  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.
