = pd.concat(
             [
-                bess_soc,
+                bess_soc_since_start,
                 pd.DataFrame(
-                    [temp_soc], columns=bess_soc.columns, index=[timestamps[i]]
+                    [temp_soc],
+                    columns=bess_soc_since_start.columns,
+                    index=[timestamps[i]],
                 ),
             ],
             axis=0,
         )
 
+        # assign SoC for cycle
+        for unit in bess_data["units"]:
+            unit_name = unit["name"]
+            # reset df if SoC is 0. Start a new cycle
+            if unit["SoC"] == 0:
+                bess_soc_for_cycle[unit_name] = init_df
+            bess_soc_for_cycle[unit_name] = pd.concat(
+                [
+                    bess_soc_for_cycle[unit_name],
+                    pd.DataFrame(
+                        [[timestamps[i], unit["SoC"]]],
+                        columns=["Timestamp", "SoC"],
+                    ),
+                ],
+                axis=0,
+            )
+
+        # predict swap times
+        swap_times = predict_swap_time(bess_soc_for_cycle)
+
         # add to cumulative load profiles
         # check if future exists and is done
         if is_running_in_async:
@@ -96,6 +130,6 @@ with ThreadPoolExecutor() as executor:
                 print(len(cumulative_load_profiles), "load profiles generated")
                 is_running_in_async = False
 
-pl.plot(bess_soc.index, bess_soc.values, label="BESS SoC", alpha=0.5)
+pl.plot(cumulative_load_profiles)
 pl.show()
-pl.xlabel("Time (s since epoch)")
+pl.plot(bess_soc_since_start)