Tao Cui and Franz Franchetti (Proc. High Performance Computing, Networking and Analytics for the Power Grid (HiPCNA-PG), 2013)
Power System Probabilistic and Security Analysis on Commodity High Performance Computing Systems
Preprint (1.2 MB)
Published paper (link to publisher)

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 computation power for specific applications becomes very difficult. Given the challenges and opportunities in both the power system and computing fields, this paper presents the unique high performance commodity computing system solution 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 solution. 2) A high performance MCS based transmission probabilistic load flow solver for transmission grid 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.

Large Scale, SIMD vectorization, Optimizing, Power systems, Probabilistic Load Flow, Simulation, Smart grid, Electric power systems