  The system operator's scheduling problem in electricity markets, called unit
commitment, is a non-convex mixed-integer program. The optimal value function
is non-convex, preventing the application of traditional marginal pricing
theory to find prices that clear the market and incentivize market participants
to follow the dispatch schedule. Units that perceive the opportunity to make a
profit may be incentivized to self-commit (submitting an offer with zero fixed
operating costs) or self-schedule their production (submitting an offer with
zero total cost). We simulate bidder behavior to show that market power can be
exercised by becoming a price taker. Agents can learn to increase their profits
via a reinforcement learning algorithm without explicit knowledge of the costs
or strategies of other agents. We investigate different non-convex pricing
models over a multi-period commitment window simulating the day-ahead market
and show that convex hull pricing can reduce producer incentives to deviate
from the central dispatch decision. In a realistic test system with
approximately 1000 generators, we find strategic bidding under the restricted
convex model can increase total producer profits by 4.4% and decrease lost
opportunity costs by 2/3. While the cost to consumers with convex hull pricing
is higher at the competitive solution, the cost to consumers is higher with the
restricted convex model after strategic bidding.
