Mathematics > Optimization and Control
[Submitted on 12 Oct 2022 (v1), last revised 7 Feb 2023 (this version, v4)]
Title:A General Stochastic Optimization Framework for Convergence Bidding
View PDFAbstract: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.
Submission history
From: Letif Mones [view email][v1] Wed, 12 Oct 2022 19:14:07 UTC (637 KB)
[v2] Mon, 19 Dec 2022 13:26:21 UTC (1,137 KB)
[v3] Tue, 24 Jan 2023 19:34:15 UTC (1,052 KB)
[v4] Tue, 7 Feb 2023 21:04:45 UTC (1,060 KB)
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