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An Analysis of Market-to-Market Coordination

Weihang Ren, Alinson S. Xavier, Fengyu Wang, Yongpei Guan, Feng Qiu

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.

An Analysis of Market-to-Market Coordination

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.
Paper Structure (18 sections, 2 theorems, 6 equations, 4 figures, 3 tables, 1 algorithm)

This paper contains 18 sections, 2 theorems, 6 equations, 4 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. M2M Congestion
  3. Related Literature
  4. Challenge and Contribution
  5. Centralized Formulation
  6. Current Iterative Method
  7. An ADMM Approach for M2M
  8. Computational Experiments
  9. Instance Construction
  10. Decomposing the given power system network
  11. Identifying the most congested flowgate
  12. Allocate flow capacities to the two RTOs
  13. Evaluation of Iterative Method
  14. Power Swings
  15. Non-Stopping Behavior Despite No Change in Solution
  16. ...and 3 more sections

Key Result

Proposition 1

Let $z_0$ be the optimal cost of the combined SCED for neighboring RTOs, $z_1$be the optimal cost of the centralized method described in formulation model:cen, and $z_2$be the cost from any M2M coordination. Then, we have

Figures (4)

  • Figure 1: An example of loop flow. The blue circles on the left represent the buses in RTO A, while the pink circles on the right represent the buses in RTO B. The arrows show power generated from bus G in RTO A flowing through RTO B before reaching its destination bus L in RTO A.
  • Figure 2: Power swing issue of the iterative method in the "case2383wp-ll" instance. The $x$-axis represents the number of iterations in the iterative M2M method, and the $y$-axis represents the flow amount in MW.
  • Figure 3: The ADMM M2M results for the "case2383wp-ll" instance. The $x$-axes in both subplots represent the iteration numbers in the ADMM algorithm, while the $y$-axis represents the global residual in the upper subplot and represents the total cost in the lower subplot.
  • Figure 4: The ADMM M2M results for the "case3375wp-ll" and "case6468rte-ll" instances. The $x$-axes in both subplots represent the iteration numbers in the ADMM algorithm, while the $y$-axis in both subplots represents the shadow price of the flowgate for two RTOs.

Theorems & Definitions (5)

  • Proposition 1
  • proof
  • Remark 1
  • Proposition 2
  • proof