wip BESS discharging function

Utilities/BESS.py

@@ -0,0 +1,30 @@
+def initialise_SoC(bess):
+    """Initialise the state of charge (SoC) for the BESS."""
+    for i in range(0, len(bess["units"])):  # initially fully charged
+        bess["units"][i]["SoC"] = 1
+    return bess
+
+
+def initial_site_assignment(c, bess):
+    """Initialise the site assignment for each BESS."""
+    k = 0
+    while k < len(c["site_info"]["sites"]):
+        bess["units"][k]["site"] = c["site_info"]["sites"][k]["name"]
+        k += 1
+
+    if k < len(c["site_info"]["sites"]):
+        bess["units"][k]["site"] = "Unassigned"
+    return bess
+
+
+def discharge_bess(bess, site_name, dt, discharge_power):
+    # convert discharge power to discharge energy (kW to kWh)
+    discharge_energy = discharge_power * dt / 3600
+
+    """Discharge the BESS for a specific site."""
+    for index, unit in enumerate(bess["units"]):
+        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
+            bess["units"][index]["SoC"] = new_soc
+    return bess

Utilities/LoadProfile.py

@@ -126,7 +126,7 @@ def get_load_profiles(c, dt, batch_start_time, batch_process_duration):
             1 - c["noise"]["range"], 1 + c["noise"]["range"], len(timestamps)
         )
 
-        # make every 2 seconds the same
+        # make every 2 minutes the same
         for i in range(0, len(noise), 2):
             noise[i : i + 2] = noise[i]
 
@@ -147,7 +147,7 @@ def get_load_profiles(c, dt, batch_start_time, batch_process_duration):
         )
 
         # baseline operating hour power is 40% higher than out-of-hours power
-        gain = 1.4
+        gain = 5
         assumed_operating_baseline_power = avg_out_of_hours_power * gain
         baseline_energy = avg_out_of_hours_power * (
             batch_process_duration_hours - no_of_operating_hours
@@ -167,7 +167,7 @@ def get_load_profiles(c, dt, batch_start_time, batch_process_duration):
         peak_profile = generate_peak_profile(idx_peak, c, site)
 
         # assign base load profile
-        load_profile[idx_operating_hours > 0] = avg_operating_hour_power
+        load_profile[idx_operating_hours > 0] = assumed_operating_baseline_power
         load_profile[idx_operating_hours == 0] = avg_out_of_hours_power
 
         # smoothen out sharp edges

YAMLs/BESS.yml

@@ -0,0 +1,16 @@
+units:
+  - name: MBESS 1
+    capacity_kWh: 2096
+    c-rate: 0.5
+  - name: MBESS 2
+    capacity_kWh: 2096
+    c-rate: 0.5
+  - name: MBESS 3
+    capacity_kWh: 2096
+    c-rate: 0.5
+  - name: MBESS 4
+    capacity_kWh: 2096
+    c-rate: 0.5
+  - name: MBESS 5
+    capacity_kWh: 2096
+    c-rate: 0.5

YAMLs/config.yml

@@ -8,3 +8,4 @@ noise:
 
 paths:
   site_info: YAMLs/site_info.yaml
+  bess: YAMLs/BESS.yml

YAMLs/site_info.yaml

@@ -35,5 +35,5 @@ peak_duration:
   min: 1
   max: 4
 out_of_hours_consumption:
-  min: 0.05
-  max: 0.15
+  min: 0.01
+  max: 0.08

main.py

@@ -1,12 +1,18 @@
+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
 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"))
 
+# read BESS data
+bess_data = yaml.safe_load(open(c["paths"]["bess"]))
+
 ## simulation time setup
 # get current time
 c["sim_start_time"] = get_start_time()
@@ -15,6 +21,7 @@ dt = c["sim_time"]["time_step_minutes"] * 60
 # compute end time based on duration in days
 duration = c["sim_time"]["duration_days"] * 24 * 60 * 60
 c["sim_end_time"] = c["sim_start_time"] + duration
+timestamps = np.arange(c["sim_start_time"], c["sim_end_time"] + 1, dt)
 
 # batch process hours in seconds
 c["sim_time"]["batch_process_seconds"] = c["sim_time"]["batch_process_hours"] * 60 * 60
@@ -22,17 +29,73 @@ c["sim_time"]["batch_process_seconds"] = c["sim_time"]["batch_process_hours"] *
 # load site info
 c["site_info"] = yaml.safe_load(open(c["paths"]["site_info"]))
 
-cumulative_load_profiles = pd.DataFrame()
 
-# loop through timesteps
-for i in range(
-    c["sim_start_time"], c["sim_end_time"], c["sim_time"]["batch_process_seconds"]
-):
-
-    # generate load profiles
-    load_profiles = get_load_profiles(
+def generate_and_cache_profiles(c, dt):
+    """Generates load profiles for all sites and caches them."""
+    return get_load_profiles(
         c, dt, c["sim_start_time"], c["sim_time"]["batch_process_seconds"]
     )
-    
-    # add to cumulative load profiles
-    cumulative_load_profiles = pd.concat([cumulative_load_profiles, load_profiles], axis=1
+
+
+# initialise BESS
+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"]])
+
+# get initial load profiles
+cumulative_load_profiles = get_load_profiles(
+    c, dt, c["sim_start_time"], c["sim_time"]["batch_process_seconds"]
+)
+
+# async function is running
+is_running_in_async = False
+
+# loop through
+with ThreadPoolExecutor() as executor:
+    for i in range(0, len(timestamps)):
+        # start generating load profiles 200 seconds before data required
+        if len(cumulative_load_profiles) <= len(timestamps):
+            if is_running_in_async is False:
+                # generate load profiles
+                future = executor.submit(generate_and_cache_profiles, c, dt)
+                is_running_in_async = True
+        else:
+            is_running_in_async = False
+
+        # discharge BESS for each site
+        for site in c["site_info"]["sites"]:
+            site_name = site["name"]
+            discharge_power = cumulative_load_profiles[site_name].iloc[i]
+            bess_data = discharge_bess(bess_data, site_name, dt, discharge_power)
+        temp_soc = [unit["SoC"] for unit in bess_data["units"]]
+
+        # append SoC to dataframe
+        bess_soc = pd.concat(
+            [
+                bess_soc,
+                pd.DataFrame(
+                    [temp_soc], columns=bess_soc.columns, index=[timestamps[i]]
+                ),
+            ],
+            axis=0,
+        )
+
+        # add to cumulative load profiles
+        # check if future exists and is done
+        if is_running_in_async:
+            if future.done():
+                load_profiles = future.result()
+                cumulative_load_profiles = pd.concat(
+                    [
+                        cumulative_load_profiles,
+                        load_profiles,
+                    ],
+                    axis=0,
+                )
+                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.show()
+pl.xlabel("Time (s since epoch)")