Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Managing power balance and reserve feasibility in the AC unit commitment problem

Robert Parker, Carleton Coffrin

TL;DR

This work addresses the challenge of solving large-scale AC Unit Commitment (AC-UC) problems for day-ahead markets by developing a simple, decomposition-based benchmark algorithm that operates within the typical two-hour market window. It combines copper-plate scheduling, AC-OPF subproblems, and reserve-allocation subroutines to produce high-quality solutions for networks ranging from dozens to thousands of buses, with up to 48 time periods. The key finding is that even straightforward decompositions can yield competitive solutions, but balancing AC feasibility with reserve feasibility is critical; a parallelized variant demonstrates practical scalability for industry-scale networks. The work demonstrates the viability of AC-UC at industrial scales using current solvers, highlighting the importance of decomposition and parallelization, and sets the stage for integrating more features (e.g., contingencies) in future research.

Abstract

Incorporating the AC power flow equations into unit commitment models has the potential to avoid costly corrective actions required by less accurate power flow approximations. However, research on unit commitment with AC power flow constraints has been limited to a few relatively small test networks. This work investigates large-scale AC unit commitment problems for the day-ahead market and develops decomposition algorithms capable of obtaining high-quality solutions at industry-relevant scales. The results illustrate that a simple algorithm that only seeks to satisfy unit commitment, reserve, and AC power balance constraints can obtain surprisingly high-quality solutions to this AC unit commitment problem. However, a naive strategy that prioritizes reserve feasibility leads to AC infeasibility, motivating the need to design heuristics that can effectively balance reserve and AC feasibility. Finally, this work explores a parallel decomposition strategy that allows the proposed algorithm to obtain feasible solutions on large cases within the two hour time limit required by typical day-ahead market operations.

Managing power balance and reserve feasibility in the AC unit commitment problem

TL;DR

This work addresses the challenge of solving large-scale AC Unit Commitment (AC-UC) problems for day-ahead markets by developing a simple, decomposition-based benchmark algorithm that operates within the typical two-hour market window. It combines copper-plate scheduling, AC-OPF subproblems, and reserve-allocation subroutines to produce high-quality solutions for networks ranging from dozens to thousands of buses, with up to 48 time periods. The key finding is that even straightforward decompositions can yield competitive solutions, but balancing AC feasibility with reserve feasibility is critical; a parallelized variant demonstrates practical scalability for industry-scale networks. The work demonstrates the viability of AC-UC at industrial scales using current solvers, highlighting the importance of decomposition and parallelization, and sets the stage for integrating more features (e.g., contingencies) in future research.

Abstract

Incorporating the AC power flow equations into unit commitment models has the potential to avoid costly corrective actions required by less accurate power flow approximations. However, research on unit commitment with AC power flow constraints has been limited to a few relatively small test networks. This work investigates large-scale AC unit commitment problems for the day-ahead market and develops decomposition algorithms capable of obtaining high-quality solutions at industry-relevant scales. The results illustrate that a simple algorithm that only seeks to satisfy unit commitment, reserve, and AC power balance constraints can obtain surprisingly high-quality solutions to this AC unit commitment problem. However, a naive strategy that prioritizes reserve feasibility leads to AC infeasibility, motivating the need to design heuristics that can effectively balance reserve and AC feasibility. Finally, this work explores a parallel decomposition strategy that allows the proposed algorithm to obtain feasible solutions on large cases within the two hour time limit required by typical day-ahead market operations.
Paper Structure (19 sections, 14 equations, 3 figures, 6 tables, 4 algorithms)

This paper contains 19 sections, 14 equations, 3 figures, 6 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Problem formulation and scale
  3. AC network constraints
  4. Reserve products
  5. Contingency constraints
  6. Problem scale
  7. Methods
  8. Subroutines
  9. Copper-plate scheduling
  10. AC optimal power flow (AC-OPF)
  11. Reserve allocation
  12. Decomposition algorithms
  13. Results and discussion
  14. Full AC-UC problem evaluation
  15. Test data
  16. ...and 4 more sections

Figures (3)

  • Figure 1: Summary of objective penalties incurred for Algorithms \ref{['alg:reserve-preserving']}-\ref{['alg:balancing']} on the larger networks considered in this work. Algorithm \ref{['alg:balancing']} reliably balances AC and reserve feasibility requirements, finding solutions with low penalties.
  • Figure 2: A breakdown of solve times with Algorithm \ref{['alg:balancing']}, where the scenarios for each network have the same order as in Table \ref{['tab:balancing']}. These results highlight how AC-OPF solve times are a dominant factor in the runtime of the algorithm.
  • Figure 3: Scaling of Algorithm 4's solve time with number of threads for five of the large cases considered in this work. The results show that 32 parallel processes are sufficient for satisfying a two hour runtime limit.