wip load profile engine

generating reasonably realistic load profiles

Utilities/LoadProfile.py

@@ -1,16 +1,40 @@
 import numpy as np
-from Utilities.Time import generate_timestrings, index_peak_times, index_operating_hours
+from Utilities.Time import (
+    generate_timestrings,
+    index_peak_times,
+    index_operating_hours,
+    check_is_weekday,
+)
 from scipy.optimize import root_scalar
 import pandas as pd
+import matplotlib.pyplot as pl
 
 
-def get_no_of_peaks(peak_bounds):
+def get_no_of_peaks(peak_bounds, is_weekday, peak_duration, peak_energy, site):
-    peak_occurences = np.random.randint(peak_bounds["min"], peak_bounds["max"], 1)
+    # calculate theoretically maximum number of peaks based on daily consumption (kWh)
+    max_occurences = np.floor(
+        peak_energy / site["maximum_demand_kW"] / (peak_duration["max"] / 60)
+    )
+
+    if is_weekday:
+        peak_occurences = np.random.randint(
+            peak_bounds["weekdays"]["min"], max_occurences, 1
+        )
+    else:
+        peak_occurences = np.random.randint(
+            peak_bounds["weekends"]["min"], max_occurences, 1
+        )
     return peak_occurences
 
 
-def generate_peak_info(c, dt):
+def generate_peak_info(c, dt, is_weekday, peak_energy, site):
-    no_of_peaks = get_no_of_peaks(c["site_info"]["no_of_peaks"])
+    no_of_peaks = get_no_of_peaks(
+        c["site_info"]["no_of_peaks"],
+        is_weekday,
+        c["site_info"]["peak_duration"],
+        peak_energy,
+        site,
+    )
     operating_hours = generate_timestrings(
         c["site_info"]["operating hours"]["start"],
         c["site_info"]["operating hours"]["end"],
@@ -26,6 +50,17 @@ def generate_peak_info(c, dt):
     return peak_times, peak_durations
 
 
+def generate_peak_profile(idx_peak, c, site):
+    # Generate a peak profile based on the peak indices and site information
+    peak_profile = np.zeros(len(idx_peak))
+    for i in range(1, np.max(idx_peak) + 1):
+        peak_profile[idx_peak == i] = site["maximum_demand_kW"] * np.random.uniform(
+            1 - c["noise"]["range"],
+            1 + c["noise"]["range"],
+        )
+    return peak_profile
+
+
 def generate_out_of_hours_consumption_ratio(c):
     # Generate a random ratio for out-of-hours consumption
     ratio = np.random.uniform(
@@ -35,18 +70,22 @@ def generate_out_of_hours_consumption_ratio(c):
     return ratio
 
 
-def recompute_load_profile(
+def recompute_load_profile(load_profile, offset, noise, peak_profile):
-    load_profile,
+    # apply noise to the load profile, including max demand
-    offset,
+    load_profile = load_profile * noise
-):
 
     # apply offset to the load profile
     load_profile += offset
 
+    # overwrite with peak profile
+    for i in range(len(peak_profile)):
+        if peak_profile[i] > 0:
+            load_profile[i] = peak_profile[i]
+
     return load_profile
 
 
-def get_load_profile(c, dt, batch_start_time, batch_process_duration):
+def get_load_profiles(c, dt, batch_start_time, batch_process_duration):
     # Generate load profile for each site
 
     # c is the configuration dictionary
@@ -56,14 +95,26 @@ def get_load_profile(c, dt, batch_start_time, batch_process_duration):
 
     # start with indexing all the peak occurences
     # generate timeseries from start to end time
+    hours2seconds = 3600
+    # check day of the week
+    is_weekday = check_is_weekday(batch_start_time)
     start_time = batch_start_time
     end_time = start_time + batch_process_duration
-    batch_process_duration_hours = batch_process_duration / 3600  # convert to hours
+    batch_process_duration_hours = (
-    timestamps = np.arange(start_time, end_time + 1, dt)
+        batch_process_duration / hours2seconds
+    )  # convert to hours
+    timestamps = np.arange(start_time, end_time, dt)
     idx_operating_hours = index_operating_hours(
         timestamps, c["site_info"]["operating hours"]
     )
-    no_of_operating_hours = np.sum(idx_operating_hours > 0)
+    no_of_operating_hours = (
+        np.sum(idx_operating_hours)
+        / len(idx_operating_hours)
+        * batch_process_duration_hours
+    )
+
+    # initialise load profiles DataFrame
+    load_profiles = pd.DataFrame(index=timestamps)
 
     # loop through each site in the configuration
     for site in c["site_info"]["sites"]:
@@ -71,14 +122,13 @@ def get_load_profile(c, dt, batch_start_time, batch_process_duration):
         load_profile = np.zeros(len(timestamps))
 
         # generate noise to make the profile more realistic
-        noise = np.random.normal(
+        noise = np.random.uniform(
             1 - c["noise"]["range"], 1 + c["noise"]["range"], len(timestamps)
         )
-        # Generate peak times and durations
-        peak_times, peak_durations = generate_peak_info(c, dt)
 
-        # Generate peak times and durations
+        # make every 2 seconds the same
-        idx_peak = index_peak_times(timestamps, peak_times, peak_durations)
+        for i in range(0, len(noise), 2):
+            noise[i : i + 2] = noise[i]
 
         # Generate out-of-hours consumption ratio
         # The % of energy used outside of the operating hours
@@ -91,20 +141,66 @@ def get_load_profile(c, dt, batch_start_time, batch_process_duration):
         )
         out_of_hours_consumption = site["daily_consumption_kWh"] * out_of_hours_ratio
 
-        avg_operating_hour_consumption = (
+        avg_operating_hour_power = operating_hour_consumption / no_of_operating_hours
-            operating_hour_consumption / no_of_operating_hours
+        avg_out_of_hours_power = out_of_hours_consumption / (
-        )
-        avg_out_of_hours_consumption = out_of_hours_consumption / (
             batch_process_duration_hours - no_of_operating_hours
         )
 
+        # baseline operating hour power is 40% higher than out-of-hours power
+        gain = 1.4
+        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
+        ) + (assumed_operating_baseline_power * no_of_operating_hours)
+
+        peak_energy = site["daily_consumption_kWh"] - baseline_energy
+
+        # Generate peak times and durations
+        peak_times, peak_durations = generate_peak_info(
+            c, dt, is_weekday, peak_energy, site
+        )
+
+        # Generate peak times and durations
+        idx_peak = index_peak_times(timestamps, peak_times, peak_durations)
+
+        # Generate peak profile
+        peak_profile = generate_peak_profile(idx_peak, c, site)
+
         # assign base load profile
-        load_profile[idx_operating_hours > 0] = avg_operating_hour_consumption
+        load_profile[idx_operating_hours > 0] = avg_operating_hour_power
-        load_profile[idx_operating_hours == 0] = avg_out_of_hours_consumption
+        load_profile[idx_operating_hours == 0] = avg_out_of_hours_power
 
-        # apply peak loads
+        # smoothen out sharp edges
-        for i in range(1, np.max(idx_peak) + 1):
+        load_profile = np.convolve(load_profile, np.ones(40) / 40, mode="same")
-            load_profile[idx_peak == i] = site["maximum_demand_kW"]
 
-        # apply noise to the load profile, including max demand
+        def objective(x):
-        load_profile = load_profile * noise
+            # Objective function to minimize the difference between the load profile and the target profile
+            # x is the offset
+            adjusted_profile = recompute_load_profile(
+                load_profile, x, noise, peak_profile
+            )
+            # get energy consumption in kWh
+            energy_consumption = np.sum(adjusted_profile) * dt / 3600
+            target_consumption = site["daily_consumption_kWh"]
+            delta = energy_consumption - target_consumption
+            return delta
+
+        # Use root_scalar to find the optimal offset
+        result = root_scalar(
+            objective,
+            bracket=[-site["maximum_demand_kW"] * 10, site["maximum_demand_kW"] * 10],
+            method="bisect",
+        )
+
+        if result.converged:
+            offset = result.root
+        else:
+            raise ValueError("Root finding did not converge")
+
+        # Recompute the load profile with the optimal offset
+        load_profile = recompute_load_profile(load_profile, offset, noise, peak_profile)
+
+        # Add the load profile to the DataFrame
+        load_profiles[site["name"]] = load_profile
+
+    return load_profiles

Utilities/Time.py

@@ -74,3 +74,14 @@ def index_operating_hours(timestamps, operating_hours):
             operating_indices[i] = 1  # mark as operating hour
 
     return operating_indices
+
+
+def check_is_weekday(batch_start_time):
+    """Checks if the batch start time is on a weekday."""
+    # batch_start_time is in seconds since the epoch
+    start_time = datetime.fromtimestamp(batch_start_time)
+    if start_time.weekday() >= 5:  # Saturday or Sunday
+        is_weekday = False
+    else:
+        is_weekday = True
+    return is_weekday

YAMLs/config.yml

@@ -4,7 +4,7 @@ sim_time:
   duration_days: 60
 
 noise:
-  range: 0.15
+  range: 0.3
 
 paths:
   site_info: YAMLs/site_info.yaml

YAMLs/site_info.yaml

@@ -26,8 +26,10 @@ operating hours:
   end: "19:00"
 time zone: Asia/Kuala_Lumpur
 no_of_peaks:
-  min: 30
+  weekdays:
-  max: 100
+    min: 5
+  weekends:
+    min: 1
 peak_duration:
   unit: minutes
   min: 1

main.py

@@ -1,6 +1,8 @@
 import yaml
 from Utilities.Time import get_start_time
-from Utilities.LoadProfile import get_load_profile
+from Utilities.LoadProfile import get_load_profiles
+import matplotlib.pyplot as pl
+import pandas as pd
 
 # read config file
 c = yaml.safe_load(open("YAMLs/config.yml"))
@@ -20,5 +22,17 @@ 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
-get_load_profile(c, dt, c["sim_start_time"], c["sim_time"]["batch_process_seconds"])
+    load_profiles = 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