Electricity Market Optimization 探索系列（二）

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本文参考链接link
负荷持续时间曲线 (Load Duration Curve)，是根据实际的符合数据进行降序排序之后得到的一个曲线

这个曲线能够发现负荷在某个区间时，将会持续多长时间，有助于发电容量的规划

净负荷(net load) 是指预期负荷和预期可再生能源发电量之间的差值，净负荷可以帮助规划资源配置

以下是根据caiso在2023年的负荷数据得到的画出四条曲线的代码

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()

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

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 #"
}

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()