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A computational framework for risk-based power systems operations under uncertainty. Part I: Theory
KTH, School of Electrical Engineering (EES), Electric Power Systems.ORCID iD: 0000-0002-4173-1390
KTH, School of Electrical Engineering (EES), Electric Power Systems.
KTH, School of Electrical Engineering (EES), Electric Power Systems.ORCID iD: 0000-0002-8189-2420
2015 (English)In: Electric power systems research, ISSN 0378-7796, Vol. 119, 45-53 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2015. Vol. 119, 45-53 p.
Keyword [en]
Wind power, Stochastic optimal power flow, Risk-limiting dispatch, Chance-constrained optimal power flow, Edgeworth expansions, Risk-based method
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-159967DOI: 10.1016/j.epsr.2014.09.008ISI: 000347756700006ScopusID: 2-s2.0-84907495082OAI: diva2:793157

QC 20150306

Available from: 2015-03-06 Created: 2015-02-12 Last updated: 2015-05-08Bibliographically approved
In thesis
1. Probabilistic security management for power system operations with large amounts of wind power
Open this publication in new window or tab >>Probabilistic security management for power system operations with large amounts of wind power
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Power systems are critical infrastructures for the society. They are therefore planned and operated to provide a reliable eletricity delivery. The set of tools and methods to do so are gathered under security management and are designed to ensure that all operating constraints are fulfilled at all times.

During the past decade, raising awareness about issues such as climate change, depletion of fossil fuels and energy security has triggered large investments in wind power. The limited predictability of wind power, in the form of forecast errors, pose a number of challenges for integrating wind power in power systems. This limited predictability increases the uncertainty already existing in power systems in the form of random occurrences of contingencies and load forecast errors. It is widely acknowledged that this added uncertainty due to wind power and other variable renewable energy sources will require new tools for security management as the penetration levels of these energy sources become significant.

In this thesis, a set of tools for security management under uncertainty is developed. The key novelty in the proposed tools is that they build upon probabilistic descriptions, in terms of distribution functions, of the uncertainty. By considering the distribution functions of the uncertainty, the proposed tools can consider all possible future operating conditions captured in the probabilistic forecasts, as well as the likeliness of these operating conditions. By contrast, today's tools are based on the deterministic N-1 criterion that only considers one future operating condition and disregards its likelihood.

Given a list of contingencies selected by the system operator and probabilitistic forecasts for the load and wind power, an operating risk is defined in this thesis as the sum of the probabilities of the pre- and post-contingency violations of the operating constraints, weighted by the probability of occurrence of the contingencies.

For security assessment, this thesis proposes efficient Monte-Carlo methods to estimate the operating risk. Importance sampling is used to substantially reduce the computational time. In addition, sample-free analytical approximations are developed to quickly estimate the operating risk. For security enhancement, the analytical approximations are further embedded in an optimization problem that aims at obtaining the cheapest generation re-dispatch that ensures that the operating risk remains below a certain threshold. The proposed tools build upon approximations, developed in this thesis, of the stable feasible domain where all operating constraints are fulfilled.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. xvi, 144 p.
TRITA-EE, ISSN 1653-5146 ; 2015:018
Power systems, wind power, probabilistic security management, chance-constrained optimal power flow, monte-carlo, importance sampling
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
urn:nbn:se:kth:diva-166398 (URN)978-91-7595-547-6 (ISBN)
Public defence
2015-05-29, E3, Lindstedtsvägen 3, KTH, Stockholm, 09:00 (English)

QC 20150508

Available from: 2015-05-08 Created: 2015-05-08 Last updated: 2015-05-08Bibliographically approved

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