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Research Highlight

Towards Faster-than-real-time Simulation Using Parallel-in-time Algorithm and High-performance Computing

The Science
A multi-institutional team of ORNL has utilized the latest computational algorithms and parallelization techniques to enable faster than real-time simulations and applied it to the power system network whose time-domain model represents very large and highly nonlinear differential-algebraic equations (DAEs) with fast dynamic characteristics. Solving thousands of nonlinear DAEs is a very challenging task. Hence, it is crucial to distribute the computation for speeding up the time-domain simulations. To this end, the team has innovatively designed parallel-in-time algorithm, which can significantly improve the computational performance. 

The Impact
The new computational framework highly utilizes the high-performance computing platforms and yields more than 10x computational performance over existing simulation approaches. This increased computational performance has great potential for supporting real-time operation and control of large-scale and complex systems. 

Office of Electricity through the Advanced Grid Modeling Program

Publication for this work
Byungkwon Park, et al., “Examination of Semi-Analytical Solution Methods in the Coarse Operator of Parareal Algorithm for Power System Simulation,” IEEE Trans. Power Syst., vol. 36, no. 6, Nov 2021 

With continuing advances in high-performance parallel computing platforms, parallel algorithms have become powerful tools for development of faster than real-time power system dynamic simulations. In particular, it has been demonstrated in recent years that parallel-in-time (Parareal) algorithms have the potential to achieve such an ambitious goal. The selection of a fast and reasonably accurate coarse operator of the Parareal algorithm is crucial for its effective utilization and performance. This paper examines semi-analytical solution (SAS) methods as the coarse operators of the Parareal algorithm and explores performance of the SAS methods to the standard numerical time integration methods. Two promising time-power series-based SAS methods were considered; Adomian decomposition method and Homotopy analysis method with a windowing approach for improving the convergence. Numerical performance case studies on 10-generator 39-bus system and 327-generator 2383-bus system were performed for these coarse operators over different disturbances, evaluating the number of Parareal iterations, computational time, and stability of convergence. All the coarse operators tested with different scenarios have converged to the same corresponding true solution (if they are convergent) and the SAS methods provide comparable computational speed, while having more stable convergence to the true solution in many cases.