Evaluating the role of information disclosure on bidding behavior in wholesale electricity markets

David P. Brown, Daniel O. Cajueiro, Andrew Eckert and Douglas Silveira

Energy Economics, 2025, vol. 146, issue C

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. Using data from Alberta, we 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)
Date: 2025
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Working Paper: Evaluating the Role of Information Disclosure on Bidding Behavior in Wholesale Electricity Markets (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:146:y:2025:i:c:s0140988325003299

DOI: 10.1016/j.eneco.2025.108505

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