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A computational framework for risk-based power system operations under uncertainty. Part I: Theory

Hamon, Camille; Perninge, Magnus LU and Söder, Lennart (2015) In Electric Power Systems Research 119. p.45-53
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) and outage rates of chosen elements of the system (generators and transmission lines). This operating risk can be seen as a probabilistic... (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) and outage rates of chosen elements of the system (generators and transmission lines). This operating risk can be seen as a probabilistic formulation of the N − 1 criterion. The stable operation domain is defined by voltage-stability limits, small-signal stability limits, thermal stability limits and other operating limits. In Part I of the paper, a previous method for estimating the operating risk is extended by using a new model for the joint distribution of the uncertainty. This new model allows for a decrease in computation time of the method, which allows for the use of later and more up-to-date forecasts. In Part II, the accuracy and the computation requirements of the method using this new model will be analyzed and compared to the previously used model for the uncertainty. 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)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Stochastic optimal power flow, Wind power, Risk-limiting dispatch, Chance-constrained optimal power flow, Edgeworth expansions, Risk-based method
in
Electric Power Systems Research
volume
119
pages
45 - 53
publisher
Elsevier
external identifiers
  • scopus:84907495082
ISSN
1873-2046
DOI
10.1016/j.epsr.2014.09.008
language
English
LU publication?
yes
id
4dc8ff87-cc44-43ce-93a0-1d10c7c74074 (old id 8518022)
date added to LUP
2016-01-12 15:54:52
date last changed
2017-01-01 08:19:09
@article{4dc8ff87-cc44-43ce-93a0-1d10c7c74074,
  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) and outage rates of chosen elements of the system (generators and transmission lines). This operating risk can be seen as a probabilistic formulation of the N − 1 criterion. The stable operation domain is defined by voltage-stability limits, small-signal stability limits, thermal stability limits and other operating limits. In Part I of the paper, a previous method for estimating the operating risk is extended by using a new model for the joint distribution of the uncertainty. This new model allows for a decrease in computation time of the method, which allows for the use of later and more up-to-date forecasts. In Part II, the accuracy and the computation requirements of the method using this new model will be analyzed and compared to the previously used model for the uncertainty. 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},
  keyword      = {Stochastic optimal power flow,Wind power,Risk-limiting dispatch,Chance-constrained optimal power flow,Edgeworth expansions,Risk-based method},
  language     = {eng},
  pages        = {45--53},
  publisher    = {Elsevier},
  series       = {Electric Power Systems Research},
  title        = {A computational framework for risk-based power system operations under uncertainty. Part I: Theory},
  url          = {http://dx.doi.org/10.1016/j.epsr.2014.09.008},
  volume       = {119},
  year         = {2015},
}