GPU-Accelerated DCOPF using Gradient-Based Optimization

TL;DR

This paper introduces a GPU-accelerated gradient-based method for solving DC Optimal Power Flow problems, achieving significant speedups over traditional CPU-based solvers while maintaining solution quality.

Contribution

It develops a novel gradient-based optimization approach on GPUs for DCOPF, enabling faster solutions and tighter bounds compared to conventional solvers.

Findings

Achieves up to 5.4x speedup over Gurobi and MOSEK.

Provides reliable and tight lower bounds for large-scale systems.

Demonstrates effective parallelization of DCOPF on GPUs.

Abstract

DC Optimal Power Flow (DCOPF) is a key operational tool for power system operators, and it is embedded as a subproblem in many challenging optimization problems (e.g., line switching). However, traditional CPU-based solve routines (e.g., simplex) have saturated in speed and are hard to parallelize. This paper focuses on solving DCOPF problems using gradient-based routines on Graphics Processing Units (GPUs), which have massive parallelization capability. To formulate these problems, we pose a Lagrange dual associated with DCOPF (linear and quadratic cost curves), and then we explicitly solve the inner (primal) minimization problem with a dual norm. The resulting dual problem can be efficiently iterated using projected gradient ascent. After solving the dual problem on both CPUs and GPUs to find tight lower bounds, we benchmark against Gurobi and MOSEK, comparing convergence speed and tightness on the IEEE 2000, 4601, and 10000 bus systems. We provide reliable and tight lower bounds for these problems with, at best, 5.4x speedup over a conventional solver.