Computational Challenges for Power System Operation Yousu Chen Pacific Northwest National Laboratory yousu.chen@pnnl.gov Zhenyu Huang Pacific Northwest National Laboratory zhenyu.huang@pnnl.gov Yan Liu Pacific Northwest National Laboratory yan.liu@pnnl.gov Mark J Rice Shuangshuang Jin Pacific Northwest National Pacific Northwest National Laboratory Laboratory mark.rice@pnnl.gov shuangshuang.jin@pnnl.gov Abstract As the power grid technology evolution and information technology revolution converge, power grids are witnessing a revolutionary transition, represented by emerging grid technologies and large scale deployment of new sensors and meters in networks. This transition brings opportunities, as well as computational challenges in the field of power grid analysis and operation. This paper presents some research outcomes in the areas of parallel state estimation using the preconditioned conjugated gradient method, parallel contingency analysis with a dynamic load balancing scheme and distributed system architecture. Based on this research, three types of computational challenges are identified: highly coupled applications, loosely coupled applications, and centralized and distributed applications. Recommendations for future work for power grid applications are also presented. 1. Introduction Today’s power grids are undergoing a revolutionary transition with fast development in the areas of advanced power grid technology and information technology. The emerging power grid technologies cover the four main areas of the power system: generation, transmission, distribution and end- users, including renewable energy generation, plug-in hybrid vehicles, distributed generation and smart loads. The information technology revolution is represented by a wide-area deployment of smart sensors and meters. With these new technologies, the grid is transitioning from the traditional one-way flow of electrons to a two-way flow of both electrons and information, which results in a more complex power grid network, of increased model size, uncertainty brought by the penetration of distributed resources and smart loads, as well as a large amount of information data across the whole infrastructure. This unprecedented transition builds a technical barrier to future power grid analysis and operation: how to significantly increase the computational speed for power grid analysis in the future power grid environment to allow operators to quickly gain wide- area situational awareness, and manage the power grid more securely and more efficiently. The analysis tools employed in today’s power grid operations are mostly on serial computers. This implementation creates a bottleneck for understanding power grid characteristics in real-time, and limits responsiveness in the adverse situations. High- performance computing (HPC) techniques, as well as new modeling methods and algorithms, are critical to overcome this technical barrier. Section 2 of this paper highlights the mathematical base of general power grid computation. The research results of parallel state estimation, parallel contingency analysis, and distributed systems architecture are presented in Sections 3, 4, and 5, respectively. Section 6 describes three types of computational challenges identified based the authors’ research. Section 7 concludes this paper. 2. General mathematical view of power system computation In general, power system computation can be categorized as steady-state and dynamic analysis. Steady-state analysis is based on a time-invariant model, such as a power flow model. Steady-state analysis determines the status of a power grid at a certain time stamp, without considering the transition between different time stamps. Dynamic analysis is based on dynamic models of certain dynamic devices 2012 45th Hawaii International Conference on System Sciences 978-0-7695-4525-7 2012 U.S. Government Work Not Protected by U.S. Copyright DOI 10.1109/HICSS.2012.171 2141