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Distributed energy resources (DERs), such as solar panels, are growing rapidly and reshaping power systems. To promote DERs, utility companies usually adopt feed-in-tariff (FIT) to pay DER owners (aka prosumers) fixed rates for supplying energy to the grid. As an alternative to FIT, consumers and prosumers can trade energy in a peer-to-peer (P2P) fashion. In this paper, we focus on a P2P market using double auctions, in which the payoffs of energy consumers/prosumers are determined by their bids and auction mechanisms. Special features of a P2P energy auction, however, including zero marginal cost and publicly-known reserve prices, may invalidate many theories on auction design and hinder market development. We discuss the impacts of such features on four specific clearing mechanisms: k -double, Vickrey, McAfee and maximum volume matching (MVM). Furthermore, we propose an automated bidding framework based on multi-agent, multi-armed bandit learning, in which each agent only needs to utilize their own bidding history to determine how to bid in the next round through certain regret-minimizing algorithms. Numerical results show that the k -double and McAfee auction appear to perform better in terms of bidders’ surplus. However, if the auctioneer also requires compensation, MVM can yield the most profit for the auctioneer.
Zhao et al. (Wed,) studied this question.