Prediction of Matching Prices in Electricity Markets through Curve Representation

Daniel Foronda-Pascual and Andrés M. Alonso ()
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Daniel Foronda-Pascual: Research Service, Universidad Carlos III de Madrid, 28911 Leganés, Spain
Andrés M. Alonso: Department of Statistics, Institute Flores de Lemus, Universidad Carlos III de Madrid, 28903 Getafe, Spain

Energies, 2023, vol. 16, issue 23, 1-20

Abstract: In the Spanish electricity market, after the daily market is held in which prices are set for the next day, the secondary and tertiary markets take place, which allow companies more accurate adjustment of the electricity they are able to offer. The objective of this paper is to predict the final price reached in these markets by predicting the supply curve in advance, which is the aggregate of what companies offer. First, we study a procedure to represent the supply curves, and then we consider different machine learning approaches to obtain the day-ahead supply curves for the secondary market. Finally, the predictions of the supply curves are crossed with the system requirements to obtain the expected price predictions. Histogram-Based Gradient Boosting is the best performing algorithm for predicting supply curves. The most relevant variables for the prediction are the lagged values, the daily market price, the price of gas and values of the wind recorded in the Spanish provinces.

Keywords: electricity secondary market; electricity supply curves; multivariate time series; forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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