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  • 1.
    Eriksson, Robert
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Perninge, Magnus
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Coordination of HVDC-links to increase dynamic stability margins2010In: 2010 IEEE International Conference on Power and Energy, PECon2010, 2010, p. 183-188Conference paper (Refereed)
    Abstract [en]

    The main contribution of this paper is a new method for adaptively coordinating the power modulation of multiple HVDC-links in a power system, to enhance the total transfer capacity. This in turn may lead to a more active electricity market. The increase in transfer capacity is obtained by an adaptive coordinated modulation control of multiple HVDC-links in the system. The control method is based on maximizing the distance to the bifurcation surface by adjusting the feedback gain of the HVDC-links modulation controllers.

  • 2.
    Eriksson, Robert
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Perninge, Magnus
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Transfer capacity enhancement by adaptive coordinated controlof HVDC-links based on forecasted load paths2011In: European transactions on electrical power, ISSN 1430-144X, E-ISSN 1546-3109, Vol. 21, no 3, p. 1455-1466Article in journal (Refereed)
    Abstract [en]

    Due to the intensive use of the transmission networks one of the major issues in electric energy trading is bottlenecks limiting the transfer capacity between different system areas. In this article, a new method for increasing transfer capacity is suggested. The increase in transfer capacity is obtained by an adaptive coordinated modulation control of multiple HVDC-links in the system. The control method is based on maximizing the distance to the bifurcation surface by adjusting the feedback gain of the HVDC-links modulation controllers. The system is linearized along the forecasted load path. The feedback gains are then chosen in such a way that system remains stable, in a small signal sense, as long as possible along the forecasted load path. The arising optimization problem is then solved using a particle swarm optimization method. If the load is predicted to increase, instability will eventually occur when the loading reaches a critical limit. Using the proposed control method the point in load-space where instability occur will be at a significantly higher loading level. The main contribution of this paper is the proposed new method for adaptively coordinating the power modulation of multiple HVDC-links in a power system, to enhance the total transfer capacity. This in turn will lead to a possibility to increase the traded volumes on the electricity market.

  • 3.
    Hamon, Camille
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Perninge, Magnus
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    A computational framework for risk-based power system operations under uncertainty. Part II: Case studies2015In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 119, p. 66-75Article in journal (Refereed)
    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.

  • 4.
    Hamon, Camille
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Perninge, Magnus
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    A computational framework for risk-based power systems operations under uncertainty. Part I: Theory2015In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 119, p. 45-53Article in journal (Refereed)
    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.

  • 5.
    Hamon, Camille
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Perninge, Magnus
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    A Stochastic Optimal Power Flow Problem With Stability Constraints-Part I: Approximating the Stability Boundary2013In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 28, no 2, p. 1839-1848Article in journal (Refereed)
    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.

  • 6.
    Hamon, Camille
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Perninge, Magnus
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    An Importance Sampling Technique for Probabilistic Security Assessment In Power Systems with Large Amounts of Wind Power2016In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 131, p. 11-18Article in journal (Refereed)
    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.

  • 7.
    Hamon, Camille
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Perninge, Magnus
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Applying stochastic optimal power flow to power systems with large amounts of wind power and detailed stability limits2013In: Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid (IREP), 2013 IREP Symposium, 2013Conference paper (Refereed)
    Abstract [en]

    Increasing wind power penetration levels bring about new challenges for power systems operation and planning, because wind power forecast errors increase the uncertainty faced by the different actors. One specific problem is generation re-dispatch during the operation period, a problem in which the system operator seeks the cheapest way of re-dispatching generators while maintaining an acceptable level of system security. Stochastic optimal power flows are re-dispatch algorithms which account for the uncertainty in the optimization problem itself. In this article, an existing stochastic optimal power flow (SOPF) formulation is extended to include the case of non-Gaussian distributed forecast errors. This is an important case when considering wind power, since it has been shown that wind power forecast errors are in general not normally distributed. Approximations are necessary for solving this SOPF formulation. The method is illustrated in a small power system in which the accuracy of these approximations is also assessed for different probability distributions of the load and wind power.

  • 8.
    Hamon, Camille
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Perninge, Magnus
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Closure of 'applying stochastic optimal power flow to power systems with large amounts of wind power and detailed stability limits'2013In: Proceedings of IREP Symposium: Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid, IREP 2013, 2013Conference paper (Refereed)
    Abstract [en]

    We thank the authors of the discussion in [1] for raising the issue of cascading events and correlated blackouts. Our method in [2] was designed as a stochastic version of the security-constrained optimal power flow (SCOPF), in which the system should be operated to remain stable in some sense (deterministic or stochastic) after any single pre-selected contingency occurs.

  • 9.
    Hamon, Camille
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Perninge, Magnus
    KTH, School of Electrical Engineering (EES), Electric Power Systems. Lund University.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Efficient importance sampling technique for estimating operating risks in power systems with large amounts of wind power2014In: 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.

  • 10.
    Hamon, Camille
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Perninge, Magnus
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Stochastic Optimal Power Flow Problem with Stability Constraints2013In: 2013 IEEE Power and Energy Society General Meeting (PES), IEEE , 2013Conference paper (Refereed)
  • 11.
    Hamon, Camille
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Perninge, Magnus
    Department of Automatic Control, Lund University, Sweden .
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    The value of using chance-constrained optimal power flows for generation re-dispatch under uncertainty with detailed security constraints2013In: 2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), IEEE Computer Society, 2013, p. 6837148-Conference paper (Refereed)
    Abstract [en]

    The uncertainty faced in the operation of power systems increases as larger amounts of intermittent sources, such as wind and solar power, are being installed. Traditionally, an optimal generation re-dispatch is obtained by solving security-constrained optimal power flows (SCOPF). The resulting system operation is then optimal for given values of the uncertain parameters. New methods have been developed to consider the uncertainty directly in the generation re-dispatch optimization problem. Chance-constrained optimal power flows (CCOPF) are such methods. In this paper, SCOPF and CCOPF are compared and the benefits of using CCOPF for power systems operation under uncertainty are discussed. The discussion is illustrated by a case study in the IEEE 39 bus system, in which the generation re-dispatch obtained by CCOPF is shown to always be cheaper than that obtained by SCOPF.

  • 12.
    Olsson, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Perninge, Magnus
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Modeling real-time balancing power demands in wind power systems using stochastic differential equations2010In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 80, no 8, p. 966-974Article in journal (Refereed)
    Abstract [en]

    The inclusion of wind power into power systems has a significant impact on the demand for real-rime balancing power due to the stochastic nature of wind power production The overall aim of this paper is to present probabilistic models of the impact of large-scale integration of wind power on the continuous demand in MW for real-time balancing power This is important not only for system operators, but also for producers and consumers since they in most systems through various market solutions provide balancing power.

    Since there can occur situations where the wind power variations cancel out other types of deviations in the system, models on an hourly basis are not sufficient Therefore the developed model is in continuous time and is based on stochastic differential equations (SDE) The model can be used within an analytical framework or in Monte Carlo simulations.

  • 13.
    Olsson, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Perninge, Magnus
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Simulation of real-time balancing power demands in power systems with wind power2010In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 80, p. 966-974Article in journal (Refereed)
  • 14.
    Perninge, Magnus
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    A Stochastic Control Approach to Include Transfer Limits in Power System Operation2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The main function of the power grid is to transfer electric energy from generating facilities to consumers. To have a reliable and economical supply of electricity, large amounts of electric energy often have to be transferred over long distances.

    The transmission system has a limited capacity to transfer electric power, called the transfer capacity. Severe system failures may follow if the transfer capacity is reached during operation.

    Due to uncertainties, such as the random failure of system components, the transfer capacity for the near future is not readily determinable. Also, due to market principles, and reaction times and ramp rates of production facilities, power flow control is not fully flexible. Therefore, a transfer limit, which is below the transfer capacity, is decided and preventative actions are taken when the transfer reaches this limit.

    In this thesis an approach to deciding an optimal strategy for power flow control through activation of regulating bids on the regulating power market is outlined. This approach leads to an optimal definition of transfer limits as the boundary between the domain where no bid should be activated and the domains where bids should be activated. The approach is based on weighing the expected cost from system failures against the production cost. This leads to a stochastic impulse control problem for a Markov process in continuous time.

    The proposed method is a novel approach to decide transfer limits in power system operation. The method is tested in a case study on the IEEE 39 bus system, that shows promising results.

    In addition to deciding optimal transfer limits, it is also investigated how the transfer capacity can be enhanced by controlling components in the power system to increase stability.

  • 15.
    Perninge, Magnus
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Approximating the loadability surface in the presence of SNB-SLL corner points2013In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 96, p. 64-74Article in journal (Refereed)
    Abstract [en]

    Power system voltage security assessment is generally applied by considering the power system loadability surface. For a large power system, the loadability surface is a complicated hyper-surface in parameter space, and local approximations are a necessity for any analysis. Unfortunately, inequality constraints due to for example generator overexitation limiters, and higher codimension bifurcations, make the loadability surface non-smooth. One situation that is particularly difficult to handle is when a saddle-node bifurcation surface intersects a switching loadability limit surface. In this article we intend to investigate how several local approximations can be combined to obtain an adequate approximation of the loadability surface near such intersections.

  • 16.
    Perninge, Magnus
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Modeling the Uncertainties Involved in Net Transmission Capacity Calculation2009Licentiate thesis, monograph (Other academic)
  • 17.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Knazkins, Valerijs
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Comparing Variance Reduction Techniques for Monte Carlo Simulation of Trading and Security in a Three-Area Power System2008In: 2008 IEEE/PES TRANSMISSION AND DISTRIBUTION CONFERENCE AND EXPOSITION: LATIN AMERICA, VOLS 1 AND 2, IEEE , 2008, p. 461-465Conference paper (Refereed)
    Abstract [en]

    A variance reduction technique is a method used to reduce the variance of a Monte Carlo Simulation. In this paper four of the most commonly used variance reduction techniques are tested to estimate the trade of between trading and security in a three-area electric power system. The comparison is made by regarding both the variance reduction and the bias induced by the method.

  • 18.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Knazkins, Valerijs
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Load Modeling Using the Ornstein-Uhlenbeck Process2008In: 2008 IEEE 2ND INTERNATIONAL POWER AND ENERGY CONFERENCE: PECON, VOLS 1-3, NEW YORK: IEEE , 2008, p. 819-821Conference paper (Refereed)
    Abstract [en]

    In this paper we show how to model the load in an electric power system using the Ornstein-Uhlenbeek process and use the method developed by Lehmann to find the distribution of the maximum of the load process in a bounded time interval. A numerical example showing how to find a upper confidence bound for the maximum of the load process in a bounded time interval using the proposed method will also be given.

  • 19.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Eriksson, Robert
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Using a fixed point method to compute the parameter space distance to the surface of constant critical energy2012In: 2012 11th International Conference on Environment and Electrical Engineering, EEEIC 2012 - Conference Proceedings, IEEE , 2012, p. 298-302Conference paper (Refereed)
    Abstract [en]

    Power system voltage security assessment is generally applied by considering the steady-state stability surface. However, as seen in the literature, random perturbations can drive the system away from stable operation, long before the steady-state stability surface is reached. In this article we include the value of the critical energy for the transient energy function, as a stability criterion when defining the stability boundary surface in parameter space. We show how the closest point on the surface of constant critical energy can be computed. The main contribution of this article is that it serves as a first step to include dynamic voltage collapse issues in the analysis of bulk power transfer security.

  • 20.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Hamon, Camille
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    A Stochastic Optimal Power Flow Problem With Stability Constraints-Part II: The Optimization Problem2013In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 28, no 2, p. 1849-1857Article in journal (Refereed)
    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.

  • 21.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Knazkins, Valerijs
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Monte Carlo Analysis of the Trade-Off between Trading and Security in a Two-Area System2007In: GMSARN (The Greater Mekong Subregion Academic and Research Network) International Conference 2007: Sustainable Development: Challenges and Opportunities for the Greater Mekong Subregion, 2007Conference paper (Refereed)
  • 22.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Knazkins, Valerijs
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Estimating Upper Confidence Bounds of Electric Power Consumption2009In: THIRTEEN INTERNATIONAL MIDDLE- EAST POWER SYSTEMS CONFERENCE, MEPCON'2009, 2009Conference paper (Refereed)
  • 23.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Knazkins, Valerijs
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Modeling the electric power consumption in a multi-area system2011In: European transactions on electrical power, ISSN 1430-144X, E-ISSN 1546-3109, Vol. 21, no 1, p. 413-423Article in journal (Refereed)
    Abstract [en]

    In this article a model of the electric power consumption in a multi-area power system is derived. The model is based on stochastic differential equations to mimic the stochastic behavior of electric power consumption in large systems. The developed model considers correlations between the consumptions in the different areas. A numerical example showing how to find the parameters of the process will be given. The load data used in the numerical example is hourly energy consumption data for the Nordic countries: Sweden, Norway, and Finland.

  • 24.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Knazkins, Valerijs
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Risk Estimation of Critical Time to Voltage Instability Induced by Saddle-Node Bifurcation2010In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 25, no 3, p. 1600-1610Article in journal (Refereed)
    Abstract [en]

    Prevention of voltage instability in electric power systems is an important objective that the system operators have to meet. Under certain circumstances the operating point of the power system may start drifting towards the set of voltage unstable operating points. If no preventive measures are taken, after some time the operating point may eventually become voltage unstable. It will thus be preferable to have a measure of the risk of voltage collapse in future loading states. This paper presents a novel method for estimation of the probability distribution of the time to voltage instability for a power system with uncertain future loading scenarios. The method uses a distance from the predicted load-path to the set of voltage unstable operating points when finding an estimate of the time to voltage instability. This will reduce the problem to a one-dimensional problem which for large systems decreases the computation time significantly.

  • 25.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Knazkins, Valerijs
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    The Impact of a Given Trading Limit on a Two-Area Test System2009In: 2009 IEEE BUCHAREST POWERTECH / [ed] Toma L; Otomega B, NEW YORK: IEEE , 2009, p. 2122-2127Conference paper (Refereed)
    Abstract [en]

    This paper uses methods from stochastic analysis and stochastic modeling to determine the impact of a certain trading limit on the transfer between the two areas of a benchmark two-area power system. We also try to state which uncertainties are important to consider when calculating this power transfer.

  • 26.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Lindskog, Filip
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Importance Sampling of Injected Powers for Electric Power System Security Analysis2011In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 27, no 1, p. 3-11Article in journal (Refereed)
    Abstract [en]

    Power system security analysis is often strongly tied with contingency analysis. To improve Monte Carlo simulation, many different contingency selection techniques have been proposed in the literature.

  • 27.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    A probabilistic distance to the power system secure operation boundary2010In: 2010 IREP Symposium - Bulk Power System Dynamics and Control - VIII, IREP2010, 2010Conference paper (Refereed)
    Abstract [en]

    Estimation of power system operation boundaries is an important issue in power system engineering. To make an adequate approximation of the operation boundary, not only the geometric properties of the boundary have to be taken under consideration but also the stochastic properties of the future injected power. In this paper two different distance functions on the power system operation boundary are suggested. The methods used to find the distance functions assumes that the future injected power can be modeled by a diffusion process. The proposed distance functions can be used when an approximation such as a Taylor's expansion of the power system operation boundary is needed. It is then suggested to use the point on the operation boundary that minimizes one of the proposed distance functions, as the basis for the approximation. Another possible application of the distance functions is when wanting to control power system equipment in order to increase stability margins.

  • 28.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    A Stochastic Control Approach to Manage Operational Risk in Power SystemsManuscript (preprint) (Other academic)
    Abstract [en]

    In this article the novel method Operational Risk Managing Optimal Power Flow (ORMOPF), for minimizing the expected cost of power system operation, is proposed. In contrast to previous research in the area, the proposed method does not use a security criterion. Instead the expected cost of operation includes expected costs of system failures.

    This will lead to more flexible operating limits, giving a more adequate balance between risk and economic benefit of transmission.

    The method assumes a set of observable system variables such as transfers through specific transmission corridors, system frequency, or distance to a bifurcation surface. Then impulse control is applied to find an optimal strategy for activation of tertiary reserves, based on the values of the observables.

  • 29.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    A Stochastic Control Approach to Manage Operational Risk in Power Systems2012In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 27, no 2, p. 1021-1031Article in journal (Refereed)
    Abstract [en]

    In this paper, the novel method operational risk managing optimal power flow (ORMOPF), for minimizing the expected cost of power system operation, is proposed. In contrast to previous research in the area, the proposed method does not use a security criterion. Instead the expected cost of operation includes expected costs of system failures. This will lead to more flexible operating limits, giving a more adequate balance between risk and economic benefit of transmission. The method assumes a set of observable system variables such as transfers through specific transmission corridors, system frequency, or distance to a bifurcation surface. Then impulse control is applied to find an optimal strategy for activation of tertiary reserves, based on the values of the observables.

  • 30.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Analysis of Transfer Capability by Markov Chain Monte Carlo Simulation2010In: PECon2010 - 2010 IEEE International Conference on Power and Energy, 2010, p. 232-237Conference paper (Refereed)
  • 31.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Analysis of transfer-limit induced power system security by Markov chain Monte Carlo simulation2012In: European transactions on electrical power, ISSN 1430-144X, E-ISSN 1546-3109, Vol. 22, no 2, p. 140-151Article in journal (Refereed)
    Abstract [en]

    Adequate security margins are commonly applied in power systems by keeping predefined transfer limits through certain transmission corridors in the system. These limits are often set to keep the criterion stating that the system should remain stable after the loss of any component. For many stability criteria such as, voltage stability, and voltage limits at specific nodes, the distribution of the injected power amongst the nodes of the system will be of vital importance. To incorporate this into the analysis of transfer limits the uncertainties in nodal loading and wind power production will have to be considered. In this article we propose a new method for generating samples of the power at all nodes given a set of transfers through specified corridors of the power system. It is then shown how the method can be used to evaluate the risk of violating the system stability limits induced by choosing a specific set of transfer limits. The method can be used in power system operations planning when setting the limits for trading and transfer between the different nodes of the power system.

  • 32.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Geometric Properties of the Loadability Surface at SNB-SLL Intersections and Tangential Intersection Points2011In: 16th International Conference on Intelligent System Application to Power Systems (ISAP) 2011, 2011, p. 1-5Conference paper (Refereed)
  • 33.
    Perninge, Magnus
    et al.
    Department of Automatic Control, Lund University.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Irreversible Investments with Delayed Reaction: An Application to Generation Re-Dispatch in Power System Operation2014In: Mathematical Methods of Operations Research, ISSN 1432-2994, E-ISSN 1432-5217, Vol. 79, no 2, p. 195-224Article in journal (Refereed)
    Abstract [en]

    In this article we consider how the operator of an electric power system should activate bids on the regulating power market in order to minimize the expected operation cost. Important characteristics of the problem are reaction times of actors on the regulating market and ramp-rates for production changes in power plants. Neglecting these will in general lead to major underestimation of the operation cost. Including reaction times and ramp-rates leads to an impulse control problem with delayed reaction. Two numerical schemes to solve this problem are proposed. The first scheme is based on the least-squares Monte Carlo method developed by Longstaff and Schwartz (Rev Financ Stud 14:113-148, 2001). The second scheme which turns out to be more efficient when solving problems with delays, is based on the regression Monte Carlo method developed by Tsitsiklis and van Roy (IEEE Trans Autom Control 44(10):1840-1851, 1999) and (IEEE Trans Neural Netw 12(4):694-703, 2001). The main contribution of the article is the idea of using stochastic control to find an optimal strategy for power system operation and the numerical solution schemes proposed to solve impulse control problems with delayed reaction.

  • 34.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    On the validity of local approximations of the power system loadability surface2011In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 26, no 4, p. 2143-2153Article in journal (Refereed)
    Abstract [en]

    Power system voltage security assessment is generally applied by considering the power system loadability surface. For a large power system, the loadability surface is a complicated hyper-surface in parameter space, and local approximations are a necessity for any analysis. Unfortunately, inequality constraints due to for example generator overexitation limiters and higher codimension bifurcations makes the loadability surface nonsmooth. This makes the use of local approximations limited and calls for a method for estimating the distance to a nonsmooth part of the surface. This paper suggests a method for calculating the distance from a point on the loadability surface to the closest point of nonsmoothness of the loadability surface.

  • 35.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Optimal activation of regulating bids to handle bottlenecks in power system operation2012In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 83, no 1, p. 151-159Article in journal (Refereed)
    Abstract [en]

    In this article we investigate how to optimally activate regulating bids to handle bottlenecks inpower system operation. This will lead to an optimal stopping problem, and activation of aregulating bid is to be performed when the transfer through a specific system bottleneck reachesa certain value. Compared to previous research in the area the work presented in this articleincludes a more detailed model of the structure of the regulating market, and reaction times ofactors on the regulating market is taken into consideration. The emphasis of the presentation willbe application to a two area test system. The method is compared to Monte Carlo simulation ina numerical example. The example shows a promising result for the suggested method.

  • 36.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Optimal distribution of primary control participation with respect to voltage stability2010In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 80, no 11, p. 1357-1363Article in journal (Refereed)
    Abstract [en]

    In competitive electricity markets the transmission system will at times be heavily loaded. At these occasions prevention of voltage instability is an important objective that the system operator has to meet. In this paper a method for finding the primary control participation that maximizes the margin from an operating point to the saddle-node bifurcation surface is proposed. The arising optimization problem is solved using a steepest descent method. The proposed method can find its applications both in generation planning and in real-time operation of electric power systems.

  • 37.
    Perninge, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Risk estimation of the distance to voltage instability using a second order approximation of the saddle-node bifurcation surface2011In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 81, no 2, p. 625-635Article in journal (Refereed)
    Abstract [en]

    Prevention of voltage instability in electric power systems is an important objective that the systemoperators have to meet. Under certain circumstances the operating point of the power system may startdrifting towards the set of voltage unstable operating points. If no preventive measures are taken, aftersome time, the operating point may eventually become voltage unstable. It will thus be preferable to havea measure of the risk of voltage collapse in future loading states. This paper presents a novel method forestimation of the probability distribution of the load-space distance to the point where voltage instabilityinduced by saddle-node bifurcation occurs. Another result of the method is an estimate of the probabilitydistribution of the time to voltage instability for a power system with uncertain future loading scenarios.The method uses a second order approximation of the saddle-node bifurcation surface. The proposedmethod can be used in power system security assessments.

1 - 37 of 37
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