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Estimation of electricity cost of wind energy using Monte Carlo simulations based on nonparametric and parametric probability density functions
Open accessAbstract
It is conventional in wind energy assessment projects to calculate the expected value of the annual energy production (AEP). However, ignoring the effect of wind speed distribution on the distribution of the annual energy production may cause wrong predictions in the cost analysis. For a better estimation of the payback period, it is essential to accurately determine the confidence levels of the electricity cost around the expected value. Wind speed distributions are commonly represented by the Weibull model. Improved functions or nonparametric functions are preferred in the case of multimodal wind speed distributions. As nonparametric probability density function, the optimum spline based functions are implemented and compared to Weibull, Weibull & Weibull for two different wind sites. The results show that the spline based probability density functions produce minimum fitting error for the analyzed cases. Once the wind speed distributions are characterized, random wind speeds are generated to calculate the AEP distributions by Monte Carlo simulations. The cost analysis is then carried out based on the AEP distributions which includes the determination of the confidence levels. It is observed that the confidence levels of the electricity costs which are not falling close to the expected cost region are much greater.
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MCS can be invaluable to risk managers and helpful for estimating project durations and costs.
“This technique can be invaluable to risk managers and helpful for estimating project durations and costs (Ali Elfarra and Kaya, 2021).”
From Beyond probability-impact matrices in project risk management: A quantitative methodology for risk prioritisation (2024) · cites this paper as Ali Elfarra and Kaya, 2021
The Introduction (page unavailable) says Monte Carlo simulations help estimate confidence levels for electricity costs in wind-energy projects, supporting the cost-estimation part of the claim. It also discusses minimizing investment risk, but does not address risk managers or estimating project durations. The pay-off period in §4.4 is not a project-duration estimate. (LLM, conf=0.86)
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Research works printed but not linked: 25
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Cited by
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Acebes, F., González-Varona, J. M., López-Paredes, A., & Pajares, J. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03180-5 · Cited by 74
Metadata
- Authors
- Ali Elfarra, M., & Kaya, M.
- Affiliations
- Ankara Yıldırım Beyazıt University (TR)
- ORCID
- Kaya, Mustafa
- Venue
- Alexandria Engineering Journal
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 60
- Issue
- 4
- Pages
- 3631-3640
- ISSN
- 1110-0168 (ISSN-L)
- OpenAlex
- W3134673939
- Cited by
- 25
- References
- 0
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 8 · 2023: 4 · 2022: 1 · 2021: 6
- FWCI
- 0.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Alexandria Engineering Journal (publishedVersion, cc-by)
- AYBU AVESIS (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Wind Energy Research and Development
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Power System Reliability and Maintenance
- Keywords
- annual energy production, wind speed distribution, Weibull distribution, Monte Carlo simulation, confidence level, payback period
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