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

Erika Pequeno dos Santos, Beatriz Silveira Buss, Mauro Augusto da Rosa and Diego Issicaba ()
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Erika Pequeno dos Santos: Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil
Beatriz Silveira Buss: Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil
Mauro Augusto da Rosa: Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil
Diego Issicaba: Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil

Energies, 2024, vol. 17, issue 23, 1-15

Abstract: 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.

Keywords: generation and transmission reliability; Monte Carlo simulation; tensor method; cross-entropy method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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