Probabilistic Modelling of Robustness and Resilience of Power Grid Systems

The present paper proposes a framework for the modeling and analysis of resilience of networked power grid systems. A probabilistic systems model is proposed based on the JCSS Probabilistic Model Code (JCSS, 2001) and deterministic engineering systems modeling techniques such as the DC flow model. This probabilistic systems model facilitates the propagation of the dominating uncertainties affecting the system performances, including characteristics of geo-hazard disturbances, internal flow, the resistances of the system with respect to these and effects of internal redistribution and subsequent possible cascading failure event scenarios (Nan and Sansavini, 2017). The concept of direct and indirect consequences proposed by the Joint Committee on Structural Safety (JCSS, 2008) is utilized to model the associated consequences. To facilitate a holistic modeling of robustness and resilience, and to identify how these characteristics may be optimized these characteristics, the power grid system is finally interlinked with its fundamental interdependent systems, i.e. a societal model, a regulatory system and control feedback loops.
The proposed framework is exemplified with reference to optimal decision support for resilience management in the IEEE Reliability Test System-1996. Parameter studies are undertaken to assess the significance of decision options on the system performance characteristics. Finally, the presented framework and example results are discussed and suggestions for further developments are provided.