GPU-Accelerated Sequential Quadratic Programming Algorithm for Solving ACOPF

TL;DR

This paper introduces a GPU-accelerated SQP algorithm for ACOPF that leverages ADMM for parallel subproblem solving, significantly improving solution speed for large-scale power flow problems.

Contribution

The paper develops a scalable GPU-based SQP method for ACOPF, integrating ADMM for parallelization, and implements it in a Julia package to outperform traditional solvers on large instances.

Findings

GPU-based SQP reduces solution time for large ACOPF instances.

Parallel ADMM subproblem solving effectively utilizes GPU architecture.

The proposed method outperforms CPU-based solvers like Ipopt on large problems.

Abstract

Sequential quadratic programming (SQP) is widely used in solving nonlinear optimization problem, with advantages of warm-starting solutions, as well as finding high-accurate solution and converging quadratically using second-order information, such as the Hessian matrix. In this study we develop a scalable SQP algorithm for solving the alternate current optimal power flow problem (ACOPF), leveraging the parallel computing capabilities of graphics processing units (GPUs). Our methodology incorporates the alternating direction method of multipliers (ADMM) to initialize and decompose the quadratic programming subproblems within each SQP iteration into independent small subproblems for each electric grid component. We have implemented the proposed SQP algorithm using our portable Julia package ExaAdmm.jl, which solves the ADMM subproblems in parallel on all major GPU architectures. For numerical experiments, we compared three solution approaches: (i) the SQP algorithm with a GPU-based ADMM subproblem solver, (ii) a CPU-based ADMM solver, and (iii) the QP solver Ipopt (the state-of-the-art interior point solver) and observed that for larger instances our GPU-based SQP solver efficiently leverages the GPU many-core architecture, dramatically reducing the solution time.