论文标题
在双方拍卖中的多机构学习。
Multi-Agent Learning in Double-side Auctions forPeer-to-peer Energy Trading
论文作者
论文摘要
分布式能源资源（DER），例如屋顶太阳能电池板，正在迅速增长，并且正在重塑电源系统。为了促进DERS，公用事业通常会采用饲养税（FIT），以向所有者支付某些固定费率以向电网供应能源。拟合的替代方法是基于市场的方法。即，在基于拍卖的点对点（P2P）市场中，消费者和DER所有者贸易能源是由市场清算过程确定的。但是，这种市场和代理商的合理性的复杂性可能使许多关于拍卖设计和阻碍市场发展的理论无效。为了解决这个问题，我们在基于多军强盗学习的重复拍卖中提出了一个自动招标框架，该框架旨在最大程度地减少每个投标人的累积遗憾。数值结果表明这种多代理学习游戏与稳态的收敛性。为了进行比较，我们将框架应用于三种不同的拍卖设计，以实现P2P市场。
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.
