2025/01/14 by Minseok Ryu, Ryu, Minseok, Geunyeong Byeon +3
Energy · Engineering · #Distributed #FOS: Computer and information sciences #FOS: Mathematics #Optimal Power Flow Distribution #Optimization and Control (math.OC) #Parallel #Power Systems and Renewable Energy #Smart Grid and Power Systems #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2501.08293
openalex publication_date 2025/01/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.