  It is a common practice in the current literature of electricity markets to
use game-theoretic approaches for strategic price bidding. However, they
generally rely on the assumption that the strategic bidders have prior
knowledge of rival bids, either perfectly or with some uncertainty. This is not
necessarily a realistic assumption. This paper takes a different approach by
relaxing such an assumption and exploits a no-regret learning algorithm for
repeated games. In particular, by using the \emph{a posteriori} information
about rivals' bids, a learner can implement a no-regret algorithm to optimize
her/his decision making. Given this information, we utilize a multiplicative
weight-update algorithm, adapting bidding strategies over multiple rounds of an
auction to minimize her/his regret. Our numerical results show that when the
proposed learning approach is used the social cost and the market-clearing
prices can be higher than those corresponding to the classical game-theoretic
approaches. The takeaway for market regulators is that electricity markets
might be exposed to greater market power of suppliers than what classical
analysis shows.
