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An Importance Sampling Technique for Probabilistic Security Assessment 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.
KTH, School of Electrical Engineering (EES), Electric Power Systems.ORCID iD: 0000-0002-8189-2420
2016 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 131, 11-18 p.Article in journal (Refereed) Published
Abstract [en]

Larger amounts of variable renewable energy sources bring about larger amounts of uncertainty in the form of forecast errors. When taking operational and planning decisions under uncertainty, a trade-off between risk and costs must be made. Today's deterministic operational tools, such as N-1-based methods, cannot directly account for the underlying risk due to uncertainties. Instead, several definitions of operating risks, which are probabilistic indicators, have been proposed in the literature. Estimating these risks require estimating very low probabilities of violations of operating constraints. Crude Monte-Carlo simulations are very computationally demanding for estimating very low probabilities. In this paper, an importance sampling technique from mathematical finance is adapted to estimate very low operating risks in power systems given probabilistic forecasts for the wind power and the load. Case studies in the IEEE 39 and 118 bus systems show a decrease in computational demand of two to three orders of magnitude.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 131, 11-18 p.
Keyword [en]
Importance sampling, Monte-Carlo simulations, N-1 criterion, Risk-based operation, Stability boundary, Wind power
National Category
Energy Systems
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-166394DOI: 10.1016/j.epsr.2015.09.016Scopus ID: 2-s2.0-84944351188OAI: oai:DiVA.org:kth-166394DiVA: diva2:810747
Note

Updated from Manuscript to Article. QC 20160126

Available from: 2015-05-08 Created: 2015-05-08 Last updated: 2017-12-04Bibliographically 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.
Series
TRITA-EE, ISSN 1653-5146 ; 2015:018
Keyword
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
Identifiers
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)
Opponent
Supervisors
Note

QC 20150508

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

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Hamon, CamilleSöder, Lennart

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