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

2025-01-14

AI Summary

To address the computational challenge of real-time optimal power flow (OPF) in active distribution networks with dynamic topologies, this paper proposes a GPU-accelerated distributed multiphase OPF algorithm. Methodologically, it introduces a novel component-level network decomposition coupled with a coordinated separation of equality and inequality constraints, integrated within a distributed alternating direction method of multipliers (ADMM) framework and multiphase power flow modeling; a topology-aware adaptive reconfiguration strategy is further embedded to enhance robustness. Validated on IEEE test systems (13–8500 nodes), the GPU implementation reduces consensus iterations by 42%–68% and cuts per-iteration time by one to two orders of magnitude versus CPU-based counterparts, achieving over 100× speedup in total solution time. The algorithm demonstrates strong scalability and millisecond-level response potential, offering an efficient and practical technical pathway for real-time coordinated optimization in dynamically reconfigurable distribution grids.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Power Distribution Optimization
• Adaptive Network Conditions
• Reduced Computation Time

Innovation

• GPU acceleration
• Parallel computing
• Adaptive network decomposition