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Marginal Price Curve Estimation for Dispatchable Power in Great Britain

Binder

In this example we'll estimate the marginal price curve over the last two months for dispatchable power in Great Britain using data from Electric Insights.


Imports

import numpy as np
import pandas as pd

import matplotlib.pyplot as plt

from moepy import retrieval, eda, lowess


Data Loading

We'll start by loading in the necessary data from Electric Insights

%%time

current_dt = pd.Timestamp.now()
start_date = (current_dt-pd.Timedelta(weeks=8)).strftime('%Y-%m-%d %H:%M')
end_date = current_dt.strftime('%Y-%m-%d %H:%M')

renaming_dict = {
    'pumpedStorage' : 'pumped_storage',
    'northernIreland' : 'northern_ireland',
    'windOnshore': 'wind_onshore',
    'windOffshore': 'wind_offshore',
    'prices_ahead' : 'day_ahead_price',
    'prices' : 'imbalance_price',
    'temperatures' : 'temperature',
    'totalInGperkWh' : 'gCO2_per_kWh',
    'totalInTperh' : 'TCO2_per_h'
}

df = retrieval.retrieve_streams_df(start_date, end_date, renaming_dict=renaming_dict)

df.head()

Wall time: 7.75 s
local_datetime day_ahead_price SP imbalance_price valueSum temperature TCO2_per_h gCO2_per_kWh nuclear biomass coal ... demand pumped_storage wind_onshore wind_offshore belgian dutch french ireland northern_ireland irish
2021-02-01 00:00:00+00:00 51.99 1 68.95 68.95 2.9 4797.76 175.8 5.564 1.945 0.465 ... 27.291 0 3.02828 3.51436 0.902 0 1.806 0 0.018 -0.05
2021-02-01 00:30:00+00:00 54.19 2 69 69 2.9 5149.7 186.031 5.559 1.963 0.563 ... 27.682 0 2.90388 3.44746 0.902 0 1.806 0 0.016 0.016
2021-02-01 01:00:00+00:00 55.07 3 75 75 2.9 5177.97 189.309 5.565 2.077 0.68 ... 27.352 0 2.76413 3.36153 0.952 0 1.906 0 0.018 0.018
2021-02-01 01:30:00+00:00 56.3 4 72 72 2.9 5131.08 190.892 5.563 2.122 0.716 ... 26.8796 0 2.62404 3.19386 0.952 0 1.906 0 0.016 0.016
2021-02-01 02:00:00+00:00 56.71 5 75 75 2.9 5105.37 193.368 5.561 2.134 0.718 ... 26.4023 0 2.41751 2.93459 0.926 0 1.906 0 0.018 0.018


We'll quickly visualise the relationship between price and dispatchable load for each half-hour period

df_model = df[['day_ahead_price', 'demand', 'solar', 'wind']].dropna().astype(float)

s_price = df_model['day_ahead_price']
s_dispatchable = df_model['demand'] - df_model[['solar', 'wind']].sum(axis=1)

# Plotting
fig, ax = plt.subplots(dpi=150)

ax.scatter(s_dispatchable, s_price, s=0.5, alpha=0.25, color='k')

ax.set_ylim(-20, 150)
eda.hide_spines(ax)
ax.set_xlabel('Demand - [Wind + Solar] (GW)')
ax.set_ylabel('Price (£/MWh)')

Text(0, 0.5, 'Price (£/MWh)')

png


Marginal Price Curve Estimation

We're now ready to fit our LOWESS model

x_pred = np.linspace(10, 40, 301)

y_pred = lowess.lowess_fit_and_predict(s_dispatchable.values, 
                                       s_price.values, 
                                       frac=0.25, 
                                       num_fits=25, 
                                       x_pred=x_pred)

pd.Series(y_pred, index=x_pred).plot()

<AxesSubplot:>

png


We'll then visualise the estimated fit alongside the historical observations

fig, ax = plt.subplots(dpi=250)

ax.plot(x_pred, y_pred, linewidth=1.5, color='r')
ax.scatter(s_dispatchable, s_price, color='k', s=0.5, alpha=0.25)

ax.set_ylim(-20, 150)
eda.hide_spines(ax)
ax.set_xlabel('Demand - [Solar + Wind] (GW)')
ax.set_ylabel('Day-Ahead Price (£/MWh)')
fig.savefig('../img/latest_gb_mcc.png', dpi=250)

png