# A General Stochastic Optimization Framework for Convergence Bidding

arXiv: https://arxiv.org/abs/2210.06543 · HTML: https://mlsanity.com/paper/2210.06543/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [JuMP: A Modeling Language for Mathematical Optimization](https://mlsanity.com/paper/1508.01982/) (2015)
- [Algorithmic Bidding for Virtual Trading in Electricity Markets](https://mlsanity.com/paper/1802.03010/) (2018)
- [Machine Learning-Driven Virtual Bidding with Electricity Market Efficiency Analysis](https://mlsanity.com/paper/2104.02754/) (2021)
- [A Data-Driven Convergence Bidding Strategy Based on Reverse Engineering of Market Participants' Performance: A Case of California ISO](https://mlsanity.com/paper/2109.09238/) (2021)

## Abstract (arXiv)

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.