Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70995

Multi-Agent Learning in Repeated Double-side Auctions for Peer-to-peer Energy Trading

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dc.contributor.author Liu, Andrew
dc.contributor.author Zhao, Zibo
dc.date.accessioned 2020-12-24T19:38:22Z
dc.date.available 2020-12-24T19:38:22Z
dc.date.issued 2021-01-05
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70995
dc.description.abstract Distributed energy resources (DERs), such as rooftop solar panels, are growing rapidly and are reshaping power systems. To promote DERs, feed-in-tariff is usually adopted by utilities to pay DER owners certain fixed rates for supplying energy to the grid. Such a non-market based approach may increase electricity rates and create inefficiency. An alternative is a market based approach; i.e., consumers and DER owners trade energy in a peer-to-peer (P2P) market, in which electricity prices are determined by real-time market supply and demand. A prevailing approach to realize a P2P marketplace is through double-side auctions. However, the auction complexity in an energy market and the participants’ bounded rationality may invalidate many well-established results in auction theory and hence, cast difficulties for market design and implementation. To address such issues, we propose an automated bidding framework based on multi-agent, multi-armed bandit learning through repeated auctions, which is aimed to minimize each bidder’s cumulative regret. Numerical results suggest the potential convergence of such a multi-agent learning game to a steady-state. We also apply the framework to three different auction designs (including uniform-price versus Vickrey-type auctions) for a P2P market to study the impacts of the different designs on market outcomes.
dc.format.extent 10 pages
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Distributed, Renewable, and Mobile Resources
dc.subject double auction
dc.subject multiarmed bandit game
dc.subject peer-to-peer
dc.title Multi-Agent Learning in Repeated Double-side Auctions for Peer-to-peer Energy Trading
dc.identifier.doi 10.24251/HICSS.2021.380
prism.startingpage 3121
Appears in Collections: Distributed, Renewable, and Mobile Resources


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