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A Comprehensive Review on Uncertainty and Risk Modeling Techniques and Their Applications in Power Systems
Open accessAbstract
The increasing integration of renewable energy sources (RESs) into power systems has introduced new complexities due to the inherent variability and uncertainty of these energy sources. In addition to the uncertainty in RES generation, the demand-side load of power systems is also subject to fluctuations, further complicating system operations. Addressing these challenges requires effective modeling and assessment techniques to quantify and mitigate the risks associated with system uncertainties. This paper evaluates the impact of various uncertainty modeling techniques on power system reliability with wind farm integration. Furthermore, this paper reviews the state of the art of the various uncertainty and risk modeling techniques in power systems. Through a detailed case study, the performance of these techniques in modeling uncertainties of wind speeds is analyzed. Based on the results, the integration of wind turbines improves the system’s overall reliability when there is a reduction in conventional power plants (CPPs)’ generation, which are dispatchable energy sources providing a stable and flexible supply. However, the generation of wind farms is associated with uncertainty. The results show Monte Carlo simulation combined with the K-Means method is consistently a more accurate uncertainty model for wind speeds, closely aligning with real-case scenarios, compared to other methods such as Markov Chain Monte Carlo (MCMC), robust optimization (RO), and information-gap decision theory (IGDT).
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“A recent review advocates for the use of advanced uncertainty analysis methods—such as Monte Carlo simulations and scenario analyses—that address both parameter and structural uncertainties42.”
From Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare (2025) · cites this paper as [42]
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Metadata
- Authors
- Afzali, P., Hosseini, S. A., & Peyghami, S.
- Affiliations
- Aalborg University (DK)
- Corresponding author
- Afzali, Peyman
- Peyghami, Saeed
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2024-12-23
- Volume
- 14
- Issue
- 24
- Pages
- 12042
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4405725328
- Cited by
- 29
- References
- 82
- Citations by year
- 2026: 15 · 2025: 14
- FWCI
- 7.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- VBN Forskningsportal (Aalborg Universitet) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Power System Reliability and Maintenance
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Keywords
- uncertainty modeling, risk modeling, power system reliability, wind power integration, wind speed uncertainty, renewable energy variability, Monte Carlo simulation, K-means clustering, Markov chain Monte Carlo, robust optimization, information gap decision theory
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