Parallel GPF solution: A GPU‐CPU‐based vectorization parallelization and sparse technique for NR implementation

Abstract

As an essential simulation tool, global power flow (GPF) can approximately model mutual impacts of interactions between transmission system (TS) and distribution system (DS). However, sequential GPF lacks timely solution performance required in several applications built on top of GPF, for example, contingency analysis. To address this issue, graphic processing unit (GPU) with excellent floating-point arithmetic operations is introduced and in turn a Master-Slave-Splitting (MSS) based parallel GPF approach is proposed via vectorization parallelization under GPU-CPU heterogeneous architecture. First, the root cause behind sparsity property of GPF and its impacts on key steps of NR-based GPF approach are quantitatively analysed. Considering the studies carried out in the authors' previous work of the application of GPU and sparse techniques on TS power flow (TS-PF), its applications on parallel implementations of corresponding steps engaged in three-phase imbalanced DS power flow (DS-PF) is mainly focused on. Numerical studies validate the effects of various custom-designed parallel schemes on acceleration enhancement of individual steps in the proposed GPU-CPU parallel GPF approach.