2021/10/20 by Zibo Zhao, Chen Feng, Zhao, Zibo +3 · 2 citations
Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #Artificial intelligence #Auction Theory and Applications #Auction theory #Bidding #Bounded rationality #Combinatorial auction #Common value auction #Computational economics #Computer Science and Game Theory (cs.GT) #Computer science #Distributed computing #Double auction #Economics #FOS: Computer and information sciences #FOS: Electrical engineering #Microeconomics #Peer-to-peer #Regret #Smart Grid Energy Management #Systems and Control (eess.SY) #Vickrey auction #cs.GT #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2110.10714
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/10/20 · openalex publication_date 2021/10/20 · arxiv updated 2021/10/22 · openalex created_date 2021/10/25 · 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; that is, consumers and DER owners trade energy in an auction-based peer-to-peer (P2P) market, and the rates are determined based on supply and demand. However, the auction complexity and market participants' bounded rationality may invalidate many well-established theories on auction design and hinder market development. To address the challenges, we propose an automated bidding framework based on multi-agent, multi-armed bandit learning for repeated auctions, which aims to minimize each bidder's cumulative regret. Numerical results indicate convergence of such a multi-agent learning game to a steady-state. Being particularly interested in auction designs, we have applied the framework to four different implementations of repeated double-side auctions to compare their market outcomes. While it is difficult to pick a clear winner, k-double auction (a variant of uniform pricing auction) and McAfee auction (a variant of Vickrey double-auction) appear to perform well in general, with their respective strengths and weaknesses.