An Implicit switching model for distribution network reliability assessment

Yang Yang; Simon Tindemans; Goran Strbac

doi:10.1109/PSCC.2016.7540953

An Implicit switching model for distribution network reliability assessment

Yang Yang, Simon Tindemans, Goran Strbac

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

Abstract

Modern active distribution networks make use of intelligent switching actions to restore supply to end users after faults. This complicates the reliability analysis of such networks, as the number of possible switching actions grows exponentially with network size. This paper proposes an approximate reliability analysis method where switching actions are modelled implicitly. It can be used graphically as a model reduction method, and simulated using time-sequential or state sampling Monte Carlo methods. The method is illustrated on a simple distribution network, and reliability indices are reported both as averages and distributions. Large speedups result from the use of biased non-sequential Monte Carlo sampling - a method that is hard to combine with explicit switching models.

Original language	English
Title of host publication	PSCC 2016
Subtitle of host publication	19th Power Systems Computation Conference : Programme
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	7
ISBN (Electronic)	9788894105124
ISBN (Print)	9788894105124
DOIs	https://doi.org/10.1109/PSCC.2016.7540953
Publication status	Published - 2016
Externally published	Yes
Event	Power Systems Computation Conference (19th : 2016) - Genova, Italy Duration: 20 Jun 2016 → 24 Jun 2016

Conference

Conference	Power Systems Computation Conference (19th : 2016)
City	Genova, Italy
Period	20/06/16 → 24/06/16

Keywords

distribution networks
reliability analysis
network topology
Monte Carlo simulations

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10.1109/PSCC.2016.7540953

Cite this

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title = "An Implicit switching model for distribution network reliability assessment",

abstract = "Modern active distribution networks make use of intelligent switching actions to restore supply to end users after faults. This complicates the reliability analysis of such networks, as the number of possible switching actions grows exponentially with network size. This paper proposes an approximate reliability analysis method where switching actions are modelled implicitly. It can be used graphically as a model reduction method, and simulated using time-sequential or state sampling Monte Carlo methods. The method is illustrated on a simple distribution network, and reliability indices are reported both as averages and distributions. Large speedups result from the use of biased non-sequential Monte Carlo sampling - a method that is hard to combine with explicit switching models.",

keywords = "distribution networks, reliability analysis, network topology, Monte Carlo simulations",

author = "Yang Yang and Simon Tindemans and Goran Strbac",

year = "2016",

doi = "10.1109/PSCC.2016.7540953",

language = "English",

isbn = "9788894105124",

booktitle = "PSCC 2016",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "Power Systems Computation Conference (19th : 2016) ; Conference date: 20-06-2016 Through 24-06-2016",

}

Yang, Y, Tindemans, S & Strbac, G 2016, An Implicit switching model for distribution network reliability assessment. in PSCC 2016: 19th Power Systems Computation Conference : Programme. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, Power Systems Computation Conference (19th : 2016), Genova, Italy, 20/06/16. https://doi.org/10.1109/PSCC.2016.7540953

An Implicit switching model for distribution network reliability assessment. / Yang, Yang; Tindemans, Simon; Strbac, Goran.
PSCC 2016: 19th Power Systems Computation Conference : Programme. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2016.

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

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T1 - An Implicit switching model for distribution network reliability assessment

AU - Yang, Yang

AU - Tindemans, Simon

AU - Strbac, Goran

PY - 2016

Y1 - 2016

N2 - Modern active distribution networks make use of intelligent switching actions to restore supply to end users after faults. This complicates the reliability analysis of such networks, as the number of possible switching actions grows exponentially with network size. This paper proposes an approximate reliability analysis method where switching actions are modelled implicitly. It can be used graphically as a model reduction method, and simulated using time-sequential or state sampling Monte Carlo methods. The method is illustrated on a simple distribution network, and reliability indices are reported both as averages and distributions. Large speedups result from the use of biased non-sequential Monte Carlo sampling - a method that is hard to combine with explicit switching models.

AB - Modern active distribution networks make use of intelligent switching actions to restore supply to end users after faults. This complicates the reliability analysis of such networks, as the number of possible switching actions grows exponentially with network size. This paper proposes an approximate reliability analysis method where switching actions are modelled implicitly. It can be used graphically as a model reduction method, and simulated using time-sequential or state sampling Monte Carlo methods. The method is illustrated on a simple distribution network, and reliability indices are reported both as averages and distributions. Large speedups result from the use of biased non-sequential Monte Carlo sampling - a method that is hard to combine with explicit switching models.

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DO - 10.1109/PSCC.2016.7540953

M3 - Conference proceeding contribution

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PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - Power Systems Computation Conference (19th : 2016)

Y2 - 20 June 2016 through 24 June 2016

ER -

An Implicit switching model for distribution network reliability assessment

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