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

TL;DR

This paper introduces a GPU-accelerated solver for the unit commitment problem in large-scale power grids, leveraging the PDHG algorithm to significantly reduce computation time while maintaining solution quality.

Contribution

It presents a novel GPU-based optimization approach using PDHG for faster solutions in large-scale power grid unit commitment problems.

Findings

Achieves substantial speed-ups on large-scale systems.

Maintains solution quality comparable to CPU methods.

Validates effectiveness on systems with over 6000 buses.

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.