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In 2017, three sub-synchronous resonance (SSR) events were reported in the transmission system of Electric Reliability Council of Texas (ERCOT). These three events with different consequences are due to the same cause, i.e., Type-3 wind farms radially connected to a series compensated transmission line. The objectives of this paper are: 1) to build a testbed to replicate these real-world events and investigate what causes the different consequences; 2) to find out the impact of wind penetration level and wind speed on SSR; and 3) to investigate how to mitigate SSR. The replication testbed is built in MATLAB/Simpowersystems with each wind farm represented by a single aggregated wind turbine with full dynamics. The challenge of replication is the limited information about those three events, the ERCOT system, and the wind farms. Nevertheless, relying on simplification and assumptions, we are able to replicate the three events. Our contributions include the configured testbed for Type-3 wind farm SSR studies, diagnosis of impacting factors on SSR, and the confirmation of an SSR mitigation strategy through grid-side converter ac voltage command signal modulation. [ABSTRACT FROM AUTHOR]

This paper demonstrates that false data injection (FDI) attacks are extremely limited in their ability to cause physical consequences on $N-1$ reliable power systems operating with real-time contingency analysis (RTCA) and security constrained economic dispatch (SCED). Prior work has shown that FDI attacks can be designed via an attacker-defender bi-level linear program (ADBLP) to cause physical overflows after re-dispatch using DCOPF. In this paper, it is shown that attacks designed using DCOPF fail to cause overflows on $N-1$ reliable systems because the system response modeled is inaccurate. An ADBLP that accurately models the system response is proposed to find the worst-case physical consequences, thereby modeling a strong attacker with system level knowledge. Simulation results on the synthetic Texas system with 2000 buses show that even with the new enhanced attacks, for systems operated conservatively due to $N-1$ constraints, the designed attacks only lead to post-contingency overflows. Moreover, the attacker must control a large portion of measurements and physically create a contingency in the system to cause consequences. Therefore, it is conceivable but requires an extremely sophisticated attacker to cause physical consequences on $N-1$ reliable power systems operated with RTCA and SCED. [ABSTRACT FROM AUTHOR]

Stochastic optimization can be used to model predictable but uncertain element failures, in an attempt to enhance system reliability in power system operation and planning. In practical applications, such as preventive operation during severe weather, the uncertainty set is often very large. This will lead to two challenges: (i) every possible scenario cannot be practically identified; and (ii) the computational demands of stochastic optimization with a large scenario set cannot be met. To address these challenges, this paper develops a multidimensional scenario selection method, which creates a rather small but representative set of scenarios. The developed method makes use of failure features as well as network features of the elements that may fail, to achieve a superior performance. The simulations studies on a synthetic large-scale Texas system, show the dominant performance of the method compared to existing algorithms in the literature, as well as common industry practices. Due to its effectiveness, the method presented in this paper enables computationally efficient implementation of stochastic power system operation and planning software tools. Such stochastic tools will improve system reliability and efficiency through enhanced use of the existing resources, without requiring any expensive system upgrade. [ABSTRACT FROM AUTHOR]