Comparative Implementation of High Performance Computing for Power System Dynamic Simulations
Comparative Implementation of High Performance Computing for Power System Dynamic Simulations
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DOI：
10.1109/tsg.2016.2647220
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发表时间：
2017-05-01
影响因子：
9.6
通讯作者：
Chen, Yousu
中科院分区：
文献类型：
--
作者：
Jin, Shuangshuang;Huang, Zhenyu;Chen, Yousu
暂态稳定评估的动态仿真是电力系统规划和运行中最重要、也是最密集的计算任务之一。一些商业软件工具提供了在并行计算机上同时执行多个动态模拟的功能，例如应急分析。然而，单个动态模拟仍然是一个耗时的过程，在一个计算核心上依次执行，因为工具最初是设计的。现代高性能计算（HPC）有望在不影响计算精度的情况下，通过并行内核算法来加速单个动态模拟。单个动态仿真的并行化是一个比偶然性并行计算更具挑战性的问题。它要求仿真算法和计算硬件之间有很好的匹配。本文为这种匹配提供指导，以便设计和实现并行动态仿真，以最大限度地利用计算硬件和仿真性能。该指南是通过在两种最先进的HPC环境中比较实现四种并行动态仿真方案得出的：1)消息传递接口和2)开放多处理。深入研究了并行化动态仿真的可扩展性和加速性能，以确定仿真算法和计算硬件配置的影响。给出了几个测试用例来说明派生的指南。
Dynamic simulation for transient stability assessment is one of the most important, but intensive, computational tasks for power system planning and operation. Several commercial software tools provide functionality for performing multiple dynamic simulations such as those in contingency analysis simultaneously on parallel computers. Nevertheless, a single dynamic simulation is still a time consuming process performed sequentially on one single computing core as the tools were originally designed. Modern high performance computing (HPC) holds the promise to accelerate a single dynamic simulation by parallelizing its kernel algorithms without compromising computational accuracy. Parallelizing a single dynamic simulation is a much more challenging problem than the contingency-type parallel computing. It requires a good match between simulation algorithms and computing hardware. This paper provides guidance for such a match so as to design and implement parallel dynamic simulation to maximize the utilization of computing hardware and the performance of the simulation. The guidance is derived through comparative implementation of four parallel dynamic simulation schemes in two state-of-the-art HPC environments: 1) message passing interface and 2) open multi-processing. The scalability and speedup performance of parallelized dynamic simulation are thoroughly studied to determine the impact of simulation algorithms and computing hardware configurations. Several testing cases are presented to illustrate the derived guidance.