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Pré-Publication, Document De Travail Année : 2021

Data-driven Structural Modeling of Electricity Price Dynamics

Résumé

In many countries, electricity prices on day-ahead auction markets result from a market clearing designed to maximize social welfare. For each hour of the day, the market price can be represented as the intersection of a supply and demand curve. Structural market models reflect this price formation mechanism and are widely used in prospective studies guiding long-term decisions (e.g. investments and market design). However, simulating the supply curve in these models proves challenging since estimating the sell orders it comprises (i.e. offer prices and corresponding quantities) typically requires formulating numerous techno-economic hypotheses about power system assets and the behaviors of market participants. In this paper we propose a method for the parameterization of sell orders associated with production units. The estimation algorithm for this parametrization makes it possible to mitigate the requirement for analytic formulation of all of the above-mentioned aspects and to take advantage of the ever-increasing volume of available data on power systems (e.g. technical and market data). Parametrized orders also offer the possibility to account for various factors in a modular fashion, such as the strategic behavior of market participants. The proposed approach is validated using data related to the French day-ahead market and power system, for the period from 2015 to 2018.
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Dates et versions

hal-03445396 , version 1 (23-11-2021)

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  • HAL Id : hal-03445396 , version 1

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Valentin Mahler, Robin Girard, Georges Kariniotakis. Data-driven Structural Modeling of Electricity Price Dynamics. 2021. ⟨hal-03445396⟩
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