A new wind speed scenario generation method based on principal component and R-Vine Copula theories
The intermittent and uncertain properties of wind power have presented enormous obsta�cles to the planning and steady operation of power systems. In this context, as an effective technique to study wind power uncertainty, the development of an accurate wind speed scenario generation method is of g...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI
2022
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/7419/1/J14368_7be52f65ae7a5046e02af3faea504294.pdf http://eprints.uthm.edu.my/7419/ https://doi.org/10.3390/en15072698 |
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| Summary: | The intermittent and uncertain properties of wind power have presented enormous obsta�cles to the planning and steady operation of power systems. In this context, as an effective technique
to study wind power uncertainty, the development of an accurate wind speed scenario generation
method is of great significance for evaluating the impact of wind power in the power system. In the
case of several wind farms, accurate scenario generation involves precise acquisition of the correlation
between wind speeds and the greatest retention of statistical properties of wind speed data. Under
this goal, this research provided a new method for scenario development based on principle compo�nent (PC) and R-vine copula theories that incorporates the spatiotemporal correlation of wind speeds.
By integrating with PC theory, this strategy avoids the dimension disaster induced by employing
R-vine copula alone while taking benefit of its flexibility. The simulation results utilizing the historical
wind speeds of three adjacent wind farms as samples showed that the method described in this article
could effectively preserve the statistical properties of wind speed data. Eight evaluation indicators
covering three facets of the scenario generation method were used to compare the proposed method
holistically to two other commonly used scenario generation methods. The results indicated that this
method’s accuracy was increased further. Additionally, the validity and necessity of applying R-vine
copula in this model was demonstrated through comparisons to C-vine and D-vine copulas. |
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