A General Stochastic Optimization Framework for Convergence Bidding

• URL: http://arxiv.org/abs/2210.06543v1
• Date: Wed, 12 Oct 2022 19:14:07 GMT
• Title: A General Stochastic Optimization Framework for Convergence Bidding
• Authors: Letif Mones and Sean Lovett
• Abstract summary: We introduce a general optimization framework to obtain optimal convergence (virtual) bid curves. Within this framework, we develop a linear programming-based optimization model, which produces bid prices and volumes simultaneously. We show that different approximations and simplifications in the general model lead naturally to well-known convergence bidding approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a general stochastic optimization framework to obtain optimal convergence (virtual) 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 well-known convergence bidding approaches, such as self-scheduling and opportunistic approaches.

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