Change search
Link to record
Permanent link

Direct link
BETA
Publications (10 of 18) Show all publications
Nycander, E., Söder, L., Eriksson, R. & Hamon, C. (2019). Minimising wind power curtailments using OPF considering voltage stability. The Journal of Engineering (18), 5064-5068
Open this publication in new window or tab >>Minimising wind power curtailments using OPF considering voltage stability
2019 (English)In: The Journal of Engineering, ISSN 1872-3284, E-ISSN 2051-3305, no 18, p. 5064-5068Article in journal (Refereed) Published
Abstract [en]

As the amount of wind power in power systems has increased, it has become necessary to curtail wind power in some high-penetration situations. In order to assess the need for curtailment arising from voltage stability considerations the authors develop a security constrained optimal power flow (SCOPF) for minimising the expected curtailment. The authors find that with a very high wind penetration and wind farms operating at unity power factor curtailment becomes necessary to satisfy voltage limits. In this case, the optimal solution in the studied system is to curtail at a single bus rather than curtailing by a smaller amount at several buses. However, allowing for reactive power production from wind farms reduces the need for curtailments.

Place, publisher, year, edition, pages
INST ENGINEERING TECHNOLOGY-IET, 2019
Keywords
load flow, wind power plants, wind power, reactive power, power system security, power factor, minimising wind power curtailments, OPF considering voltage stability, power systems, high-penetration situations, voltage stability considerations, optimal power flow, expected curtailment, high wind penetration, wind farms, unity power factor curtailment, voltage limits, reactive power production
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-260206 (URN)10.1049/joe.2018.9371 (DOI)000482590000074 ()
Note

QC 20190930

Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-09-30Bibliographically approved
Hamon, C., Perninge, M. & Söder, L. (2016). An Importance Sampling Technique for Probabilistic Security Assessment In Power Systems with Large Amounts of Wind Power. Electric power systems research, 131, 11-18
Open this publication in new window or tab >>An Importance Sampling Technique for Probabilistic Security Assessment In Power Systems with Large Amounts of Wind Power
2016 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 131, p. 11-18Article 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
Keywords
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:nbn:se:kth:diva-166394 (URN)10.1016/j.epsr.2015.09.016 (DOI)2-s2.0-84944351188 (Scopus ID)
Note

Updated from Manuscript to Article. QC 20160126

Available from: 2015-05-08 Created: 2015-05-08 Last updated: 2017-12-04Bibliographically approved
Hamon, C., Perninge, M. & Söder, L. (2015). A computational framework for risk-based power system operations under uncertainty. Part II: Case studies. Electric power systems research, 119, 66-75
Open this publication in new window or tab >>A computational framework for risk-based power system operations under uncertainty. Part II: Case studies
2015 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 119, p. 66-75Article 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. 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.

Keywords
Wind power, Stochastic optimal power flow, Risk-limiting dispatch, Chance-constrained optimal power flow, Edgeworth expansions, Risk-based methods
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-159968 (URN)10.1016/j.epsr.2014.09.007 (DOI)000347756700008 ()2-s2.0-84907487450 (Scopus ID)
Note

QC 20150306

Available from: 2015-03-06 Created: 2015-02-12 Last updated: 2017-12-04Bibliographically approved
Hamon, C., Perninge, M. & Söder, L. (2015). A computational framework for risk-based power systems operations under uncertainty. Part I: Theory. Electric power systems research, 119, 45-53
Open this publication in new window or tab >>A computational framework for risk-based power systems operations under uncertainty. Part I: Theory
2015 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 119, p. 45-53Article 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.

Keywords
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
Identifiers
urn:nbn:se:kth:diva-159967 (URN)10.1016/j.epsr.2014.09.008 (DOI)000347756700006 ()2-s2.0-84907495082 (Scopus ID)
Note

QC 20150306

Available from: 2015-03-06 Created: 2015-02-12 Last updated: 2017-12-04Bibliographically approved
Hamon, C. (2015). Probabilistic security management for power system operations with large amounts of wind power. (Doctoral dissertation). Stockholm: KTH Royal Institute of Technology
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. p. xvi, 144
Series
TRITA-EE, ISSN 1653-5146 ; 2015:018
Keywords
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
Hamon, C., Shayesteh, E., Amelin, M. & Söder, L. (2015). Two partitioning methods for multi-area studies in large power systems. International Transactions on Electrical Energy Systems, 25(4), 648-660
Open this publication in new window or tab >>Two partitioning methods for multi-area studies in large power systems
2015 (English)In: International Transactions on Electrical Energy Systems, E-ISSN 2050-7038, Vol. 25, no 4, p. 648-660Article in journal (Refereed) Published
Abstract [en]

Multi-area studies are an important tool for today's and future power systems. In this paper, a two-step algorithm for creating multi-area models is presented that, first, identifies areas, and, second, computes reduced models of these areas. For the first step, two new methods to identify areas in power systems have been developed. The first method is based upon spectral partitioning, whereas the second one is formulated as a linear optimization problem. The methods are compared in terms of computation time on the IEEE 118 bus system, and the first method clearly stands out in this comparison. The first method is then applied to the IEEE 300 bus system and to a model of the Polish power system with 2746 buses to study how it scales in large power systems. Even in the latter case, it runs in less than 30s. For the second step, existing equivalencing methods can be used. As an example, radial, equivalent, and independent equivalents are used to model the areas identified by the partitioning methods. The partitioning and equivalencing methods have been tested on the IEEE 118 bus system by running 1000 regular and optimal power flows. Comparisons with the original IEEE 118 bus system in terms of flows, costs and losses are carried out.

Keywords
spectral partitioning, power system partitioning, multi-area study, REI
National Category
Energy Systems
Identifiers
urn:nbn:se:kth:diva-158949 (URN)10.1002/etep.1864 (DOI)000353388700006 ()2-s2.0-84928023607 (Scopus ID)
Note

QC 20150805

Available from: 2015-01-16 Created: 2015-01-16 Last updated: 2017-12-05Bibliographically approved
Hamon, C., Perninge, M. & Söder, L. (2014). Efficient importance sampling technique for estimating operating risks in power systems with large amounts of wind power. In: Uta Betancourt, Thomas Ackermann (Ed.), 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: . Paper presented at 13th Wind Integration Workshop,22 - 24 October 2013 | London, UK. Energynautics GmbH
Open this publication in new window or tab >>Efficient importance sampling technique for estimating operating risks in power systems with large amounts of wind power
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, Published 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
Keywords
Wind power, Importance sampling, Rare-event simulation, Monte-Carlo
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-166265 (URN)978-3-98 13870-9-4 (ISBN)
Conference
13th Wind Integration Workshop,22 - 24 October 2013 | London, UK
Note

QC 20150507

Available from: 2015-05-06 Created: 2015-05-06 Last updated: 2015-05-08Bibliographically approved
Shayesteh, E., Hamon, C., Amelin, M. & Söder, L. (2014). REI method for multi-area modeling of power systems. International Journal of Electrical Power & Energy Systems, 60, 283-292
Open this publication in new window or tab >>REI method for multi-area modeling of power systems
2014 (English)In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 60, p. 283-292Article in journal (Refereed) Published
Abstract [en]

Interconnections between different electricity markets and high penetration levels of wind power have resulted in an increase in the size of power systems with higher levels of uncertainties. This paper presents an algorithm for bulk power system simulations with large wind power penetrations, based on multi-area modeling with transmission constraints. The present study differs from previous multi-area studies by taking into account the capacity of intra-area lines during the simulations, which leads to more accurate results. The method that we introduce consists of three steps. First, a power system with high wind power penetration is divided into several areas using a practical measure, admittance matrix. Second, the internal system of each area is replaced with a smaller system, to which an improved version of the REI (Radial, Equivalent, and Independent) method is developed and applied. Finally, the technical properties of the reduced power system (such as voltage limits and transmission capacities) are tuned by adjusting optimization, in a way that the simulation results of the reduced power system are comparable with those of the original system. The IEEE 30-bus and IEEE 118-bus test systems are used to show the efficiency of the proposed algorithm.

Keywords
Multi-area modeling, REI equivalent method, System partitioning, Wind power penetration
National Category
Energy Systems
Identifiers
urn:nbn:se:kth:diva-145379 (URN)10.1016/j.ijepes.2014.03.002 (DOI)000336340400028 ()2-s2.0-84898065151 (Scopus ID)
Note

QC 20140520

Available from: 2014-05-20 Created: 2014-05-19 Last updated: 2017-12-05Bibliographically approved
Hamon, C., Perninge, M. & Söder, L. (2013). A Stochastic Optimal Power Flow Problem With Stability Constraints-Part I: Approximating the Stability Boundary. IEEE Transactions on Power Systems, 28(2), 1839-1848
Open this publication in new window or tab >>A Stochastic Optimal Power Flow Problem With Stability Constraints-Part I: Approximating the Stability Boundary
2013 (English)In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 28, no 2, p. 1839-1848Article in journal (Refereed) Published
Abstract [en]

Stochastic optimal power flow can provide the system operator with adequate strategies for controlling the power flow to maintain secure operation under stochastic parameter variations. One limitation of stochastic optimal power flow has been that only line flows have been used as security constraints. In many systems voltage stability and small-signal stability also play an important role in constraining the operation. In this paper we aim to extend the stochastic optimal power flow problem to include constraints for voltage stability as well as small-signal stability. This is done by approximating the voltage stability and small-signal stability constraint boundaries with second-order approximations in parameter space. Then we refine methods from mathematical finance to be able to estimate the probability of violating the constraints. In this first part of the paper, we derive second-order approximations of stability boundaries in parameter space. In the second part, the approximations will be used to solve a stochastic optimal power flow problem.

Keywords
Hopf bifurcation, saddle-node bifurcation, stability boundary, stochastic optimal power flow (SOPF), switching loadability limit
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-129639 (URN)10.1109/TPWRS.2012.2226760 (DOI)000322139300129 ()2-s2.0-84886423924 (Scopus ID)
Funder
StandUp
Note

QC 20150623

Available from: 2013-10-03 Created: 2013-10-03 Last updated: 2017-12-06Bibliographically approved
Perninge, M. & Hamon, C. (2013). A Stochastic Optimal Power Flow Problem With Stability Constraints-Part II: The Optimization Problem. IEEE Transactions on Power Systems, 28(2), 1849-1857
Open this publication in new window or tab >>A Stochastic Optimal Power Flow Problem With Stability Constraints-Part II: The Optimization Problem
2013 (English)In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 28, no 2, p. 1849-1857Article in journal (Refereed) Published
Abstract [en]

Stochastic optimal power flow can provide the system operator with adequate strategies for controlling the power flow to maintain secure operation under stochastic parameter variations. One limitation of stochastic optimal power flow has been that only limits on line flows have been used as stability constraints. In many systems voltage stability and small-signal stability also play an important role in constraining the operation. In this paper we aim to extend the stochastic optimal power flow problem to include constraints for voltage stability as well as small-signal stability. This is done by approximating the voltage stability and small-signal stability constraint surfaces with second-order approximations in parameter space. Then we refine methods from mathematical finance to be able to estimate the probability of violating the constraints. In this, the second part of the paper, we look at how Cornish-Fisher expansion combined with a method of excluding sets that are counted twice, can be used to estimate the probability of violating the stability constraints. We then show in a numerical example how this leads to an efficient solution method for the stochastic optimal power flow problem.

Keywords
Hopf bifurcation, saddle-node bifurcation, stochastic optimal power flow, switching loadability limit
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-129640 (URN)10.1109/TPWRS.2012.2226761 (DOI)000322139300130 ()2-s2.0-84886388808 (Scopus ID)
Funder
StandUp
Note

QC 20150623

Available from: 2013-10-03 Created: 2013-10-03 Last updated: 2017-12-06Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4173-1390

Search in DiVA

Show all publications