A GPU-Based Model for Composite Power System Reliability Evaluation and Sensitivity Analysis

Olowolaju, Joshua; Thapa, Jitendra; Hossain, Rakib; Benidris, Mohammed; Livani, Hanif

doi:10.1109/ias54024.2023.10406815

2023 IEEE Industry Applications Society Annual Meeting (IAS) 2023

DOI: 10.1109/ias54024.2023.10406815

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A GPU-Based Model for Composite Power System Reliability Evaluation and Sensitivity Analysis

Joshua Olowolaju,

Jitendra Thapa,

Rakib Hossain

et al.

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2024

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Cited by 2 publications

References 15 publications

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A Fast Reliability and Sensitivity Analysis Approach for Composite Generation and Transmission Systems

Olowolaju,

Thapa,

Hossain

et al. 2024

IEEE Trans. on Ind. Applicat.

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A Fast Reliability and Sensitivity Analysis Approach for Composite Generation and Transmission Systems

Olowolaju,

Thapa,

Hossain

et al. 2024

IEEE Trans. on Ind. Applicat.

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Tensor-Based Predictor–Corrector Algorithm for Power Generation and Transmission Reliability Assessment with Sequential Monte Carlo Simulation

Pequeno dos Santos,

Buss,

Rosa

et al. 2024

Energies

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The reliability assesment of large power systems, particularly when considering both generation and transmission facilities, is a computationally demanding and complex problem. The sequential Monte Carlo simulation is arguably the most versatile approach for tackling this problem. However, assessing sampled states in the sequential Monte Carlo simulation is time-intensive, rendering its use less appealing, particularly if nonlinear network representation must be deployed. In this context, this paper introduces a tensor-based predictor–corrector approach to reduce the burden of state evaluations in power generation and transmission reliability assessments. The approach allows for searching for sequences of operation points which can be assigned as success states within the sequential Monte Carlo simulation. If required, failure states are evaluated using a cross-entropy optimization algorithm designed to minimize load curtailments taking into account discrete variables. Numerical results emphasize the applicability of the developed algorithms using a small test system and the IEEE-RTS79 test system.

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A GPU-Based Model for Composite Power System Reliability Evaluation and Sensitivity Analysis

Cited by 2 publications

References 15 publications

A Fast Reliability and Sensitivity Analysis Approach for Composite Generation and Transmission Systems

A Fast Reliability and Sensitivity Analysis Approach for Composite Generation and Transmission Systems

Tensor-Based Predictor–Corrector Algorithm for Power Generation and Transmission Reliability Assessment with Sequential Monte Carlo Simulation

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