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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. 

Identifying the location of a disturbance and its magnitude is an important component for stable operation of power systems. We study the problem of localizing and estimating a disturbance in the interconnected power system. We take a model-free approach to this problem by using frequency data from generators. Specifically, we develop a logistic regression based method for localization and a linear regression based method for estimation of the magnitude of disturbance. Our model-free approach does not require the knowledge of system parameters such as inertia constants and topology, and is shown to achieve highly accurate localization and estimation performance even in the presence of measurement noise and missing data. 

We study the performance of a decentralized inte- gral control scheme for joint power grid frequency regulation and economic dispatch. We show that by properly designing the controller gains, after a power flow perturbation, the control achieves near-optimal economic dispatch while recovering the nominal frequency, without requiring any communication. We quantify the gap between the controllable power generation cost under the decentralized control scheme and the optimal cost, based on the DC power flow model. Moreover, we study the tradeoff between the cost and the convergence time, by adjusting parameters of the control scheme. Communication between generators reduces the convergence time. We identify key communication links whose failures have more significant impacts on the performance of a distributed power grid control scheme that requires information exchange between neighbors. 

Despite the infrequent occurrence of cascading power failures, their large sizes and enormous social costs mean that they contribute substantially to the overall risk to society from power failures in the grid. Therefore it is important to accurately understand the risk associated with such events. A cascading event may be triggered by a small subset of k components failing simultaneously or in rapid succession. While most prior work, including our own work into an efficient “Random Chemistry” method for risk analysis, has assumed that components fail independently, this paper proposes a method for deriving correlated outage probabilities such that pairs of branches that are proximate in space are more likely to fail together than distant ones. Combining Random Chemistry risk analysis with this approach to correlated outage probabilities shows that overall blackout risk can greatly increase with even small amounts of correlation. Results from the 2383-bus Polish test case under various load levels illustrate the substantial impact that correlation has on blackout risk.