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http://hdl.handle.net/10125/70995
Multi-Agent Learning in Repeated Double-side Auctions for Peer-to-peer Energy Trading
Item Summary
Title: | Multi-Agent Learning in Repeated Double-side Auctions for Peer-to-peer Energy Trading |
Authors: | Liu, Andrew Zhao, Zibo |
Keywords: | Distributed, Renewable, and Mobile Resources double auction multiarmed bandit game peer-to-peer |
Date Issued: | 05 Jan 2021 |
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. |
Pages/Duration: | 10 pages |
URI: | http://hdl.handle.net/10125/70995 |
ISBN: | 978-0-9981331-4-0 |
DOI: | 10.24251/HICSS.2021.380 |
Rights: | Attribution-NonCommercial-NoDerivatives 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0/ |
Appears in Collections: |
Distributed, Renewable, and Mobile Resources |
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