A General Stochastic Optimization Framework for Convergence Bidding

Letif Mones; Sean Lovett

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arXiv · 2210.06543

A General Stochastic Optimization Framework for Convergence Bidding

Letif Mones·Sean Lovett

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Abstract

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.

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BibTeX RIS

Letif Mones, Sean Lovett. 2022-10-12. A General Stochastic Optimization Framework for Convergence Bidding. https://arxiv.org/abs/2210.06543

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