A Review of Uncertainty Modelling Techniques for Probabilistic Stability Analysis of Renewable-Rich Power Systems

Ali M. Hakami, Kazi N. Hasan, Mohammed Alzubaidi () and Manoj Datta ()
Additional contact information
Ali M. Hakami: School of Engineering, RMIT University, Melbourne 3001, Australia
Kazi N. Hasan: School of Engineering, RMIT University, Melbourne 3001, Australia
Mohammed Alzubaidi: School of Engineering, RMIT University, Melbourne 3001, Australia
Manoj Datta: School of Engineering, RMIT University, Melbourne 3001, Australia

Energies, 2022, vol. 16, issue 1, 1-26

Abstract: In pursuit of identifying the most accurate and efficient uncertainty modelling (UM) techniques, this paper provides an extensive review and classification of the available UM techniques for probabilistic power system stability analysis. The increased penetration of system uncertainties related to renewable energy sources, new types of loads and their fluctuations, and deregulation of the electricity markets necessitates probabilistic power system analysis. The abovementioned factors significantly affect the power system stability, which requires computationally intensive simulation, including frequency, voltage, transient, and small disturbance stability. Altogether 40 UM techniques are collated with their characteristics, advantages, disadvantages, and application areas, particularly highlighting their accuracy and efficiency (as both are crucial for power system stability applications). This review recommends the most accurate and efficient UM techniques that could be used for probabilistic stability analysis of renewable-rich power systems.

Keywords: power system stability; renewable energy; uncertainty modelling techniques (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/1/112/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/1/112/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2022:i:1:p:112-:d:1011319

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().