A computational framework for risk-based power system operations under uncertainty. Part II: Case studies
(2015) In Electric Power Systems Research 119. p.66-75- Abstract
- With larger penetrations of wind power, the uncertainty increases in power systems operations. The wind power forecast errors must be accounted for by adapting existing operating tools or designing new ones. A switch from the deterministic framework used today to a probabilistic one has been advocated. This two-part paper presents a framework for risk-based operations of power systems. This framework builds on the operating risk defined as the probability of the system to be outside the stable operation domain, given probabilistic forecasts for the uncertainty, load and wind power generation levels. This operating risk can be seen as a probabilistic formulation of the N − 1 criterion. In Part I, the definition of the operating risk and a... (More)
- With larger penetrations of wind power, the uncertainty increases in power systems operations. The wind power forecast errors must be accounted for by adapting existing operating tools or designing new ones. A switch from the deterministic framework used today to a probabilistic one has been advocated. This two-part paper presents a framework for risk-based operations of power systems. This framework builds on the operating risk defined as the probability of the system to be outside the stable operation domain, given probabilistic forecasts for the uncertainty, load and wind power generation levels. This operating risk can be seen as a probabilistic formulation of the N − 1 criterion. In Part I, the definition of the operating risk and a method to estimate it were presented. A new way of modeling the uncertain wind power injections was presented. In Part II of the paper, the method's accuracy and computational requirements are assessed for both models. It is shown that the new model for wind power introduced in Part I significantly decreases the computation time of the method, which allows for the use of later and more accurate forecasts. The method developed in this paper is able to tackle the two challenges associated with risk-based real-time operations: accurately estimating very low operating risks and doing so in a very limited amount of time. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8518034
- author
- Hamon, Camille ; Perninge, Magnus LU and Söder, Lennart
- organization
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Wind power, Stochastic optimal power flow, Risk-limiting dispatch, Chance-constrained optimal power flow, Edgeworth expansions, Risk-based methods ☆
- in
- Electric Power Systems Research
- volume
- 119
- pages
- 66 - 75
- publisher
- Elsevier
- external identifiers
-
- scopus:84907487450
- ISSN
- 1873-2046
- DOI
- 10.1016/j.epsr.2014.09.007
- language
- English
- LU publication?
- yes
- id
- 3bde1663-f59f-4b49-861c-d4881f8e8397 (old id 8518034)
- date added to LUP
- 2016-04-04 13:07:42
- date last changed
- 2022-01-29 23:46:41
@article{3bde1663-f59f-4b49-861c-d4881f8e8397,
abstract = {{With larger penetrations of wind power, the uncertainty increases in power systems operations. The wind power forecast errors must be accounted for by adapting existing operating tools or designing new ones. A switch from the deterministic framework used today to a probabilistic one has been advocated. This two-part paper presents a framework for risk-based operations of power systems. This framework builds on the operating risk defined as the probability of the system to be outside the stable operation domain, given probabilistic forecasts for the uncertainty, load and wind power generation levels. This operating risk can be seen as a probabilistic formulation of the N − 1 criterion. In Part I, the definition of the operating risk and a method to estimate it were presented. A new way of modeling the uncertain wind power injections was presented. In Part II of the paper, the method's accuracy and computational requirements are assessed for both models. It is shown that the new model for wind power introduced in Part I significantly decreases the computation time of the method, which allows for the use of later and more accurate forecasts. The method developed in this paper is able to tackle the two challenges associated with risk-based real-time operations: accurately estimating very low operating risks and doing so in a very limited amount of time.}},
author = {{Hamon, Camille and Perninge, Magnus and Söder, Lennart}},
issn = {{1873-2046}},
keywords = {{Wind power; Stochastic optimal power flow; Risk-limiting dispatch; Chance-constrained optimal power flow; Edgeworth expansions; Risk-based methods ☆}},
language = {{eng}},
pages = {{66--75}},
publisher = {{Elsevier}},
series = {{Electric Power Systems Research}},
title = {{A computational framework for risk-based power system operations under uncertainty. Part II: Case studies}},
url = {{http://dx.doi.org/10.1016/j.epsr.2014.09.007}},
doi = {{10.1016/j.epsr.2014.09.007}},
volume = {{119}},
year = {{2015}},
}