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This paper discusses how a cyber attack could take advantage of torsional resonances in the shaft of turbo-generators to inflict severe physical damage to a power system. If attackers were able to take over the control of a battery energy storage device, they could modulate the injection of this device at a frequency that matches one of the sub-synchronous resonance frequencies of a generator. Small changes in injection might be sufficient to excite one of these mechanical resonances, resulting in metal fatigue and ultimately a catastrophic failure in the shaft of the generator. Using a state-space model of the electromechanical system, the paper develops transfer functions linking the magnitude of the malicious injections to the magnitude of oscillations in the speed and angle of the various masses connected to the shaft. Numerical results from a two-area power system demonstrate the existence of vulnerable frequencies and show that damaging mechanical oscillations can be triggered without causing easily detectable signals at the generator terminals. 

Abstract Objectives: To determine if solar-powered battery systems could be successfully used for electricity-dependent medical devices by families during a power outage. Methods: We assessed the use of and satisfaction with solar-powered battery systems distributed to 15 families following Hurricane Maria in rural Puerto Rico. Interviews were conducted in July 2018, 3 mo following distribution of the systems. Results: The solar-powered battery systems powered refrigeration for medications and prescribed diets, asthma therapy, inflatable mattresses to prevent bedsores, and continuous positive airway pressure machines for sleep apnea. Despite some system problems, such as inadequate power, defective cables, and blown fuses, families successfully dealt with these issues with some outside help. Almost all families were pleased with the systems and a majority would recommend solar-powered battery systems to a neighbor. Conclusions: Families accepted and successfully used solar-powered battery systems to power medical devices. Solar-powered battery systems should be considered as alternatives to generators for power outages after hurricanes and other disasters. Additional research and analysis are needed to inform policy on increasing access to such systems. 

Although electricity transmission systems are typically very robust, the impacts that arise when they are disrupted motivate methods for analyzing outage risk. For example, N-k interdiction models were developed to characterize disruptions by identifying the sets of k power system components whose failure results in “worst case” outages. While such models have advanced considerably, they generally neglect how failures outside the power system can cause large-scale outages. Specifically, failures in natural gas pipeline networks that provide fuel for gas-fired generators can affect the function of the power grid. In this study, we extend N-k interdiction modeling to gas pipeline networks. We use recently developed convex relaxations for natural gas flow equations to yield tractable formulations for identifying sets of k components whose failure can cause curtailment of natural gas delivery. We then present a novel cutting-plane algorithm to solve these problems. Finally, we use test instances to analyze the performance of the approach in conjunction with simulations of outage effects on electrical power grids. 

Natural disasters, such as hurricanes, large wind and ice storms, typically require the repair of a large number of components in electricity distribution networks. Since power cannot be restored before the completion of repairs, optimally scheduling the available crews to minimize the cumulative duration of the customer interruptions reduces the harm done to the affected community. We have previously proposed approximation algorithms to schedule post-disaster repairs in electricity distribution networks with complete damage information [1]. In this paper, we extend our previous work to the case with incomplete damage information. We model this problem as scheduling a set of jobs with stochastic processing times on parallel identical machines in order to minimize the total weighted energization time. A linear programming (LP) based list scheduling policy is proposed and then analyzed in terms of theoretical performance.