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.

翻译：本文提出了一种GPU加速的分布式优化算法，用于控制具有动态变化拓扑结构的有源配电系统中的多相最优潮流。为应对变化的网络配置并实现适应性分解，我们提出了一种基于组件的分解策略。然而，该方法可能导致计算时间延长，这主要源于大量细粒度组件间达成共识所需的过多迭代次数。为克服此问题，我们引入了一种将等式约束与不等式约束分离的技术，利用GPU并行化将单次迭代时间降低数个数量级，从而显著加速整体计算过程。在13至8500个节点的IEEE测试系统上进行的数值实验表明，与基于CPU的同类方法相比，所提方法具有更优越的可扩展性。