Assessment of post-contingency congestion risk of wind power with asset dynamic ratings

Research output: Contribution to journalArticlepeer-review

Standard

Assessment of post-contingency congestion risk of wind power with asset dynamic ratings. / Banerjee, Binayak; Jayaweera, Dilan; Islam, Syed.

In: International Journal of Electrical Power and Energy Systems, Vol. 69, 07.2015, p. 295-303.

Research output: Contribution to journalArticlepeer-review

Harvard

APA

Vancouver

Author

Bibtex

@article{cc9a6773baad486c9c86ec4b31a8c7d3,
title = "Assessment of post-contingency congestion risk of wind power with asset dynamic ratings",
abstract = "Large scale integration of wind power can be deterred by congestion following an outage that results in constrained network capacity. Post outage congestion can be mitigated by the application of event control strategies; however they may not always benefit large wind farms. This paper investigates this problem in detail and proposes an advanced mathematical framework to model network congestion as functions of stochastic limits of network assets to capture post contingency risk of network congestion resulting through the constrained network capacity that limits high penetration of wind. The benefit of this approach is that it can limit the generation to be curtailed or re-dispatched by dynamically enhancing the network latent capacity in the event of outages or as per the need. The uniqueness of the proposed mathematical model is that it converts conventional thermal constraints to dynamic constraints by using a discretized stochastic penalty function with quadratic approximation of constraint relaxation penalty. The case study results with large and small network models suggest that the following an outage, wind utilization under dynamic line rating can be increased considerably if the wind power producers maintain around a 15% margin of operation.",
keywords = "Dynamic asset ratings, Latent network capacity, Locational marginal price, Stochastic optimization, Wind power generation",
author = "Binayak Banerjee and Dilan Jayaweera and Syed Islam",
year = "2015",
month = jul,
doi = "10.1016/j.ijepes.2014.12.088",
language = "English",
volume = "69",
pages = "295--303",
journal = "International Journal of Electrical Power and Energy Systems",
issn = "0142-0615",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Assessment of post-contingency congestion risk of wind power with asset dynamic ratings

AU - Banerjee, Binayak

AU - Jayaweera, Dilan

AU - Islam, Syed

PY - 2015/7

Y1 - 2015/7

N2 - Large scale integration of wind power can be deterred by congestion following an outage that results in constrained network capacity. Post outage congestion can be mitigated by the application of event control strategies; however they may not always benefit large wind farms. This paper investigates this problem in detail and proposes an advanced mathematical framework to model network congestion as functions of stochastic limits of network assets to capture post contingency risk of network congestion resulting through the constrained network capacity that limits high penetration of wind. The benefit of this approach is that it can limit the generation to be curtailed or re-dispatched by dynamically enhancing the network latent capacity in the event of outages or as per the need. The uniqueness of the proposed mathematical model is that it converts conventional thermal constraints to dynamic constraints by using a discretized stochastic penalty function with quadratic approximation of constraint relaxation penalty. The case study results with large and small network models suggest that the following an outage, wind utilization under dynamic line rating can be increased considerably if the wind power producers maintain around a 15% margin of operation.

AB - Large scale integration of wind power can be deterred by congestion following an outage that results in constrained network capacity. Post outage congestion can be mitigated by the application of event control strategies; however they may not always benefit large wind farms. This paper investigates this problem in detail and proposes an advanced mathematical framework to model network congestion as functions of stochastic limits of network assets to capture post contingency risk of network congestion resulting through the constrained network capacity that limits high penetration of wind. The benefit of this approach is that it can limit the generation to be curtailed or re-dispatched by dynamically enhancing the network latent capacity in the event of outages or as per the need. The uniqueness of the proposed mathematical model is that it converts conventional thermal constraints to dynamic constraints by using a discretized stochastic penalty function with quadratic approximation of constraint relaxation penalty. The case study results with large and small network models suggest that the following an outage, wind utilization under dynamic line rating can be increased considerably if the wind power producers maintain around a 15% margin of operation.

KW - Dynamic asset ratings

KW - Latent network capacity

KW - Locational marginal price

KW - Stochastic optimization

KW - Wind power generation

UR - http://www.scopus.com/inward/record.url?scp=84922489873&partnerID=8YFLogxK

U2 - 10.1016/j.ijepes.2014.12.088

DO - 10.1016/j.ijepes.2014.12.088

M3 - Article

AN - SCOPUS:84922489873

VL - 69

SP - 295

EP - 303

JO - International Journal of Electrical Power and Energy Systems

JF - International Journal of Electrical Power and Energy Systems

SN - 0142-0615

ER -