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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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 languageEnglish
Title of host publication52nd International Universities Power Engineering Conference (UPEC)
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)978-1-5386-2344-2
ISBN (Print)978-1-5386-2345-9
DOIs
Publication statusPublished - 2017
Event52nd International Universities' Power Engineering Conference (UPEC 2017) - Heraklion, Greece
Duration: 28 Aug 201731 Aug 2017
Conference number: 52
http://www.upec2017.com/
http://www.upec2017.com/

Conference

Conference52nd International Universities' Power Engineering Conference (UPEC 2017)
Abbreviated titleUPEC 2017
CountryGreece
CityHeraklion
Period28/08/1731/08/17
Internet address

Fingerprint

Electric power transmission networks
Electric power distribution
Random variables
Numerical methods
Planning
Uncertainty

Cite this

Reinders, J., Morren, J., & Slootweg, J. G. (2017). Comparing probabilistic load flow methods in dealing with uncertainties at TSO/DSO interface. In 52nd International Universities Power Engineering Conference (UPEC) [91] Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/UPEC.2017.8232030
Reinders, J. ; Morren, J. ; Slootweg, J.G. / Comparing probabilistic load flow methods in dealing with uncertainties at TSO/DSO interface. 52nd International Universities Power Engineering Conference (UPEC) . Piscataway : Institute of Electrical and Electronics Engineers, 2017.
@inproceedings{c1aeb4694c81482aa7958e5aee2994ab,
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.",
author = "J. Reinders and J. Morren and J.G. Slootweg",
year = "2017",
doi = "10.1109/UPEC.2017.8232030",
language = "English",
isbn = "978-1-5386-2345-9",
booktitle = "52nd International Universities Power Engineering Conference (UPEC)",
publisher = "Institute of Electrical and Electronics Engineers",
address = "United States",

}

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 proceedingConference contributionAcademicpeer-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.

PY - 2017

Y1 - 2017

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.

U2 - 10.1109/UPEC.2017.8232030

DO - 10.1109/UPEC.2017.8232030

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

ER -

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