Design and operation of the electric power grid (EPG) relies heavily on computational models. High-fidelity, full-order models are used to study transient phenomena on only a small part of the network. Reduced-order dynamic and power flow models are used when analysis involving thousands of nodes are required due to the computational demands when simulating large numbers of nodes. The level of complexity of the future EPG will dramatically increase due to large-scale deployment of variable renewable generation, active load and distributed generation resources, adaptive protection and control systems, and price-responsive demand. High-fidelity modeling of this future grid will require significant advances in coupled, multi-scale tools and their use on high performance computing (HPC) platforms. This LDRD report demonstrates SNL's capability to apply HPC resources to these 3 tasks: (1) High-fidelity, large-scale modeling of power system dynamics; (2) Statistical assessment of grid security via Monte-Carlo simulations of cyber attacks; and (3) Development of models to predict variability of solar resources at locations where little or no ground-based measurements are available.
The goal of the work discussed in this document is to understand the risk to the nation of cyber attacks on critical infrastructures. The large body of research results on cyber attacks against physical infrastructure vulnerabilities has not resulted in clear understanding of the cascading effects a cyber-caused disruption can have on critical national infrastructures and the ability of these affected infrastructures to deliver services. This document discusses current research and methodologies aimed at assessing the translation of a cyber-based effect into a physical disruption of infrastructure and thence into quantification of the economic consequences of the resultant disruption and damage. The document discusses the deficiencies of the existing methods in correlating cyber attacks with physical consequences. The document then outlines a research plan to correct those deficiencies. When completed, the research plan will result in a fully supported methodology to quantify the economic consequences of events that begin with cyber effects, cascade into other physical infrastructure impacts, and result in degradation of the critical infrastructure's ability to deliver services and products. This methodology enables quantification of the risks to national critical infrastructure of cyber threats. The work addresses the electric power sector as an example of how the methodology can be applied.
Flexible Alternating Current Transmission Systems (FACTS) devices are installed on electric power transmission lines to stabilize and regulate power flow. Power lines protected by FACTS devices can increase power flow and better respond to contingencies. The University of Missouri Rolla (UMR) is currently working on a multi-year project to examine the potential use of multiple FACTS devices distributed over a large power system region in a cooperative arrangement in which the FACTS devices work together to optimize and stabilize the regional power system. The report describes operational and security challenges that need to be addressed to employ FACTS devices in this way and recommends references, processes, technologies, and policies to address these challenges.
The large number of government and industry activities supporting the Unit of Action (UA), with attendant documents, reports and briefings, can overwhelm decision-makers with an overabundance of information that hampers the ability to make quick decisions often resulting in a form of gridlock. In particular, the large and rapidly increasing amounts of data and data formats stored on UA Advanced Collaborative Environment (ACE) servers has led to the realization that it has become impractical and even impossible to perform manual analysis leading to timely decisions. UA Program Management (PM UA) has recognized the need to implement a Decision Support System (DSS) on UA ACE. The objective of this document is to research the commercial Knowledge Discovery and Data Mining (KDDM) market and publish the results in a survey. Furthermore, a ranking mechanism based on UA ACE-specific criteria has been developed and applied to a representative set of commercially available KDDM solutions. In addition, an overview of four R&D areas identified as critical to the implementation of DSS on ACE is provided. Finally, a comprehensive database containing detailed information on surveyed KDDM tools has been developed and is available upon customer request.
This report describes the results of research and development in the area of communication among disparate species of software agents. The two primary elements of the work are the formation of ontologies for use by software agents and the means by which software agents are instructed to carry out complex tasks that require interaction with other agents. This work was grounded in the areas of commercial transport and cybersecurity.