A General Stochastic Optimization Framework for Convergence Bidding

TL;DR

This paper introduces a comprehensive stochastic optimization framework for convergence bidding in electricity markets, unifying and comparing existing approaches while providing a tractable method for optimal bid curve determination.

Contribution

It presents a novel, general linear programming-based stochastic framework for convergence bidding, connecting various existing strategies and enabling performance comparison.

Findings

Framework effectively models different bidding strategies

Numerical experiments validate the approach on CAISO market data

Provides a unified method for optimal bid curve computation

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.