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It is well known that interdependence between electric power systems and other infrastructures can impact energy reliability and resilience, but it is less clear which particular interactions have the most impact. There is a need for methods that can rank the relative importance of these interdependencies. This paper describes a new tool for measuring resilience and ranking interactions. This tool, known as Computing Resilience of Infrastructure Simulation Platform (CRISP), samples from historical utility data to avoid many of the assumptions required for simulation-based approaches to resilience quantification. This paper applies CRISP to rank the relative importance of four types of interdependence (natural gas supply, communication systems, nuclear generation recovery, and a generic restoration delay) in two test cases: the IEEE 39-bus test case and a 6394-bus model of the New England/New York power grid. The results confirm industry studies suggesting that a loss of the natural gas system is the most severe specific interdependence faced by this region. 

Given increasing risk from climate-induced natural hazards, there is growing interest in the development of methods that can quantitatively measure resilience in power systems. This work quantifies resilience in electric power transmission networks in a new and comprehensive way that can represent the multiple processes of resilience. A novel aspect of this approach is the use of empirical data to develop the probability distributions that drive the computational model. This paper demonstrates the approach by measuring the impact of one potential improvement to a power system. Specifically, we measure the impact of additional distributed generation (DG) on power system resilience, and find that DG can substantially increase resilience. 

We transform historically observed line outages in a power transmission network into an influence graph that statistically describes how cascades propagate in the power grid. The influence graph can predict the critical lines that are historically most involved in cascading propagation. After upgrading these critical lines, simulating the influence graph suggests that these upgrades could mitigate large blackouts by reducing the probability of large cascades. 

There is increasing consensus that flexible demand is critical to solve challenges associated with the rapid growth of variable renewable generation and aging transmission, distri- bution and generation infrastructure. Conventional direct load control programs are largely insufficient to address these issues. This paper presents results from validation tests of a new approach to demand side management, in which an aggregated fleet of devices is managed as a virtual battery, using principles that are found in communication networks: packetization and randomization. Validation results from a cyber-physical testbed with 5000 devices and a field-trial with 82 customer-owned water heaters show that the packetized virtual battery system can effectively solve a number of different problems. Customer satisfaction survey results illustrate that the system is able to maintain a high level of service quality.