Multi-Agent Learning in Double-side Auctions forPeer-to-peer Energy Trading

TL;DR

This paper introduces a multi-agent learning framework using multi-armed bandit algorithms for repeated auctions in peer-to-peer energy trading, aiming to optimize bidding strategies amid market complexities and agent bounded rationality.

Contribution

It proposes a novel automated bidding framework based on multi-armed bandit learning for P2P energy markets, addressing market complexities and agent rationality issues.

Findings

Convergence of the multi-agent learning game to a steady state.

Framework successfully applied to three different auction designs.

Demonstrates potential for market development with adaptive bidding strategies.

Abstract

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.