The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
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).
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems
Helton, J. C., & Davis, F. J. (2003) · Reliability Engineering & System Safety · doi:10.1016/s0951-8320(03)00058-9 · Cited by 2,719
Zadeh, L. A. (2011) · Information Sciences · doi:10.1016/j.ins.2011.02.022 · Cited by 1,375
Combination of Evidence in Dempster-Shafer Theory
Sentz, K., & Ferson, S. A. (2002) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.2172/800792 · Cited by 1,129
Ben-Tal, A., Ghaoui, L. E., & Nemirovski, A. (2021) · Princeton Series in Applied Mathematics · doi:10.1515/9781400831050 · Cited by 1,046
Reserve Requirements for Wind Power Integration: A Scenario-Based Stochastic Programming Framework
Papavasiliou, A., Oren, S. S., & O’Neill, R. P. (2011) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2011.2121095 · Cited by 583
A comprehensive review on uncertainty modeling techniques in power system studies
Aien, M., Hajebrahimi, A., & Fotuhi‐Firuzabad, M. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.12.070 · Cited by 433
Liu, Z., Wen, F., & Ledwich, G. F. (2011) · IEEE Transactions on Power Delivery · doi:10.1109/tpwrd.2011.2165972 · Cited by 417
Moghaddam, M. J. H., Kalam, A., Nowdeh, S. A., Ahmadi, A., Babanezhad, M., & Saha, S. (2018) · Renewable Energy · doi:10.1016/j.renene.2018.09.078 · Cited by 328
Learning about risk: Machine learning for risk assessment
Paltrinieri, N., Comfort, L., & Reniers, G. (2019) · Safety Science · doi:10.1016/j.ssci.2019.06.001 · Cited by 254
Measurement Placement in Distribution System State Estimation
Singh, R., Pal, B. C., & Vinter, R. (2009) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2009.2016457 · Cited by 238
Research works printed but not linked: 3
- Billinton, R., and Ronald, N.A. (2013). Reliability Evaluation of Power Systems, Springer Science & Business Media.
- Gupta, A.K., and Varga, T. (2013). An Introduction to Actuarial Mathematics, Springer Science & Business Media.
- (2024, December 03). IEEE 24-Bus RTS. Available online: https://matpower.org/docs/ref/matpower5.0/case24_ieee_rts.html.
Cited by
Sorted by citations
Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare
El Arab, R. A., & Al Moosa, O. A. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01722-y · Cited by 149
“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 uncertainties 42 .”
Carozzani, A., & D’Alpaos, C. (2025) · Energies · doi:10.3390/en18205535 · Cited by 19
Assessment of Transmission Reliability Margin: Existing Methods and Challenges and Future Prospects
Edeh, U. E., Lie, T. T., & Mahmud, M. A. (2025) · Energies · doi:10.3390/en18092267 · Cited by 16
Shabanian‐Poodeh, M., Hooshmand, R., Shafie‐khah, M., & Siano, P. (2025) · IEEE Access · doi:10.1109/access.2025.3556233 · Cited by 16
Păsculescu, D., Riurean, S. M., Obretenova, M. I., Lazăr, T., Tătar, A. M., & Fîţă, N. D. (2025) · Energies · doi:10.3390/en19010148 · Cited by 12
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Comprehensive Review on Uncertainty and Risk Modeling Techniques and Their Applications in Power Systems. https://app.cochat.ai/papers/10.3390/app142412042
- The paper
- Afzali, P., Hosseini, S. A., & Peyghami, S. (2024). A Comprehensive Review on Uncertainty and Risk Modeling Techniques and Their Applications in Power Systems. Applied Sciences. https://doi.org/10.3390/app142412042
