Accelerated Computation and Tracking of AC Optimal Power Flow Solutions using GPUs

TL;DR

This paper introduces a GPU-accelerated, scalable method for rapidly solving and tracking AC optimal power flow problems, enabling real-time grid management amid fluctuating load and generation conditions.

Contribution

The paper proposes a novel GPU-based decomposition approach using ADMM for fast ACOPF computation and tracking, improving speed and scalability over traditional methods.

Findings

Achieves significant speedup in solving large-scale ACOPF problems.

Demonstrates effective warm-start convergence for real-time tracking.

Handles grids with up to 70,000 buses efficiently.

Abstract

We present a scalable solution method based on an alternating direction method of multipliers and graphics processing units (GPUs) for rapidly computing and tracking a solution of alternating current optimal power flow (ACOPF) problem. Such a fast computation is particularly useful for mitigating the negative impact of frequent load and generation fluctuations on the optimal operation of a large electrical grid. To this end, we decompose a given ACOPF problem by grid components, resulting in a large number of small independent nonlinear nonconvex optimization subproblems. The computation time of these subproblems is significantly accelerated by employing the massive parallel computing capability of GPUs. In addition, the warm-start ability of our method leads to faster convergence, making the method particularly suitable for fast tracking of optimal solutions. We demonstrate the performance of our method on grids having up to 70,000 buses by solving associated optimal power flow problems with both cold start and warm start.