  Convergence (virtual) bidding is an important part of two-settlement electric
power markets as it can effectively reduce discrepancies between the day-ahead
and real-time markets. Consequently, there is extensive research into the
bidding strategies of virtual participants aiming to obtain optimal bids to
submit to the day-ahead market. In this paper, we introduce a price-based
general stochastic optimization framework to obtain optimal convergence bid
curves. Within this framework, we develop a computationally tractable linear
programming-based optimization model, which produces bid prices and volumes
simultaneously. We also show that different approximations and simplifications
in the general model lead naturally to state-of-the-art convergence bidding
approaches, such as self-scheduling and opportunistic approaches. Our general
framework also provides a straightforward way to compare the performance of
these models, which is demonstrated by numerical experiments on the California
(CAISO) market.
