European Energy Analysis Using ENTSO-E API in Python
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Chapter 1: Introduction to ENTSO-E API
This guide will demonstrate how to automate your analysis of European energy data using Python and the ENTSO-E API. We’ll walk you through the process with practical examples.
Section 1.1: Understanding Price Dynamics
When we analyze the electricity prices in Spain (ES) and Italy (IT_SACO_AC), it becomes evident that the rise in Spain's hourly electricity prices occurs more gradually. A significant factor contributing to this is the impact of solar energy generation, which tends to lower electricity prices.
Despite both countries being in the same time zone, Italy is positioned further east, resulting in earlier sunsets. This difference plays a crucial role in the timing of electricity price changes.
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Section 1.2: Accessing the ENTSO-E API
To programmatically retrieve data from the ENTSO-E API, you will first need an access token. Begin by registering on the ENTSO-E website, and then send a request to [email protected] with the subject line "Restful API access."
API_TOKEN = 'YOUR_TOKEN'
Utilizing the open-source entsoe library simplifies working with the ENTSO-E API, as it incorporates the most frequently used endpoints. There are two primary methods to access data:
- EntsoePandasClient: This client downloads and processes data into a DataFrame format for easier analysis using pandas.
- EntsoeRawClient: This client provides data in its raw XML format.
For our purposes, we'll focus on using EntsoePandasClient.
from entsoe import EntsoePandasClient
client = EntsoePandasClient(api_key=API_TOKEN)
Chapter 2: Data Retrieval Examples
Section 2.1: Downloading Generation Data by Technology
We will begin by obtaining data on the generation by technology in Italy for February 2024.
import pandas as pd
start = pd.Timestamp('20240201', tz='Europe/Rome')
end = pd.Timestamp('20240229T2359', tz='Europe/Rome')
df = client.query_generation(
country_code='IT',
start=start, end=end
)
Section 2.2: Analyzing Generation Data
To visualize the differences in generation types throughout the day, we will create a heat matrix.
df['hour'] = df.index.hour
df.groupby(['hour']).sum()
As February has limited sunlight hours, solar generation is only prominent from 9 AM to 3 PM.
Section 2.3: Downloading Electricity Prices
Now, we will enhance our analysis by retrieving electricity prices from various European market areas.
To do this, we need to use specific country codes recognized by the entsoe library.
areas = [
'AT', 'BE', 'BG', 'HR', 'CZ', 'DE_LU', 'DK_1',
'EE', 'FI', 'MK', 'FR', 'GR', 'HU', 'IE_SEM',
'IT_SACO_AC', 'LV', 'LT', 'LU_BZN', 'ME', 'NL', 'NO_1',
'PL', 'PT', 'RO', 'SE_1', 'RS', 'SK', 'SI',
'ES', 'CH', 'UA_IPS'
]
We will iterate through these areas to collect the prices for February 2024 and compile them into a list.
ls = []
for area in areas:
s = client.query_day_ahead_prices(
country_code=area,
start=start, end=end
)
s.name = area
ls.append(s)
Finally, we will merge this list into a DataFrame where each column corresponds to a different market area.
df = pd.concat(ls, axis=1)
Chapter 3: Insights and Conclusions
In our analysis of electricity prices by area, we calculate the hourly averages, as this provides a more representative view than merely summing the values.
df['hour'] = df.index.hour
df.groupby(['hour']).mean()
Notice how the electricity price tends to peak as the sun sets and demand rises, especially across different countries.
When comparing Spain (ES) and Italy (IT_SACO_AC), we observe that the price uptick in Spain occurs more slowly, likely due to its western position in the same time zone.
In conclusion, using EntsoePandasClient allows for seamless access to ENTSO-E API data, enabling efficient data analysis. From downloading generation data to assessing electricity prices by area, the steps outlined here should help facilitate your own energy data analyses.
If you have an idea for an application, feel free to share it in the comments. I’m here to assist with creating tutorials tailored to your needs!