Date of Original Version

11-2013

Type

Conference Proceeding

Rights Management

© ACM, 2013. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available at http://doi.acm.org/10.1145/2536780.2536781

Abstract or Description

Large scale integration of stochastic energy resources in power systems requires probabilistic analysis approaches for comprehensive system analysis. The large-varying grid condition on the aging and stressed power system infrastructures also requires merging of offline security analyses into online operation. Meanwhile in computing, the recent rapid hardware performance growth comes from the more and more complicated architecture. Fully utilizing the computing power for specific applications becomes very difficult. Given the challenges and opportunities in both the power system and the computing fields, this paper presents the unique commodity high performance computing system solutions to the following fundamental tools for power system probabilistic and security analysis: 1) a high performance Monte Carlo simulation (MCS) based distribution probabilistic load flow solver for real-time distribution feeder probabilistic solutions. 2) A high performance MCS based transmission probabilistic load flow solver for transmission grid probabilistic analysis. 3) A SIMD accelerated AC contingency calculation solver based on Woodbury matrix identity on multi-core CPUs. By aggressive algorithm level and computer architecture level performance optimizations including optimized data structures, optimization for superscalar out-of-order execution, SIMDization, and multi-core scheduling, our software fully utilizes the modern commodity computing systems, makes the critical and computational intensive power system probabilistic and security analysis problems solvable in real-time on commodity computing systems.

DOI

10.1145/2536780.2536781

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Published In

Proceedings of the International Workshop on High Performance Computing, Networking and Analytics for the Power Grid (HiPCNA-PG), Article 2.