An Analysis of Market-to-Market Coordination

TL;DR

This paper analyzes and enhances the market-to-market coordination process for congestion management in energy markets, introducing an ADMM-based method that achieves centralized optimality and demonstrating its superior performance through extensive case studies.

Contribution

It develops an ADMM-based enhancement for the market-to-market process, achieving optimal congestion management comparable to centralized solutions, and provides an open-source implementation.

Findings

ADMM method matches centralized optimal costs

Enhanced M2M process outperforms existing iterative methods

Open-source package facilitates broader adoption

Abstract

The growing usage of renewable energy resources has introduced significant uncertainties in energy generation, enlarging challenges for Regional Transmission Operators (RTOs) in managing transmission congestion. To mitigate congestion that affects neighboring regions, RTOs employ a market-to-market (M2M) process through an iterative method, in which they exchange real-time security-constrained economic dispatch solutions and communicate requests for congestion relief. While this method provides economic benefits, it struggles with issues like power swings and time delays. To explore the full potential of M2M enhancements, in this paper, we first analyze the current M2M iterative method practice to better understand its efficacy and identify places for improvements. Then, we explore enhancements and develop an ADMM method for the M2M coordination that optimizes congestion management. Specifically, our ADMM method can achieve a minimal cost that is the same as the cost obtained through a centralized model that optimizes multiple markets altogether. Our final case studies, across a comprehensive set of multi-area benchmark instances, demonstrate the superior performance of the proposed ADMM algorithm for the M2M process. Meanwhile, we identify scenarios where the existing M2M process fails to provide solutions as a by-product. Finally, the algorithm is implemented in an open-source package UnitCommitment.jl for easy access by a broader audience.