Load Duration Curve

A load duration curve shows the demand in relation to time. It is traditionally used in electric power system planning to assess the ideal generation portfolio.

1. Load packages

import pandas as pd
import numpy as np
# Import the necessaries libraries
import plotly.express as px
import plotly.offline as pyo
pyo.init_notebook_mode()

2. Read data

data_file = "caiso_load_2023_hourly.csv"
data_all = pd.read_csv(data_file)

3. Prepare data

load_chron = np.array(data_all["load.load"])
load_dur = np.flip(np.sort(load_chron))
net_load_chron = np.array(data_all["net_load"])
net_load_dur = np.flip(np.sort(net_load_chron))

data_plot = pd.DataFrame({
    "datetime": pd.Series(pd.date_range(start='2023-01-01 01:00:00', end='2023-12-31 23:00:00', freq='h')),
    "h_count": np.arange(1,len(load_dur)+1),
    "load_chron": load_chron,
    "load_dur": load_dur,
    "net_load_chron": net_load_chron,
    "net_load_dur": net_load_dur,
})
label_dict ={
    "load_chron": "Load in MW",
    "value": "Load in MW",
    "datetime": "Timestamp",
    "h_count": "Hour #"
}

4. Plot curves

• First: chronologial load data \(x_i\)

• Second: load duration durve with

fig = px.line(data_plot, x="datetime", y="load_chron", labels=label_dict)
fig.update_layout(
    width=900,  
    height=600
    )
fig.show()

fig = px.line(data_plot, x="h_count", y=["load_chron", "load_dur", "net_load_chron", "net_load_dur"], labels=label_dict)
fig.update_layout(
    width=900,  
    height=600, 
    )
fig.update_traces(visible="legendonly")
fig.show()