Towards Efficient Alternating Current Optimal Power Flow Analysis on Graphical Processing Units

TL;DR

This paper demonstrates the first significant acceleration of sparse alternating current optimal power flow (ACOPF) analysis on GPUs, achieving promising speed-ups for large-scale power grid systems compared to traditional CPU methods.

Contribution

The paper introduces a GPU-based solution for sparse ACOPF analysis, focusing on accelerating the linear solver to improve computational efficiency for large-scale power systems.

Findings

GPU implementation achieves notable speed-up over CPU methods

First demonstration of accelerated sparse ACOPF on GPUs

Effective acceleration on large-scale synthetic power grids

Abstract

We present a solution of sparse alternating current optimal power flow (ACOPF) analysis on graphical processing unit (GPU). In particular, we discuss the performance bottlenecks and detail our efforts to accelerate the linear solver, a core component of ACOPF that dominates the computational time. ACOPF analyses of two large-scale systems, synthetic Northeast (25,000 buses) and Eastern (70,000 buses) U.S. grids [1], on GPU show promising speed-up compared to analyses on central processing unit (CPU) using a state-of-the-art solver. To our knowledge, this is the first result demonstrating a significant acceleration of sparse ACOPF on GPUs.