GPU-Accelerated Optimization Solver for Unit Commitment in Large-Scale Power Grids

2025/12/07 by Sharadga, Hussein, Mohammadi, Javad
Engineering · #Electric Power System Optimization #FOS: Computer and information sciences #FOS: Mathematics #Hardware Architecture (cs.AR) #Optimal Power Flow Distribution #Optimization and Control (math.OC) #Power System Optimization and Stability

paper · doi:10.48550/arxiv.2512.06715

openalex publication_date 2025/12/07 · openalex created_date 2025/12/10 · openalex updated_date 2026/07/28

Abstract

This work presents a GPU-accelerated solver for the unit commitment (UC) problem in large-scale power grids. The solver uses the Primal-Dual Hybrid Gradient (PDHG) algorithm to efficiently solve the relaxed linear subproblem, achieving faster bound estimation and improved crossover and branch-and-bound convergence compared to conventional CPU-based methods. These improvements significantly reduce the total computation time for the mixed-integer linear UC problem. The proposed approach is validated on large-scale systems, including 4224-, 6049-, and 6717-bus networks with long control horizons and computationally intensive problems, demonstrating substantial speed-ups while maintaining solution quality.

Citations

• Managing power balance and reserve feasibility in the AC unit commitment problem
• Accurate and Warm-Startable Linear Cutting-Plane Relaxations for ACOPF
• Optimizing Multi-Timestep Security-Constrained Optimal Power Flow for Large Power Grids
• A Parallelized, Adam-Based Solver for Reserve and Security Constrained AC Unit Commitment
• Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient

Related

• On Solving Unit Commitment with Alternating Current Optimal Power Flow on GPU
• Optimization models and algorithms for the Unit Commitment problem
• Successive Fixing for Large-Scale SCUC Using First-Order Methods
• A Fast Solution Method for Large-scale Unit Commitment Based on Lagrangian Relaxation and Dynamic Programming
• Vertex-Guided Redundant Constraints Identification for Unit Commitment