battery swap method wip

.streamlit/config.toml

@@ -0,0 +1,34 @@
+[server]
+enableStaticServing = true
+
+[[theme.fontFaces]]
+family = "Exo2"
+url = "app/static/EXO2-VARIABLEFONT_WGHT.TTF"
+style = "normal"
+weight = 400
+
+
+[[theme.fontFaces]]
+family = "Exo2"
+url = "app/static/EXO2-BOLD.TTF"
+style = "bold"
+weight = 700
+
+[[theme.fontFaces]]
+family = "Exo2"
+url = "app/static/EXO2-ITALIC.TTF"
+style = "italic"
+weight = 400
+
+[[theme.fontFaces]]
+family = "Exo2"
+url = "app/static/EXO2-BOLDITALIC.TTF"
+style = "bold italic"
+weight = 7
+
+[theme]
+base="dark"
+primaryColor="#fcd913"
+font="Exo2"
+codeFont="Exo2"
+

.vscode/launch.json

@@ -0,0 +1,28 @@
+{
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "name": "🔍 Debug Streamlit",
+      "type": "debugpy",
+      "request": "launch",
+
+      // Tell VS Code to use `python -m streamlit run ...`
+      "module": "streamlit",
+
+      // Replace `app.py` (or dashboard.py) with your entry-point
+      "args": [
+        "run",
+        "dashboard.py",
+
+        // (optional but highly recommended) disable the auto-reloader
+        "--server.runOnSave=false"
+      ],
+
+      // so you can interact with the app and see logs
+      "console": "integratedTerminal",
+
+      // only step into *your* code, not the Streamlit internals
+      "justMyCode": true
+    }
+  ]
+}

Utilities/BESS.py

@@ -31,13 +31,13 @@ def discharge_bess(bess, site_name, dt, discharge_power):
             continue
 
         if unit["site"] == site_name:
-            new_soc = unit["SoC"] - (dt * discharge_energy) / unit["capacity_kWh"]
+            new_soc = unit["SoC"] - discharge_energy / unit["capacity_kWh"]
             new_soc = 0 if new_soc < 0 else new_soc
         else:
-            # maintain SoC if not assigned to the site
-            new_soc = unit["SoC"]
+            continue
 
-        # update SoC
+        # update SoC and current load
+        bess["units"][index]["current_load_kW"] = discharge_power
         bess["units"][index]["SoC"] = new_soc
     return bess
 
@@ -72,7 +72,7 @@ def predict_swap_time(bess_soc_for_cycle):
     return swap_times
 
 
-def update_cycle_SoC(bess_data, bess_soc_for_cycle, timestamps):
+def update_cycle_SoC(bess_data, bess_soc_for_cycle, timestamp):
     init_df = pd.DataFrame(columns=["Timestamp", "SoC"])
     # assign SoC for cycle
     for unit in bess_data["units"]:
@@ -85,20 +85,60 @@ def update_cycle_SoC(bess_data, bess_soc_for_cycle, timestamps):
             [
                 bess_soc_for_cycle[unit_name],
                 pd.DataFrame(
-                    [[timestamps[i], unit["SoC"]]],
+                    [[timestamp, unit["SoC"]]],
                     columns=["Timestamp", "SoC"],
                 ),
             ],
             axis=0,
         )
 
+    return bess_soc_for_cycle
 
-def arrange_swap(bess_data, c):
-    for unit in bess_data["units"]:
-        if unit["SoC"] < c["bess"]["buffer"]:
-            # find for unassigned BESS unit with SOC at 100%
-            for candidate in bess_data["units"]:
-                if candidate["SoC"] == 1 and candidate["site"] == "Unassigned":
-                    # assign the candidate to the site
-                    candidate["site"] = unit["site"]
-                    break
+
+def arrange_swap(c, bess_data, bess_soc_for_cycle):
+    # identify BESS units that need swapping
+    units_needing_swap = [
+        unit for unit in bess_data["units"] if unit["SoC"] < bess_data["buffer"]["min"]
+    ]
+
+    if not units_needing_swap:
+        return bess_data, bess_soc_for_cycle
+
+    # identify BESS units that are unassigned and fully charged
+    unassigned_fully_charged = [
+        unit
+        for unit in bess_data["units"]
+        if unit["SoC"] == 1 and unit["site"] == "Unassigned"
+    ]
+
+    if not unassigned_fully_charged:
+        return bess_data, bess_soc_for_cycle
+
+    # assign unassigned fully charged units to units needing swap
+    for unit in units_needing_swap:
+        # take the first unassigned fully charged unit
+        new_unit = unassigned_fully_charged.pop(0)
+        # assign it to the site of the unit needing swap
+        new_unit["site"] = unit["site"]
+        # reset SoC to 1 (fully charged)
+        new_unit["SoC"] = 1
+        # set current load to existing load
+        new_unit["current_load_kW"] = unit["current_load_kW"]
+
+        # reset old unit
+        unit["site"] = "Unassigned"  # mark the old unit as unassigned
+        unit["current_load_kW"] = 0  # reset current load
+
+        # update the BESS data
+        # search for the index of the unit needing swap and replace it with the new unit
+        index = next(
+            i for i, d in enumerate(bess_data["units"]) if d["name"] == unit["name"]
+        )
+        bess_data["units"][index] = new_unit
+        # search for index of new unit, and replace with old unit
+        new_index = next(
+            i for i, d in enumerate(bess_data["units"]) if d["name"] == new_unit["name"]
+        )
+        bess_data["units"][new_index] = unit
+
+    return bess_data, bess_soc_for_cycle

Utilities/DataVis.py

@@ -0,0 +1,67 @@
+import pandas as pd
+
+
+def format_dataframe(
+    bess_soc_for_cycle, bess_data, load_profiles_since_start, swap_time, current_time
+):
+    """Formats the DataFrame for display in the dashboard."""
+    # Create a DataFrame for sites
+    # columns = ["Site Name", "MBESS Unit", "Current Load (kW)", "SoC (%)", "Predicted Swap Time"]
+
+    status_df = pd.DataFrame(
+        columns=[
+            "Site Name",
+            "MBESS Unit",
+            "Current Load (kW)",
+            "SoC (%)",
+            "Predicted Swap Time",
+            "Estimated Time To Swap",
+            "Cycle Discharge Profile",
+            "Load Profile Since Start",
+        ]
+    )
+
+    for site in load_profiles_since_start.keys():
+        index = next(i for i, d in enumerate(bess_data["units"]) if d["site"] == site)
+        soc = bess_data["units"][index]["SoC"]
+        current_load = bess_data["units"][index]["current_load_kW"]
+        unit_name = bess_data["units"][index]["name"]
+        predicted_swap_time = swap_time.get(unit_name, "N/A")
+        # calculate estimated time to swap
+        if isinstance(predicted_swap_time, float):
+            estimated_time_to_swap = predicted_swap_time - current_time
+            estimated_time_to_swap = pd.to_timedelta(estimated_time_to_swap, unit="s")
+        else:
+            estimated_time_to_swap = "N/A"
+        # convert predicted_swap_time to a readable format
+        if isinstance(predicted_swap_time, float):
+            predicted_swap_time = pd.to_datetime(
+                predicted_swap_time, unit="s"
+            ).strftime("%Y-%m-%d %H:%M:%S")
+
+        status_df = pd.concat(
+            [
+                status_df,
+                pd.DataFrame(
+                    [
+                        {
+                            "Site Name": site,
+                            "MBESS Unit": unit_name,
+                            "Current Load (kW)": current_load,
+                            "SoC (%)": soc * 100,  # Convert to percentage
+                            "Predicted Swap Time": predicted_swap_time,
+                            "Estimated Time To Swap": estimated_time_to_swap,
+                            "Cycle Discharge Profile": bess_soc_for_cycle[unit_name][
+                                "SoC"
+                            ].tolist(),
+                            "Load Profile Since Start": load_profiles_since_start[
+                                site
+                            ].tolist(),
+                        }
+                    ]
+                ),
+            ],
+            ignore_index=True,
+        )
+
+    return status_df

dashboard.py

@@ -0,0 +1,111 @@
+# dashboard.py
+import streamlit as st
+import matplotlib.pyplot as pl
+import pandas as pd
+import main
+from main import (
+    start_sim,
+    stop_sim,
+    reset_sim,
+)
+import time
+
+
+st.set_page_config(layout="wide")
+
+# Header
+st.logo("https://rooftop.my/logo.svg", size="large")
+st.title("MEOS Control Dashboard")
+st.subheader("Mobile Energy Operations Simulation (MEOS)")
+st.text("Run MEOS Simulation and Monitor MBESS Status")
+
+# some instructions
+
+# --- SESSION STATE SETUP ---
+if "running" not in st.session_state:
+    st.session_state.running = False
+if "plot_area" not in st.session_state:
+    st.session_state.plot_area = st.empty()
+
+# --- CONTROL BUTTONS ---
+col1, col2, col3 = st.columns(3)
+with col1:
+    if st.button("Start", use_container_width=True):
+        start_sim()
+        st.session_state.running = True
+with col2:
+    if st.button("Stop", use_container_width=True):
+        stop_sim()
+        st.session_state.running = False
+with col3:
+    if st.button("Reset", use_container_width=True):
+        reset_sim()
+        st.session_state.running = False
+
+placeholder = st.empty()
+
+
+def show_table():
+    df = main.status_df
+    if df is None or df.empty:
+        placeholder.text("Waiting for first simulation step…")
+    else:
+        placeholder.dataframe(
+            df,
+            column_config={
+                "Site Name": st.column_config.TextColumn("Site Name"),
+                "MBESS Unit": st.column_config.TextColumn(
+                    "MBESS Unit", help="Name of the MBESS unit at the site"
+                ),
+                "Current Load (kW)": st.column_config.NumberColumn(
+                    "Current Load (kW)", help="Current BESS discharge load in kW"
+                ),
+                "SoC (%)": st.column_config.ProgressColumn(
+                    "State of Charge",
+                    help="State of Charge of the BESS unit",
+                    format="%.1f%%",
+                    min_value=0,
+                    max_value=100,
+                ),
+                "Predicted Swap Time": st.column_config.TextColumn(
+                    "Predicted Swap Time", help="Predicted time for BESS swap"
+                ),
+                "Cycle Discharge Profile": st.column_config.LineChartColumn(
+                    "Cycle Discharge Profile",
+                    help="Cycle discharge profile of the BESS unit",
+                ),
+                "Load Profile Since Start": st.column_config.LineChartColumn(
+                    "Load Profile Since Start",
+                    help="Load profile since the start of the simulation",
+                ),
+            },
+            use_container_width=True,
+        )
+
+
+if st.session_state.running:
+    # display simulation start time
+    st.metric(
+        "Simulation Start Time",
+        value=pd.to_datetime(main.c["sim_start_time"], unit="s").strftime(
+            "%Y-%m-%d %H:%M:%S"
+        ),
+    )
+    st.metric(
+        "Current Time",
+        value=pd.to_datetime(
+            main.c["sim_start_time"] + main.sim_i * main.dt, unit="s"
+        ).strftime("%Y-%m-%d %H:%M:%S"),
+    )
+
+    st.metric(
+        "Time Elapsed in DD:HH:MM:SS",
+        value=str(pd.to_timedelta(main.sim_i * main.dt, unit="s")),
+    )
+    # display BESS data, SoC, Load Consumption
+    show_table()
+    time.sleep(1)
+    st.rerun()
+else:
+    show_table()
+    st.info("Simulation not running")

main.py

@@ -3,16 +3,20 @@ import yaml
 from Utilities.Time import get_start_time
 from Utilities.LoadProfile import get_load_profiles
 from Utilities.BESS import (
+    arrange_swap,
     initialise_SoC,
     initial_site_assignment,
     discharge_bess,
     predict_swap_time,
     update_cycle_SoC,
 )
+from Utilities.DataVis import format_dataframe
 import matplotlib.pyplot as pl
 import pandas as pd
 from concurrent.futures import ThreadPoolExecutor
 
+import threading  ### <<< CONTROL ADDED >>>
+import time  ### <<< CONTROL ADDED >>>
 
 # read config file
 c = yaml.safe_load(open("YAMLs/config.yml"))
@@ -21,105 +25,139 @@ c = yaml.safe_load(open("YAMLs/config.yml"))
 bess_data = yaml.safe_load(open(c["paths"]["bess"]))
 
 ## simulation time setup
-# get current time
 c["sim_start_time"] = get_start_time()
-# get time step in minutes, then convert to seconds
 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
+load_profiles_since_start = None
+status_df = None
 
 # load site info
 c["site_info"] = yaml.safe_load(open(c["paths"]["site_info"]))
 
 
-def generate_and_cache_profiles(c, dt):
-    """Generates load profiles for all sites and caches them."""
-    return get_load_profiles(
+### <<< CONTROL ADDED >>> Initialize simulation state globals
+sim_i = 0
+running = False
+is_running_in_async = False
+sim_lock = threading.Lock()
+
+
+# initialise BESS
+def _init_state():
+    global bess_data, bess_soc_since_start, bess_soc_for_cycle, cumulative_load_profiles, load_profiles_since_start, init_df
+
+    bd = initialise_SoC(bess_data.copy())
+    bd = initial_site_assignment(c, bd)
+    bess_data = bd
+
+    bess_soc_since_start = pd.DataFrame(
+        columns=[unit["name"] for unit in bess_data["units"]]
+    )
+    init_df = pd.DataFrame(columns=["Timestamp", "SoC"])
+    bess_soc_for_cycle = {unit["name"]: init_df for unit in bess_data["units"]}
+
+    cumulative_load_profiles = get_load_profiles(
         c, dt, c["sim_start_time"], c["sim_time"]["batch_process_seconds"]
     )
 
 
-# initialise BESS
-bess_data = initialise_SoC(bess_data)
-bess_data = initial_site_assignment(c, bess_data)
-# bess SoC dataframe
-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"]}
+# do initial setup
+_init_state()
 
-# 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
+def simulation_loop():
+    """Runs the loop, stepping through timestamps until stopped or finished."""
+    global sim_i, running, is_running_in_async, cumulative_load_profiles, bess_data, bess_soc_for_cycle, load_profiles_since_start, status_df
+    with ThreadPoolExecutor() as executor:
+        while True:
+            with sim_lock:
+                if not running or sim_i >= len(timestamps):
+                    break
 
-# 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
+                i = sim_i
+                sim_i += 1
 
-        # check if any BESS units are below threshold (buffer as defined in config)
-
-        # 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_since_start = pd.concat(
-            [
-                bess_soc_since_start,
-                pd.DataFrame(
-                    [temp_soc],
-                    columns=bess_soc_since_start.columns,
-                    index=[timestamps[i]],
-                ),
-            ],
-            axis=0,
-        )
-
-        # update cycle SoC
-        # this is for predicting swap times
-        bess_soc_for_cycle = update_cycle_SoC(bess_data, bess_soc_for_cycle, timestamps)
-
-        # 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:
-            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")
+            # pre-fetch next batch if needed
+            if len(cumulative_load_profiles) <= len(timestamps):
+                if not is_running_in_async:
+                    future = executor.submit(
+                        get_load_profiles,
+                        c,
+                        dt,
+                        c["sim_start_time"],
+                        c["sim_time"]["batch_process_seconds"],
+                    )
+                    is_running_in_async = True
+            else:
                 is_running_in_async = False
 
-pl.plot(cumulative_load_profiles)
-pl.show()
-pl.plot(bess_soc_since_start)
+            # discharge BESS for each site
+            for site in c["site_info"]["sites"]:
+                name = site["name"]
+                p = cumulative_load_profiles[name].iloc[i]
+                bess_data = discharge_bess(bess_data, name, dt, p)
+
+            # record SoC
+            temp_soc = [u["SoC"] for u in bess_data["units"]]
+            bess_soc_since_start.loc[timestamps[i]] = temp_soc
+
+            # update cycle SoC and predict swaps
+            bess_soc_for_cycle = update_cycle_SoC(
+                bess_data, bess_soc_for_cycle, timestamps[i]
+            )
+            swap_times = predict_swap_time(bess_soc_for_cycle)
+
+            # trigger swap if needed
+            bess_data, bess_soc_for_cycle = arrange_swap(
+                c, bess_data, bess_soc_for_cycle
+            )
+
+            # integrate newly fetched profiles
+            if is_running_in_async and future.done():
+                load_profiles = future.result()
+                cumulative_load_profiles = pd.concat(
+                    [cumulative_load_profiles, load_profiles], axis=0
+                )
+                print(len(cumulative_load_profiles), "profiles generated")
+                is_running_in_async = False
+
+            load_profiles_since_start = cumulative_load_profiles.iloc[: i + 1]
+
+            # format data for display
+            status_df = format_dataframe(
+                bess_soc_for_cycle,
+                bess_data,
+                load_profiles_since_start,
+                swap_times,
+                timestamps[i],
+            )
+
+            # small sleep to allow dashboard to refresh / release GIL
+            time.sleep(0.01)
+
+
+### <<< CONTROL ADDED >>> Control functions
+def start_sim():
+    """Starts the simulation in a background thread."""
+    global running, sim_thread
+    if not running:
+        running = True
+        sim_thread = threading.Thread(target=simulation_loop, daemon=True)
+        sim_thread.start()
+
+
+def stop_sim():
+    """Stops the simulation loop."""
+    global running
+    running = False
+
+
+def reset_sim():
+    """Stops and re-initializes the simulation state."""
+    global running, sim_i
+    running = False
+    sim_i = 0
+    _init_state()