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Efficient importance sampling technique for estimating operating risks in power systems with large amounts of wind power
KTH, School of Electrical Engineering (EES), Electric Power Systems.ORCID iD: 0000-0002-4173-1390
KTH, School of Electrical Engineering (EES), Electric Power Systems. Lund University.
KTH, School of Electrical Engineering (EES), Electric Power Systems.ORCID iD: 0000-0002-8189-2420
2014 (English)In: Proceedings of the 13th International Workshop on Large-scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power Plants / [ed] Uta Betancourt, Thomas Ackermann, Energynautics GmbH, 2014Conference paper (Refereed)
Abstract [en]

Uncertainties faced by operators of power systems are expected to increase with increasing amounts of wind power. This paper presents a method to design efficient importance sampling estimators to estimate the operating risk by Monte-Carlo simulations given the joint probability distribution describing the wind power and load forecasts. The operating risk is defined as the probability of violating stability and / or operating constraints. The method relies on an exisiting framework for rare-event simulations but takes into account the peculiarities of power systems. In case studies, it is shown that the number of Monte-Carlo runs needed to achieve a certain accuracy on the estimator can be reduced by up to three orders of magnitude.

Place, publisher, year, edition, pages
Energynautics GmbH, 2014.
Keyword [en]
Wind power, Importance sampling, Rare-event simulation, Monte-Carlo
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-166265ISBN: 978-3-98 13870-9-4OAI: diva2:810222
13th Wind Integration Workshop,22 - 24 October 2013 | London, UK

QC 20150507

Available from: 2015-05-06 Created: 2015-05-06 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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