A General Stochastic Optimization Framework for Convergence Bidding
This work addresses the need for efficient bidding strategies for virtual participants in two-settlement power markets, though it appears incremental by building on and comparing to existing methods.
The paper tackles the problem of optimizing convergence bidding in electric power markets by introducing a general stochastic optimization framework that produces bid curves, demonstrating its application and comparison with existing approaches through numerical experiments on the CAISO market.
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.