Algorithmic trading of real-time electricity with machine learning

Vighnesh Natarajan Ganesh and Derek Bunn

Quantitative Finance, 2024, vol. 24, issue 11, 1545-1559

Abstract: Algorithmic trading is becoming the dominant approach in many electricity spot and futures markets. This paper focuses on the emerging interest in the less documented real-time imbalance markets, by developing reinforcement learning agents to find profit-making opportunities algorithmically. We develop a repeatable experimental setting to compare different market participants and explore the applications of Q-learning with neural networks for three types of market participants: a non-physical trader, a gas generator, and a battery electricity storage system. We backtest all three agents using British data across summer and winter months to compare their profits, risks and various experimental design considerations.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2024.2420609 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:24:y:2024:i:11:p:1545-1559

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2024.2420609

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().