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Probabilistic power flow calculation based on importance-hammersley sampling with eigen-decomposition

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Probabilistic power flow calculation based on importance-hammersley sampling with eigen-decomposition. / Li, Quan; Zhao, Nan.
In: International Journal of Electrical Power and Energy Systems, Vol. 130, 106947, 30.09.2021.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Li, Q & Zhao, N 2021, 'Probabilistic power flow calculation based on importance-hammersley sampling with eigen-decomposition', International Journal of Electrical Power and Energy Systems, vol. 130, 106947. https://doi.org/10.1016/j.ijepes.2021.106947

APA

Li, Q., & Zhao, N. (2021). Probabilistic power flow calculation based on importance-hammersley sampling with eigen-decomposition. International Journal of Electrical Power and Energy Systems, 130, Article 106947. https://doi.org/10.1016/j.ijepes.2021.106947

Vancouver

Li Q, Zhao N. Probabilistic power flow calculation based on importance-hammersley sampling with eigen-decomposition. International Journal of Electrical Power and Energy Systems. 2021 Sept 30;130:106947. Epub 2021 Mar 25. doi: 10.1016/j.ijepes.2021.106947

Author

Li, Quan ; Zhao, Nan. / Probabilistic power flow calculation based on importance-hammersley sampling with eigen-decomposition. In: International Journal of Electrical Power and Energy Systems. 2021 ; Vol. 130.

Bibtex

@article{2d9f43838a60485bb49faa696f6d26e9,
title = "Probabilistic power flow calculation based on importance-hammersley sampling with eigen-decomposition",
abstract = "This paper presents a novel probabilistic power flow calculation method for power systems with integrated wind farms, based on importance sampling and Hammersley sequence with eigen-decomposition. The method proposed in this paper adopts importance sampling to build probability density functions in a desired range to compress the sampling space and hence reduce the calculation burden. The Hammersley sequence, a kind of low discrepancy sequence, is used to obtain uniform samples for improving the sampling efficiency. In addition, since the traditional Cholesky method is unable to decompose the non-positive definite correlation matrix, this paper applies the eigen-decomposition to solve this problem for multiple correlated wind sources. Case studies are conducted on modified IEEE test systems, where the advantages of the proposed method are verified. According to the simulation results, the proposed method shows greater accuracy and efficiency, compared to the traditional methods.",
keywords = "Probabilistic power flow, Importance sampling, Hammersley sequence, Eigen-decomposition, Renewable energy",
author = "Quan Li and Nan Zhao",
year = "2021",
month = sep,
day = "30",
doi = "10.1016/j.ijepes.2021.106947",
language = "English",
volume = "130",
journal = "International Journal of Electrical Power and Energy Systems",
issn = "0142-0615",
publisher = "Elsevier Limited",

}

RIS

TY - JOUR

T1 - Probabilistic power flow calculation based on importance-hammersley sampling with eigen-decomposition

AU - Li, Quan

AU - Zhao, Nan

PY - 2021/9/30

Y1 - 2021/9/30

N2 - This paper presents a novel probabilistic power flow calculation method for power systems with integrated wind farms, based on importance sampling and Hammersley sequence with eigen-decomposition. The method proposed in this paper adopts importance sampling to build probability density functions in a desired range to compress the sampling space and hence reduce the calculation burden. The Hammersley sequence, a kind of low discrepancy sequence, is used to obtain uniform samples for improving the sampling efficiency. In addition, since the traditional Cholesky method is unable to decompose the non-positive definite correlation matrix, this paper applies the eigen-decomposition to solve this problem for multiple correlated wind sources. Case studies are conducted on modified IEEE test systems, where the advantages of the proposed method are verified. According to the simulation results, the proposed method shows greater accuracy and efficiency, compared to the traditional methods.

AB - This paper presents a novel probabilistic power flow calculation method for power systems with integrated wind farms, based on importance sampling and Hammersley sequence with eigen-decomposition. The method proposed in this paper adopts importance sampling to build probability density functions in a desired range to compress the sampling space and hence reduce the calculation burden. The Hammersley sequence, a kind of low discrepancy sequence, is used to obtain uniform samples for improving the sampling efficiency. In addition, since the traditional Cholesky method is unable to decompose the non-positive definite correlation matrix, this paper applies the eigen-decomposition to solve this problem for multiple correlated wind sources. Case studies are conducted on modified IEEE test systems, where the advantages of the proposed method are verified. According to the simulation results, the proposed method shows greater accuracy and efficiency, compared to the traditional methods.

KW - Probabilistic power flow

KW - Importance sampling

KW - Hammersley sequence

KW - Eigen-decomposition

KW - Renewable energy

U2 - 10.1016/j.ijepes.2021.106947

DO - 10.1016/j.ijepes.2021.106947

M3 - Journal article

VL - 130

JO - International Journal of Electrical Power and Energy Systems

JF - International Journal of Electrical Power and Energy Systems

SN - 0142-0615

M1 - 106947

ER -