Evaluation Of Year 2020 IEEE RTS Generation Reliability Indices

Preston, E.G.; Barrows, Clayton

doi:10.1109/pmaps.2018.8440394

Cited by 11 publications

(4 citation statements)

References 4 publications

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“…test this idea, we use the 9-bus test case, and we take the normalized wind and solar aggregated generation data from the RTS-GMLC [55]. Then, we include the wind generation on bus 5 and the solar generation on bus 6 with different penetration values.…”

Section: B Learning the Implicit Power System Measurement Modelmentioning

confidence: 99%

Attack Power System State Estimation by Implicitly Learning the Underlying Models

Costilla-Enriquez

Weng

2023

IEEE Trans. Smart Grid

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False data injection attacks (FDIAs) are a real and latent threat in modern power systems networks due to the unprecedented integration of data acquisition systems. It is of utmost importance to understand attacking mechanisms to design countermeasures. To successfully deploy a FDIA, most past FDIA strategies need privileged power system information, which is carefully held by the power system operator. Newer approaches circumvent this issue by solely relying on intercepted measurement data, but they lack mathematical warranties of succeeding. This paper exposes power systems' vulnerability by showing that it is possible to deploy an attack without confidential information and, at the same time, to have a high probability of being successful. We present a scheme that learns (1) the implicit power system measurement distribution and (2) a surrogate of the unknown state estimator model. The proposed framework utilizes a Wasserstein generative adversarial network to learn the measurement distribution and an autoencoder to learn the unknown state estimator model. Additionally, we present a convergence proof that ensures that the proposed framework converges to the power system measurement distribution. The proposed method is demonstrated to be successful via extensive simulation on IEEE 9-, 14-, 57-, 118-, and 300-bus test cases.

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Section: B Learning the Implicit Power System Measurement Modelmentioning

confidence: 99%

Attack Power System State Estimation by Implicitly Learning the Underlying Models

Costilla-Enriquez

Weng

2023

IEEE Trans. Smart Grid

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“…Summaries of these scenarios and qualitative indications of their computational complexity are provided in Table A.2. The Conv scenario is constructed according to the work of Preston and Barrows (2018). It is based on a simple convolution method that only considers the net load duration curve and thermal generator forced outages, with no explicit modeling of the power network or chronological system operations.…”

Section: Reliability-focused Reservementioning

confidence: 99%

A Guide for Improved Resource Adequacy Assessments in Evolving Power Systems: Institutional and Technical Dimensions

Carvallo

Zhang

Leibowicz

et al. 2023

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“…To evaluate the proposed prioritization method, the Reliability Test System of the Grid Modernization Lab Consortium (RTS-GMLC) test system [6] was used. RTS-GMLC is an updated version of the IEEE RTS-96 test system [7], with modernizing changes [8]:…”

Section: Model Descriptionmentioning

confidence: 99%

“…The renewable generation profile of RTS-GMLC is based on the Southwest U.S., a region filled with solar and wind resources [6]. Today's energy portfolio of Oregon and the PNW, however, differs from this: about a half and a tenth of the generated power comes from hydro and wind resources, respectively [9] - [10].…”

Section: B Energy Portfoliomentioning

confidence: 99%

Rapid Method for Generation Prioritization during System Restoration with Renewable Resources

Mate,

Cotilla-Sanchez

2021

Preprint

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Quick and reliable power system restoration is critically important after natural disasters or other sudden threats, such as cyber-attacks. Leveraging renewable resources in system restoration shortens recovery times, resulting in prevented life-loss and avoided economic-loss, and improves the resilience of the entire grid. However, it is not a common practice today; the inherent variability of these resources represents a challenge for a streamlined restoration process. This paper presents a prioritized method --starting with renewable generator units then lowering priority to conventional units --to plan the operational schedule of a power system during the restoration process. The goal is to achieve a well balanced system in the presence of significant renewable penetration. Validation and benchmarking experiments were performed on a customized version of the RTS-GMLC test system using six months out of year-long data, tested through hourly simulations. After evaluating the performance and computational costs, this method proved faster than common approaches: a MILP Unit Commitment algorithm, widely used today, and an "enable-and-try" algorithm. In summary, herein a more convenient method is provided to be utilized during timesensitive restoration, as an online operation-planning aid.

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Evaluation Of Year 2020 IEEE RTS Generation Reliability Indices

Cited by 11 publications

References 4 publications

Attack Power System State Estimation by Implicitly Learning the Underlying Models

Attack Power System State Estimation by Implicitly Learning the Underlying Models

A Guide for Improved Resource Adequacy Assessments in Evolving Power Systems: Institutional and Technical Dimensions

Rapid Method for Generation Prioritization during System Restoration with Renewable Resources

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