Simulating wind power forecast error distributions for spatially aggregated wind power plants
Open accessAbstract
Dispersion and aggregation of wind power plants lower the uncertainty of wind power by reducing wind power forecasting errors. Using quantitative methods, this paper studies the dispersion's impact on the uncertainty of the aggregated wind power production. A method to simulate day‐ahead forecast error distributions at different dispersion and forecasting skill scenarios is presented. The proposed method models the uncertainty of wind power forecasting on an annual basis and at different levels of production. As a result, the uncertainty in the forecasting of spatially dispersed wind power plants is modelled using two continuous distributions: Laplace and beta distribution. The analysis shows that even the production level uncertainty can be modelled in various dispersion and forecasting skill scenarios. The model for aggregated forecast error distributions requires only four variables: capacity‐weighted distance of the total wind power plant fleet, mean of the site‐specific mean absolute errors (MAEs), number of aggregated wind power plants, and the mean variability of the elevations from the proximities of the aggregated wind power plants. The results are especially promising when the number of aggregated wind power plants exceeds five, the terrain complexity is low or moderate, and the aggregation region is large. This is demonstrated through a case study for Texas, United States.
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The aggregation of uncertain parameters leads to a reduction in variance of uncertainty.
“The aggregation of uncertain parameters leads to the reduction in variance of uncertainty is a well known concept [54].”
From Chance constrained day-ahead robust flexibility needs assessment for low voltage distribution network (2022) · cites this paper as [54]
Section 5.3 reports that aggregating more wind-power sites produces narrower distributions and reduces uncertainty. The paper supports this for spatially aggregated wind-power forecasts, but not the unrestricted claim that aggregating uncertain parameters generally reduces their variance. Page numbers were not available in our copy. (LLM, conf=0.84)
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Hashmi, M. U., Koirala, A., Ergun, H., & Van Hertem, D. (2022) · arXiv · doi:10.48550/arxiv.2207.10234 · Cited by 1 · see citing works
- Authors
- Miettinen, J., Holttinen, H., & Hodge, B.
- Affiliations
- VTT Technical Research Centre of Finland (FI)
- National Laboratory of the Rockies (US)
- Venue
- Wind Energy
- Publisher
- Wiley
- Published
- 2019-09-11
- Volume
- 23
- Issue
- 1
- Pages
- 45-62
- ISSN
- 1095-4244 (ISSN-L)
- 1099-1824
- OpenAlex
- W2972451568
- Cited by
- 23
- References
- 0
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 5 · 2023: 4 · 2022: 3 · 2021: 2 · 2020: 2
- FWCI
- 0.61
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Locations
- Wind Energy (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- Academy of Finland
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Physical Sciences › Engineering › Aerospace Engineering › Wind Energy Research and Development
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- wind power forecasting, forecast error distribution, spatial aggregation, wind farms, forecast uncertainty, day-ahead forecasting, Laplace distribution, beta distribution, forecasting skills, terrain complexity, mean absolute error
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