111 lines
3.9 KiB
Python
111 lines
3.9 KiB
Python
import numpy as np
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from Utilities.Time import generate_timestrings, index_peak_times, index_operating_hours
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from scipy.optimize import root_scalar
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import pandas as pd
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def get_no_of_peaks(peak_bounds):
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peak_occurences = np.random.randint(peak_bounds["min"], peak_bounds["max"], 1)
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return peak_occurences
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def generate_peak_info(c, dt):
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no_of_peaks = get_no_of_peaks(c["site_info"]["no_of_peaks"])
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operating_hours = generate_timestrings(
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c["site_info"]["operating hours"]["start"],
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c["site_info"]["operating hours"]["end"],
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dt,
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)
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peak_times = np.random.choice(operating_hours, no_of_peaks, replace=False)
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peak_durations = np.random.randint(
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c["site_info"]["peak_duration"]["min"],
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c["site_info"]["peak_duration"]["max"],
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no_of_peaks,
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)
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return peak_times, peak_durations
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def generate_out_of_hours_consumption_ratio(c):
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# Generate a random ratio for out-of-hours consumption
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ratio = np.random.uniform(
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c["site_info"]["out_of_hours_consumption"]["min"],
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c["site_info"]["out_of_hours_consumption"]["max"],
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)
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return ratio
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def recompute_load_profile(
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load_profile,
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offset,
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):
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# apply offset to the load profile
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load_profile += offset
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return load_profile
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def get_load_profile(c, dt, batch_start_time, batch_process_duration):
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# Generate load profile for each site
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# c is the configuration dictionary
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# dt is the time step in seconds
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# batch_start_time is the start time for the batch process in seconds since the epoch
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# batch_process_duration is the duration of the batch process in seconds
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# start with indexing all the peak occurences
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# generate timeseries from start to end time
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start_time = batch_start_time
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end_time = start_time + batch_process_duration
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batch_process_duration_hours = batch_process_duration / 3600 # convert to hours
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timestamps = np.arange(start_time, end_time + 1, dt)
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idx_operating_hours = index_operating_hours(
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timestamps, c["site_info"]["operating hours"]
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)
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no_of_operating_hours = np.sum(idx_operating_hours > 0)
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# loop through each site in the configuration
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for site in c["site_info"]["sites"]:
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# Initialise the load profile
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load_profile = np.zeros(len(timestamps))
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# generate noise to make the profile more realistic
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noise = np.random.normal(
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1 - c["noise"]["range"], 1 + c["noise"]["range"], len(timestamps)
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)
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# Generate peak times and durations
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peak_times, peak_durations = generate_peak_info(c, dt)
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# Generate peak times and durations
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idx_peak = index_peak_times(timestamps, peak_times, peak_durations)
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# Generate out-of-hours consumption ratio
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# The % of energy used outside of the operating hours
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out_of_hours_ratio = generate_out_of_hours_consumption_ratio(c)
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# start by computing average consumption during operating hours
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# and outside of operating hours
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operating_hour_consumption = site["daily_consumption_kWh"] * (
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1 - out_of_hours_ratio
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)
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out_of_hours_consumption = site["daily_consumption_kWh"] * out_of_hours_ratio
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avg_operating_hour_consumption = (
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operating_hour_consumption / no_of_operating_hours
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)
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avg_out_of_hours_consumption = out_of_hours_consumption / (
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batch_process_duration_hours - no_of_operating_hours
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)
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# assign base load profile
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load_profile[idx_operating_hours > 0] = avg_operating_hour_consumption
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load_profile[idx_operating_hours == 0] = avg_out_of_hours_consumption
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# apply peak loads
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for i in range(1, np.max(idx_peak) + 1):
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load_profile[idx_peak == i] = site["maximum_demand_kW"]
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# apply noise to the load profile, including max demand
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load_profile = load_profile * noise
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