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Two-Stage Adaptive Robust Optimization Model for Joint Unit Maintenance and Unit Commitment Considering Source-Load Uncertainty

Hongrui Lu, Yuxiong Huang, Tong He, Gengfeng Li

TL;DR

The paper tackles joint unit maintenance and unit commitment under wind and load uncertainty by formulating a two-stage adaptive robust optimization model with a min-max-min structure. It solves the model using an inexact column-and-constraint generation (i-C&CG) framework, augmented by an outer-approximation (OA) approach to handle bilinear terms in the sub-problem. The uncertainty set captures load and wind deviations with budget constraints, enabling robust scheduling decisions that balance reliability and cost. Case studies on the RTS-79 system show the approach improves robustness and cost-efficiency compared to decoupled or non-robust strategies, and the i-C&CG method provides substantial computational advantages over traditional C&CG. The work contributes a practical, scalable framework for robust J-UMUC in renewable-rich power systems.

Abstract

Unit maintenance and unit commitment are two critical and interrelated aspects of electric power system operation, both of which face the challenge of coordinating efforts to enhance reliability and economic performance. This challenge becomes increasingly pronounced in the context of increased integration of renewable energy and flexible loads, such as wind power and electric vehicles, into the power system, where high uncertainty is prevalent. To tackle this issue, this paper develops a two-stage adaptive robust optimization model for the joint unit maintenance and unit commitment strategy. The first stage focuses on making joint decisions regarding unit maintenance and unit commitment, while the second stage addresses economic dispatch under the worst-case scenarios of wind power and load demand. Then a practical solution methodology is proposed to solve this model efficiently, which combines the inexact column-and-constraint generation algorithm with an outer approximation method. Finally, the economic viability and adaptability of the proposed method is demonstrated based on the RTS-79 test system.

Two-Stage Adaptive Robust Optimization Model for Joint Unit Maintenance and Unit Commitment Considering Source-Load Uncertainty

TL;DR

The paper tackles joint unit maintenance and unit commitment under wind and load uncertainty by formulating a two-stage adaptive robust optimization model with a min-max-min structure. It solves the model using an inexact column-and-constraint generation (i-C&CG) framework, augmented by an outer-approximation (OA) approach to handle bilinear terms in the sub-problem. The uncertainty set captures load and wind deviations with budget constraints, enabling robust scheduling decisions that balance reliability and cost. Case studies on the RTS-79 system show the approach improves robustness and cost-efficiency compared to decoupled or non-robust strategies, and the i-C&CG method provides substantial computational advantages over traditional C&CG. The work contributes a practical, scalable framework for robust J-UMUC in renewable-rich power systems.

Abstract

Unit maintenance and unit commitment are two critical and interrelated aspects of electric power system operation, both of which face the challenge of coordinating efforts to enhance reliability and economic performance. This challenge becomes increasingly pronounced in the context of increased integration of renewable energy and flexible loads, such as wind power and electric vehicles, into the power system, where high uncertainty is prevalent. To tackle this issue, this paper develops a two-stage adaptive robust optimization model for the joint unit maintenance and unit commitment strategy. The first stage focuses on making joint decisions regarding unit maintenance and unit commitment, while the second stage addresses economic dispatch under the worst-case scenarios of wind power and load demand. Then a practical solution methodology is proposed to solve this model efficiently, which combines the inexact column-and-constraint generation algorithm with an outer approximation method. Finally, the economic viability and adaptability of the proposed method is demonstrated based on the RTS-79 test system.

Paper Structure

This paper contains 16 sections, 24 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Model Formulation
  3. Uncertainty Set
  4. Two-Stage Robust Optimization Model
  5. Unit maintenance constraints
  6. Unit commitment constraints
  7. Economic dispatch constraints
  8. Model Reformulation
  9. Solution Methodology
  10. The Master Problem
  11. The Sub-Problem
  12. Solution Algorithm
  13. Case Study
  14. Cost Efficiency and Adaptability
  15. Analysis of the Solution Algorithm
  16. ...and 1 more sections

Figures (2)

  • Figure 1: The illustration of the overall solution algorithm.
  • Figure 2: The net load curve and system available generating capacity around G24's maintenance. (a) Case 3. (b) Case 4.