Evaluating the Role of Information Disclosure on Bidding Behavior in Wholesale Electricity Markets

David Brown, Daniel Cajueiro, Andrew Eckert and Douglas Silveira

No 2024-2, Working Papers from University of Alberta, Department of Economics

Abstract: Real-time information has the potential to improve market outcomes in wholesale electricity markets. However, transparency can also facilitate coordination between firms, raising questions over the appropriate extent of information disclosure. Despite this ongoing debate, there is a lack of understanding of the information employed by firms when bidding in wholesale electricity markets. We use data from Alberta’s wholesale market and leverage machine learning techniques to evaluate the real-time information firms use when forming their bidding decisions. We find that aggregate market-level variables emerge as important predictors, while detailed firm-specific information does not lead to a material improvement in predicting firms’ bidding decisions. These results suggest that firm-specific information, which has raised concerns because of its potential use in facilitating coordinated behavior, may not be required to promote efficient market outcomes.

Keywords: Machine Learning; Electricity; Price Forecasting; Competition Policy (search for similar items in EconPapers)
JEL-codes: D43 L13 L50 L94 Q40 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2024-08-18
New Economics Papers: this item is included in nep-big, nep-com, nep-ene, nep-ind, nep-reg and nep-res
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Persistent link: https://EconPapers.repec.org/RePEc:ris:albaec:2024_002

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