Multi-unit multiple bid auctions in balancing markets: an agent-based Q-learning approach

There is an ongoing debate on the appropriate auction design for competitive electricity balancing markets. Uniform (UPA)and discriminatory price auctions (DPA), the prevalent designs in use today, are assumed to have different properties with regard to prices and effciencies. These properties cannot be thoroughly described using analytical methods due to the complex strategy space in repeated multi-unit multiple bid auctions. Therefore, using an agent-based Q-learning model, we simulate the strategic bidding behaviour in these auctions under a variety of market conditions. We find that UPAs lead to higher prices in all analysed market settings. This is mainly due to the fact that players engage in bid shading more aggressively. Moreover, small players in UPAs learn to free ride on the price setting of large players and learn higher profits per unit of capacity owned, while they are disadvantaged in DPAs. UPAs also generally feature higher effciencies, but there are exceptions to this observation. If demand is varying and players are provided with additional information about scarcity in the market, market prices increase only in case asymmetric players are present.