Market Coefficients Module

This module provides functions for analyzing supply and demand market data to prepare values for the price zone coefficients based on EPEX Spot data format. As well as cleaning data downloaded from ENTSO-E for load and generation trends.

functions are found in pyflow_acdc.Market_Coeff

Price Zone Coefficient Analysis

Obtain price zone coefficients

price_zone_coef_data(df, start, end, increase_eq_price=50)

Analyzes market data to generate price zone coefficients.

Parameter	Type	Description	Default	Units
df	DataFrame	Market data	Required
start	int	Start hour	Required	hour
end	int	End hour	Required	hour
increase_eq_price	float	Price increase for equilibrium	50	€/MWh

Returns a list of dictionaries containing:

• Date - Date of the data

• Hour - Hour of the data

• Sell - Supply curve data

• Purchase - Demand curve data

• Integrated_sets - Benefit of consumption and cost of generation from integral of supply and demand curves

• Dem_data_points - Number of demand data points

• Gen_data_points - Number of supply data points

• max_gen - Maximum supply value

• min_demand - Minimum demand value

• Market_price - Market clearing price

• Volume_eq - Market clearing volume

• poly - Dictionary containing polynomial coefficients

• prediction_BC - Predicted benefit of consumption

• prediction_CG - Predicted cost of generation

• Volume_0 - Volume where first positive price is supplied

And timing information with the following keys:

• load data - Time taken to load data

• avg process - Average time taken to process data

• tot process - Total time taken to process data

Export data to csv

price_zone_data_pd(data, save_csv=None)

Converts market data to pandas DataFrame and saves it to a csv file.

Parameter	Type	Description	Default
data	dict	Market data	Required
save_csv	str	Save csv file	None

Visualization

Generates plots of market curves.

plot_curves(data, hour, name=None)

Creates visualization of market curves.

Parameter	Type	Description	Default	Units
data	dict	Market data	Required
hour	int	Hour to plot	Required	hour
name	str	Output filename	None

Creates subplots showing:

• Supply and demand curves

• Cost of generation curve

• Integrated supply and demand

• Price curves

Example

pyf.plot_curves(market_data, hour=12, name='Belgium')

ENTSO-E Data Cleaning

For the use of entsoe data cleaning, the data has to be structured as follows:

path
|-- key0
|   |-- AGGREGATED_GENERATION_PER_TYPE_GENERATION_{year_0-1}12312300-{year_0}12312300.csv
|   |-- GUI_TOTAL_LOAD_DAYAHEAD_{year_0-1}12312300-{year_0}12312300.csv
|   |-- AGGREGATED_GENERATION_PER_TYPE_GENERATION_{year_1-1}12312300-{year_1}12312300.csv
|   |-- GUI_TOTAL_LOAD_DAYAHEAD_{year_1-1}12312300-{year_1}12312300.csv
|   |-- ...
|-- key1
|   |-- ...
|-- ...

This is the name of the files when downloaded from ENTSO-E transparency platform.

clean_entsoe_data(key_list, year_list, production_types=[], output_excel=None,path=None):

Process generation and load data for multiple areas/years and save to Excel.

Parameter	Type	Description	Default
key_list	list	List of keys	Required
year_list	list	List of years	Required
production_types	list	Reduced list of production types	All
output_excel	str	Output excel file	output_data.xlsx
path	str	Path to save the excel file	Current working directory

Returns

Excel file containing the following sheets:

• Maximum values - Contains maximum values for each column

• year_0 - Contains normalized data for the first year

• year_n - Contains normalized data for subsequent years