2020/02/21 by Zibo Zhao, Zhao, Zibo, Andrew L. Liu +1
Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #Auction Theory and Applications #FOS: Electrical engineering #Smart Grid Energy Management #Systems and Control (eess.SY) #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2002.09435
arxiv created 2020/02/21 · openalex publication_date 2020/02/21 · arxiv updated 2020/02/24 · openalex created_date 2020/03/06 · openalex updated_date 2026/07/28
Distributed energy resources (DERs), such as rooftop solar panels, are growing rapidly and are reshaping power systems. To promote DERs, feed-in-tariff (FIT) is usually adopted by utilities to pay DER owners certain fixed rates for supplying energy to the grid. An alternative to FIT is a market based approach; i.e., consumers and DER owners trade energy in an auction-based peer-to-peer (P2P) market, and the rates are determined by a market clearing process. However, the complexities in sucha market and agents' bounded rationality may invalidate many well-established theories on auction design and hinder market development. To address this issue, we propose an automated bidding framework in a repeated auction based on multi-armed bandit learning, which aims to minimize each bidder's cumulative regret. Numerical results indicate convergence of such a multi-agent learning game to a steady-state. For comparison purpose, we apply the framework to three different auction designs to realize a P2P market.