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Periodic double auctions (PDA) have applications in many areas such as ine-commerce, intra-day equity markets, and day-ahead energy markets insmart-grids. While the trades accomplished using PDAs are worth trillions ofdollars, finding a reliable bidding strategy in such auctions is still achallenge as it requires the consideration of future auctions. A participatingbuyer in a PDA has to design its bidding strategy by planning for current andfuture auctions. Many equilibrium-based bidding strategies proposed are complexto use in real-time. In the current exposition, we propose a scale-basedbidding strategy for buyers participating in PDA. We first present anequilibrium analysis for single-buyer single-seller multi-unit single-shotk-Double auctions. Specifically, we analyze the situation when a seller and abuyer trade two identical units of quantity in a double auction where both thebuyer and the seller deploy a simple, scale-based bidding strategy. Theequilibrium analysis becomes intractable as the number of participantsincreases. To be useful in more complex settings such as wholesale markets insmart-grids, we model equilibrium bidding strategy as a learning problem. Wedevelop a deep deterministic policy gradient (DDPG) based learning strategy,DDPGBBS, for a participating agent in PDAs to suggest an action at any auctioninstance. DDPGBBS, which empirically follows the obtained theoreticalequilibrium, is easily extendable when the number of buyers/sellers increases.We take Power Trading Agent Competition's (PowerTAC) wholesale market PDA as atestbed to evaluate our novel bidding strategy. We benchmark our DDPG basedstrategy against several baselines and state-of-the-art bidding strategies ofthe PowerTAC wholesale market PDA and demonstrate the efficacy of DDPGBBSagainst several benchmarked strategies.

Multi-unit Double Auctions: Equilibrium Analysis and Bidding Strategy using DDPG in Smart-grids