Smart Partitioning Based Large-Scale Power System Analysis on High-Performance Computing Platform: Modeling, Algorithms, and Computations

Mississippi State University, Mississippi State MS

Investigators

Abstract

With the ongoing integration of renewable energy, distributed generation, energy storage, smart loads, and new market-driven incentives, today's power system is undergoing an evolutionary transformation towards a more stochastic and dynamic paradigm for power system operation. In order to handle the ever-growing size, complexity, and heterogeneity of the mathematical problems resulting from power system expansion and evolution, and to conduct rapid and accurate power system analyses especially with the aid of advanced computing techniques, the project team envisions a new concept of "Smart Partitioning" as an innovative decomposition method to enhance the analysis of large-scale and complex power systems by leveraging a high performance parallel computing platform. The research will systematically explore this novel domain decomposition concept in terms of problem modeling, solution algorithms and computational implementation to transform how steady state and dynamic large-scale power system analyses are efficiently performed on a high performance parallel computing platform. The research will help expedite the proliferation of applications based on the high-performance parallel computing platform to enhance the economics, reliability and stability of electric power systems and accelerate the development and deployment of other smart grid technologies. As an integral part of this project, the educational plan focuses on power system applications on high-performance parallel computing technology and emphasizes how the knowledge and results gained from the research can be directly channeled into the project's education goals via standard academic means, stimulate students' interest in large-scale power system studies, and empower industry engineers with skills for life-long learning. The proposed smart partitioning based decomposition method could revolutionize power system analysis by constructing a virtual domain based, flexible, scalable, and efficient partitioning structure, and utilizing parallel optimization science and parallel computing techniques to tame the size and computational difficulty of large-scale power system analysis problems. The proposed power system decomposition will no longer need to be formulated around one particular domain like functions, scenarios, geographical areas or time. Instead, it can be made based on one or a set of virtual domains to fully exploit the inherent structures, physical coherency, component characteristics and unique properties of the power system. The proposed smart partition strategies have distinct and unique attributes that are consistent with the objectives of modern power system analysis, including flexibility and extensibility of the modeling, scalability and parallelism of the algorithms, and efficiency and usability of the computations. The key research tasks include (1) developing virtual domain decomposition based flexible modeling for both steady state and dynamic analyses of the power system composed of heterogeneous types of power production and delivery elements; (2) exploring scalable and effective parallel computing algorithms that process the proposed power system studies in a fully parallel manner and optimize the information interactions among calculation tasks; and (3) implementing efficient and rapid high-performance parallel computations that optimally map the computation tasks to processors and balance the arrangement of tasks.