GPU Accelerated Security Constrained Optimal Power Flow

TL;DR

This paper introduces a GPU-accelerated, differentiable algorithm for solving large-scale, contingency-constrained optimal power flow problems efficiently, leveraging sparse matrix operations and PyTorch implementation.

Contribution

It presents a novel GPU-based ADMM algorithm for OPF that avoids linear system solves and is fully differentiable, enabling rapid solutions for large-scale problems.

Findings

Achieves over 100x speedup compared to CPU-based solvers.

Successfully solves large problems with over 500 million variables in under a minute.

Supports a broad range of devices and constraints in the OPF formulation.

Abstract

We propose a GPU accelerated proximal message passing algorithm for solving contingency-constrained DC optimal power flow problems (OPF). We consider a highly general formulation of OPF that uses a sparse device-node model and supports a broad range of devices and constraints, e.g., energy storage and ramping limits. Our algorithm is a variant of the alternating direction method multipliers (ADMM) that does not require solving any linear systems and only consists of sparse incidence matrix multiplies and vectorized scalar operations. We develop a pure PyTorch implementation of our algorithm that runs entirely on the GPU. The implementation is also end-to-end differentiable, i.e., all updates are automatic differentiation compatible. We demonstrate the performance of our method using test cases of varying network sizes and time horizons. Relative to a CPU-based commercial optimizer, our implementation achieves well over 100x speedups on large test cases, solving problems with over 500 million variables in under a minute on a single GPU.