Comparing probabilistic load flow methods in dealing with uncertainties at TSO/DSO interface

J. Reinders; J. Morren; J.G. Slootweg

doi:10.1109/UPEC.2017.8232030

Comparing probabilistic load flow methods in dealing with uncertainties at TSO/DSO interface

J. Reinders, J. Morren, J.G. Slootweg

Electrical Energy Systems

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

2 Citations (Scopus)

1 Downloads (Pure)

Abstract

Increasing decentralized and renewable power production, which is mainly installed in distribution networks, makes planning of the transmission network more challenging. The application of probabilistic power flow methods provides additional information that can be used to predict future power flows in networks with a high share of decentralized and renewable generation. This paper compares numerical and analytical probabilistic power flow approaches focusing on the trade-off between computational time and information gain. It is shown that for the 6-bus Roy Billinton Test System, using normally distributed random variables, the analytical method is a suitable alternative to the numerical method. Reducing computational effort while retaining considerable accuracy. This makes analytical probabilistic power flow an interesting method for studying networks with a high share of decentralized and renewable generation.

Original language	English
Title of host publication	52nd International Universities Power Engineering Conference (UPEC)
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Number of pages	5
ISBN (Electronic)	978-1-5386-2344-2
ISBN (Print)	978-1-5386-2345-9
DOIs	https://doi.org/10.1109/UPEC.2017.8232030
Publication status	Published - 19 Dec 2017
Event	52nd International Universities' Power Engineering Conference (UPEC 2017) - Heraklion, Greece Duration: 28 Aug 2017 → 31 Aug 2017 Conference number: 52 http://www.upec2017.com/ http://www.upec2017.com/

Conference

Conference	52nd International Universities' Power Engineering Conference (UPEC 2017)
Abbreviated title	UPEC 2017
Country/Territory	Greece
City	Heraklion
Period	28/08/17 → 31/08/17
Internet address	http://www.upec2017.com/ http://www.upec2017.com/

Keywords

Analytical probabilistic load flow
TSO/DSO interface
Transmission network
Uncertainties

UN SDGs

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

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10.1109/UPEC.2017.8232030

Cite this

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title = "Comparing probabilistic load flow methods in dealing with uncertainties at TSO/DSO interface",

abstract = "Increasing decentralized and renewable power production, which is mainly installed in distribution networks, makes planning of the transmission network more challenging. The application of probabilistic power flow methods provides additional information that can be used to predict future power flows in networks with a high share of decentralized and renewable generation. This paper compares numerical and analytical probabilistic power flow approaches focusing on the trade-off between computational time and information gain. It is shown that for the 6-bus Roy Billinton Test System, using normally distributed random variables, the analytical method is a suitable alternative to the numerical method. Reducing computational effort while retaining considerable accuracy. This makes analytical probabilistic power flow an interesting method for studying networks with a high share of decentralized and renewable generation.",

keywords = "Analytical probabilistic load flow, TSO/DSO interface, Transmission network, Uncertainties",

author = "J. Reinders and J. Morren and J.G. Slootweg",

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booktitle = "52nd International Universities Power Engineering Conference (UPEC)",

publisher = "Institute of Electrical and Electronics Engineers",

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note = "52nd International Universities' Power Engineering Conference (UPEC 2017), UPEC 2017 ; Conference date: 28-08-2017 Through 31-08-2017",

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}

Reinders, J, Morren, J & Slootweg, JG 2017, Comparing probabilistic load flow methods in dealing with uncertainties at TSO/DSO interface. in 52nd International Universities Power Engineering Conference (UPEC) ., 91, Institute of Electrical and Electronics Engineers, Piscataway, 52nd International Universities' Power Engineering Conference (UPEC 2017), Heraklion, Greece, 28/08/17. https://doi.org/10.1109/UPEC.2017.8232030

Comparing probabilistic load flow methods in dealing with uncertainties at TSO/DSO interface. / Reinders, J.; Morren, J.; Slootweg, J.G.
52nd International Universities Power Engineering Conference (UPEC) . Piscataway: Institute of Electrical and Electronics Engineers, 2017. 91.

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

TY - GEN

T1 - Comparing probabilistic load flow methods in dealing with uncertainties at TSO/DSO interface

AU - Reinders, J.

AU - Morren, J.

AU - Slootweg, J.G.

N1 - Conference code: 52

PY - 2017/12/19

Y1 - 2017/12/19

N2 - Increasing decentralized and renewable power production, which is mainly installed in distribution networks, makes planning of the transmission network more challenging. The application of probabilistic power flow methods provides additional information that can be used to predict future power flows in networks with a high share of decentralized and renewable generation. This paper compares numerical and analytical probabilistic power flow approaches focusing on the trade-off between computational time and information gain. It is shown that for the 6-bus Roy Billinton Test System, using normally distributed random variables, the analytical method is a suitable alternative to the numerical method. Reducing computational effort while retaining considerable accuracy. This makes analytical probabilistic power flow an interesting method for studying networks with a high share of decentralized and renewable generation.

AB - Increasing decentralized and renewable power production, which is mainly installed in distribution networks, makes planning of the transmission network more challenging. The application of probabilistic power flow methods provides additional information that can be used to predict future power flows in networks with a high share of decentralized and renewable generation. This paper compares numerical and analytical probabilistic power flow approaches focusing on the trade-off between computational time and information gain. It is shown that for the 6-bus Roy Billinton Test System, using normally distributed random variables, the analytical method is a suitable alternative to the numerical method. Reducing computational effort while retaining considerable accuracy. This makes analytical probabilistic power flow an interesting method for studying networks with a high share of decentralized and renewable generation.

KW - Analytical probabilistic load flow

KW - TSO/DSO interface

KW - Transmission network

KW - Uncertainties

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M3 - Conference contribution

SN - 978-1-5386-2345-9

BT - 52nd International Universities Power Engineering Conference (UPEC)

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 52nd International Universities' Power Engineering Conference (UPEC 2017)

Y2 - 28 August 2017 through 31 August 2017

ER -

Comparing probabilistic load flow methods in dealing with uncertainties at TSO/DSO interface

Abstract

Conference

Keywords

UN SDGs

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