Bidding in Periodic Double Auctions Using Heuristics and Dynamic Monte Carlo Tree Search
(M. P. Chowdhury, C. Kiekintveld, S. Tran, W. Yeoh)
In International Joint Conference on Artificial Intelligence (IJCAI), 2018.
This is the author's version of the work.
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Abstract
In a Periodic Double Auction (PDA), there are multiple
discrete trading periods for a single type of
good. PDAs are commonly used in real-world energy
markets to trade energy in specific time slots
to balance demand on the power grid. Strategically,
bidding in a PDA is complicated because the bidder
must predict and plan for future auctions that
may influence the bidding strategy for the current
auction. We present a general bidding strategy for
PDAs based on forecasting clearing prices and using
Monte Carlo Tree Search (MCTS) to plan a bidding
strategy across multiple time periods. In addition,
we present a fast heuristic strategy that can be
used either as a standalone method or as an initial
set of bids to seed the MCTS policy. We evaluate
our bidding strategies using a PDA simulator based
on the wholesale market implemented in the Power
Trading Agent Competition (PowerTAC) competition.
We demonstrate that our strategies outperform
state-of-the-art bidding strategies designed for that
competition.
