Stochastic load flow of active distribution network based on hybrid Monte Carlo method

Zece Zhang; Xiuli Wang; Yushen Hou; Zechen Wu

doi:10.1109/APPEEC.2016.7779619

Stochastic load flow of active distribution network based on hybrid Monte Carlo method

Zece Zhang
, Xiuli Wang
, Yushen Hou
, Zechen Wu

School of Electrical Engineering

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Active distribution network is an effective way to accommodate intermittent renewable energy and a realization of smart grid. There are distributed power resources in active distribution network and the load can be adjusted. The traditional deterministic model is no longer able to evaluate the risk accurately. To deal with the randomness and uncertainty in active distribution network, a hybrid Monte Carlo method (HMC) which has the high-dimension insensitivity of Monte Carlo method and the sampling uniformity of quasi Monte Carlo method is proposed and used to form system state matrix. Stochastic load flow calculation and risk assessment of node overvoltage are carried out in active distribution network based on HMC method. An example is taken to prove that the hybrid-Monte Carlo method can make the calculation results more close to the high precision convergence results under the same calculation times.

Original language	English
Title of host publication	IEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference
Publisher	IEEE Computer Society
Pages	870-874
Number of pages	5
ISBN (Electronic)	9781509054183
DOIs	https://doi.org/10.1109/APPEEC.2016.7779619
State	Published - 9 Dec 2016
Event	2016 IEEE PES Asia Pacific Power and Energy Engineering Conference, APPEEC 2016 - Xi'an, China Duration: 25 Oct 2016 → 28 Oct 2016

Publication series

Name	Asia-Pacific Power and Energy Engineering Conference, APPEEC
Volume	2016-December
ISSN (Print)	2157-4839
ISSN (Electronic)	2157-4847

Conference

Conference	2016 IEEE PES Asia Pacific Power and Energy Engineering Conference, APPEEC 2016
Country/Territory	China
City	Xi'an
Period	25/10/16 → 28/10/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

active distribution network
hybrid-Monte Carlo
stochastic load flow

Access to Document

10.1109/APPEEC.2016.7779619

Cite this

Zhang, Z., Wang, X., Hou, Y., & Wu, Z. (2016). Stochastic load flow of active distribution network based on hybrid Monte Carlo method. In IEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference (pp. 870-874). Article 7779619 (Asia-Pacific Power and Energy Engineering Conference, APPEEC; Vol. 2016-December). IEEE Computer Society. https://doi.org/10.1109/APPEEC.2016.7779619

@inproceedings{793fa9309ba94ef6885f7cc68776b3e3,

title = "Stochastic load flow of active distribution network based on hybrid Monte Carlo method",

abstract = "Active distribution network is an effective way to accommodate intermittent renewable energy and a realization of smart grid. There are distributed power resources in active distribution network and the load can be adjusted. The traditional deterministic model is no longer able to evaluate the risk accurately. To deal with the randomness and uncertainty in active distribution network, a hybrid Monte Carlo method (HMC) which has the high-dimension insensitivity of Monte Carlo method and the sampling uniformity of quasi Monte Carlo method is proposed and used to form system state matrix. Stochastic load flow calculation and risk assessment of node overvoltage are carried out in active distribution network based on HMC method. An example is taken to prove that the hybrid-Monte Carlo method can make the calculation results more close to the high precision convergence results under the same calculation times.",

keywords = "active distribution network, hybrid-Monte Carlo, stochastic load flow",

author = "Zece Zhang and Xiuli Wang and Yushen Hou and Zechen Wu",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference, APPEEC 2016 ; Conference date: 25-10-2016 Through 28-10-2016",

year = "2016",

month = dec,

day = "9",

doi = "10.1109/APPEEC.2016.7779619",

language = "英语",

series = "Asia-Pacific Power and Energy Engineering Conference, APPEEC",

publisher = "IEEE Computer Society",

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booktitle = "IEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference",

}

Zhang, Z, Wang, X, Hou, Y & Wu, Z 2016, Stochastic load flow of active distribution network based on hybrid Monte Carlo method. in IEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference., 7779619, Asia-Pacific Power and Energy Engineering Conference, APPEEC, vol. 2016-December, IEEE Computer Society, pp. 870-874, 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference, APPEEC 2016, Xi'an, China, 25/10/16. https://doi.org/10.1109/APPEEC.2016.7779619

Stochastic load flow of active distribution network based on hybrid Monte Carlo method. / Zhang, Zece; Wang, Xiuli; Hou, Yushen et al.
IEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference. IEEE Computer Society, 2016. p. 870-874 7779619 (Asia-Pacific Power and Energy Engineering Conference, APPEEC; Vol. 2016-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Stochastic load flow of active distribution network based on hybrid Monte Carlo method

AU - Zhang, Zece

AU - Wang, Xiuli

AU - Hou, Yushen

AU - Wu, Zechen

PY - 2016/12/9

Y1 - 2016/12/9

N2 - Active distribution network is an effective way to accommodate intermittent renewable energy and a realization of smart grid. There are distributed power resources in active distribution network and the load can be adjusted. The traditional deterministic model is no longer able to evaluate the risk accurately. To deal with the randomness and uncertainty in active distribution network, a hybrid Monte Carlo method (HMC) which has the high-dimension insensitivity of Monte Carlo method and the sampling uniformity of quasi Monte Carlo method is proposed and used to form system state matrix. Stochastic load flow calculation and risk assessment of node overvoltage are carried out in active distribution network based on HMC method. An example is taken to prove that the hybrid-Monte Carlo method can make the calculation results more close to the high precision convergence results under the same calculation times.

AB - Active distribution network is an effective way to accommodate intermittent renewable energy and a realization of smart grid. There are distributed power resources in active distribution network and the load can be adjusted. The traditional deterministic model is no longer able to evaluate the risk accurately. To deal with the randomness and uncertainty in active distribution network, a hybrid Monte Carlo method (HMC) which has the high-dimension insensitivity of Monte Carlo method and the sampling uniformity of quasi Monte Carlo method is proposed and used to form system state matrix. Stochastic load flow calculation and risk assessment of node overvoltage are carried out in active distribution network based on HMC method. An example is taken to prove that the hybrid-Monte Carlo method can make the calculation results more close to the high precision convergence results under the same calculation times.

KW - active distribution network

KW - hybrid-Monte Carlo

KW - stochastic load flow

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DO - 10.1109/APPEEC.2016.7779619

M3 - 会议稿件

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T3 - Asia-Pacific Power and Energy Engineering Conference, APPEEC

SP - 870

EP - 874

BT - IEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference

PB - IEEE Computer Society

T2 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference, APPEEC 2016

Y2 - 25 October 2016 through 28 October 2016

ER -

Stochastic load flow of active distribution network based on hybrid Monte Carlo method

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