首页> 外文会议>2012 SC Companion: High Performance Computing, Networking, Storage and Analysis. >EmPower: An Efficient Load Balancing Approach for Massive Dynamic Contingency Analysis in Power Systems
【24h】

EmPower: An Efficient Load Balancing Approach for Massive Dynamic Contingency Analysis in Power Systems

机译:EmPower:一种有效的负载均衡方法,用于电力系统中的大规模动态应变分析

获取原文
获取原文并翻译 | 示例

摘要

Power system simulations involving solution of thousands of stiff differential and algebraic equations (DAE) are computationally intensive and yet crucial for grid security and reliability. Online simulations of a large number of contingencies require very high computational efficiency. Furthermore, since the simulation times across the contingencies vary considerably, dynamic load balancing of parallel contingency analysis (CA) is required to ensure maximum resource utilization. However, the state-of-the-art contingency analysis techniques fail to fulfill this requirement. In this paper, we present EmPower, an Efficient load balancing approach for massive dynamic contingency analysis in Power systems. For single contingency analysis, EmPower uses time domain simulations and incorporates efficient numerical algorithms for solving the DAE. Further, the contingency analysis approach is scaled for large scale contingency analysis using MPI based parallelization. For enabling an efficient, non-blocking implementation of work-stealing, multithreading is employed within each processor. Simulations of thousands of contingencies on a supercomputer have been performed and the results show the effectiveness of EmPower in providing good scalability and huge computational savings.
机译:电力系统仿真涉及数千个刚性微分方程和代数方程(DAE)的求解,计算量大,但对电网安全性和可靠性至关重要。大量意外事件的在线仿真需要非常高的计算效率。此外,由于跨意外事件的仿真时间相差很大,因此需要并行偶发分析(CA)的动态负载平衡以确保最大程度地利用资源。但是,最新的权变分析技术无法满足这一要求。在本文中,我们介绍了EmPower,这是一种用于电力系统中大规模动态偶发性分析的有效负载平衡方法。对于单个偶发事件分析,EmPower使用时域仿真并结合了有效的数值算法来求解DAE。此外,权变分析方法被缩放以使用基于MPI的并行化进行大规模的权变分析。为了实现工作窃取的高效,非阻塞实现,在每个处理器中采用了多线程。在超级计算机上进行了数千种突发事件的仿真,结果表明EmPower在提供良好的可伸缩性和节省大量计算量方面是有效的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号