A GPU-Accelerated Distributed Algorithm for Optimal Power Flow in Distribution Systems

TL;DR

This paper introduces a GPU-accelerated distributed algorithm for optimal power flow in active distribution systems, significantly reducing computation time and improving scalability for large and dynamic networks.

Contribution

It presents a novel GPU-based parallelization technique that segregates constraints to accelerate distributed optimization in complex power systems.

Findings

Achieves orders-of-magnitude reduction in per-iteration time.

Demonstrates superior scalability on large IEEE test systems.

Effectively handles dynamic network topologies.

Abstract

We propose a GPU-accelerated distributed optimization algorithm for controlling multi-phase optimal power flow in active distribution systems with dynamically changing topologies. To handle varying network configurations and enable adaptable decomposition, we advocate a componentwise decomposition strategy. However, this approach can lead to a prolonged computation time mainly due to the excessive iterations required for achieving consensus among a large number of fine-grained components. To overcome this, we introduce a technique that segregates equality constraints from inequality constraints, enabling GPU parallelism to reduce per-iteration time by orders of magnitude, thereby significantly accelerating the overall computation. Numerical experiments on IEEE test systems ranging from 13 to 8500 buses demonstrate the superior scalability of the proposed approach compared to its CPU-based counterparts.