Statistical probability based transmission congestion cost increasing tendency analysis

J. Li, L. Zhou, F. Li

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The integration of renewable generation into power system brings challenges and threats for the power system operation and planning. Transmission congestion is one of the most intricate problems. Congestion cost links the short-term system operation and long-term network planning. But few literature study the congestion cost over long term. The main innovation of this paper is to employ statistical probability of wind and load from real data to calculate congestion cost over a long period. The high accuracy and fast speed of the proposed calculation method are verified through a case study. The main contribution of this paper is the introduction of a typical congestion cost increasing curve after exploring the impacts of wind generation capacity, transmission capacity and peak load on congestion cost. The simulation results show that, with increasing wind generation capacity connected, the congestion cost increasing curve is divided into three periods - constant, exponential increasing and saturation. The dividing points between different periods are determined by transmission capacity and wind/load characteristics. Furthermore, the constant values in constant and saturation periods are determined by different electricity prices and the transmission capacity shortage. The research findings contribute to congestion cost forecasting and renewable generation deployment.
Original languageEnglish
Title of host publicationPower Engineering Conference (UPEC), 2013 48th International Universities
PublisherIEEE
Pages1-6
ISBN (Print)9781479932542
DOIs
Publication statusPublished - 2013
Event2013 48th International Universities' Power Engineering Conference, UPEC 2013 - Dublin , Ireland
Duration: 2 Sep 20135 Sep 2013

Conference

Conference2013 48th International Universities' Power Engineering Conference, UPEC 2013
CountryIreland
CityDublin
Period2/09/135/09/13

Fingerprint

Costs
Planning
Power generation
Electricity
Innovation

Cite this

Li, J., Zhou, L., & Li, F. (2013). Statistical probability based transmission congestion cost increasing tendency analysis. In Power Engineering Conference (UPEC), 2013 48th International Universities (pp. 1-6). [6714911] IEEE. https://doi.org/10.1109/UPEC.2013.6714911

Statistical probability based transmission congestion cost increasing tendency analysis. / Li, J.; Zhou, L.; Li, F.

Power Engineering Conference (UPEC), 2013 48th International Universities. IEEE, 2013. p. 1-6 6714911.

Research output: Chapter in Book/Report/Conference proceedingChapter

Li, J, Zhou, L & Li, F 2013, Statistical probability based transmission congestion cost increasing tendency analysis. in Power Engineering Conference (UPEC), 2013 48th International Universities., 6714911, IEEE, pp. 1-6, 2013 48th International Universities' Power Engineering Conference, UPEC 2013, Dublin , Ireland, 2/09/13. https://doi.org/10.1109/UPEC.2013.6714911
Li J, Zhou L, Li F. Statistical probability based transmission congestion cost increasing tendency analysis. In Power Engineering Conference (UPEC), 2013 48th International Universities. IEEE. 2013. p. 1-6. 6714911 https://doi.org/10.1109/UPEC.2013.6714911
Li, J. ; Zhou, L. ; Li, F. / Statistical probability based transmission congestion cost increasing tendency analysis. Power Engineering Conference (UPEC), 2013 48th International Universities. IEEE, 2013. pp. 1-6
@inbook{94617b3c8d4b4ca5b9679d6e325be59f,
title = "Statistical probability based transmission congestion cost increasing tendency analysis",
abstract = "The integration of renewable generation into power system brings challenges and threats for the power system operation and planning. Transmission congestion is one of the most intricate problems. Congestion cost links the short-term system operation and long-term network planning. But few literature study the congestion cost over long term. The main innovation of this paper is to employ statistical probability of wind and load from real data to calculate congestion cost over a long period. The high accuracy and fast speed of the proposed calculation method are verified through a case study. The main contribution of this paper is the introduction of a typical congestion cost increasing curve after exploring the impacts of wind generation capacity, transmission capacity and peak load on congestion cost. The simulation results show that, with increasing wind generation capacity connected, the congestion cost increasing curve is divided into three periods - constant, exponential increasing and saturation. The dividing points between different periods are determined by transmission capacity and wind/load characteristics. Furthermore, the constant values in constant and saturation periods are determined by different electricity prices and the transmission capacity shortage. The research findings contribute to congestion cost forecasting and renewable generation deployment.",
author = "J. Li and L. Zhou and F. Li",
year = "2013",
doi = "10.1109/UPEC.2013.6714911",
language = "English",
isbn = "9781479932542",
pages = "1--6",
booktitle = "Power Engineering Conference (UPEC), 2013 48th International Universities",
publisher = "IEEE",
address = "USA United States",

}

TY - CHAP

T1 - Statistical probability based transmission congestion cost increasing tendency analysis

AU - Li, J.

AU - Zhou, L.

AU - Li, F.

PY - 2013

Y1 - 2013

N2 - The integration of renewable generation into power system brings challenges and threats for the power system operation and planning. Transmission congestion is one of the most intricate problems. Congestion cost links the short-term system operation and long-term network planning. But few literature study the congestion cost over long term. The main innovation of this paper is to employ statistical probability of wind and load from real data to calculate congestion cost over a long period. The high accuracy and fast speed of the proposed calculation method are verified through a case study. The main contribution of this paper is the introduction of a typical congestion cost increasing curve after exploring the impacts of wind generation capacity, transmission capacity and peak load on congestion cost. The simulation results show that, with increasing wind generation capacity connected, the congestion cost increasing curve is divided into three periods - constant, exponential increasing and saturation. The dividing points between different periods are determined by transmission capacity and wind/load characteristics. Furthermore, the constant values in constant and saturation periods are determined by different electricity prices and the transmission capacity shortage. The research findings contribute to congestion cost forecasting and renewable generation deployment.

AB - The integration of renewable generation into power system brings challenges and threats for the power system operation and planning. Transmission congestion is one of the most intricate problems. Congestion cost links the short-term system operation and long-term network planning. But few literature study the congestion cost over long term. The main innovation of this paper is to employ statistical probability of wind and load from real data to calculate congestion cost over a long period. The high accuracy and fast speed of the proposed calculation method are verified through a case study. The main contribution of this paper is the introduction of a typical congestion cost increasing curve after exploring the impacts of wind generation capacity, transmission capacity and peak load on congestion cost. The simulation results show that, with increasing wind generation capacity connected, the congestion cost increasing curve is divided into three periods - constant, exponential increasing and saturation. The dividing points between different periods are determined by transmission capacity and wind/load characteristics. Furthermore, the constant values in constant and saturation periods are determined by different electricity prices and the transmission capacity shortage. The research findings contribute to congestion cost forecasting and renewable generation deployment.

UR - http://www.scopus.com/inward/record.url?scp=84894150067&partnerID=8YFLogxK

UR - http://dx.doi.org/10.1109/UPEC.2013.6714911

UR - http://dit.ie/upec2013/

U2 - 10.1109/UPEC.2013.6714911

DO - 10.1109/UPEC.2013.6714911

M3 - Chapter

SN - 9781479932542

SP - 1

EP - 6

BT - Power Engineering Conference (UPEC), 2013 48th International Universities

PB - IEEE

ER -