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  • 1.
    Hamon, Camille
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Probabilistic security management for power system operations with large amounts of wind power2015Doctoral 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.

  • 2.
    Hamon, Camille
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    On Frequency Control Schemes in Power Systems with Large Amounts of Wind Power2012Licentiate thesis, monograph (Other academic)
    Abstract [en]

    In recent years, large investments have been made in wind power, and this trend is expected to continue in the coming decades. Integrating more wind power in the production mix offers great opportunities for the society, such as reducing greenhouse gas emissions and the dependence on foreign fuel. Large wind power penetration does, however, require changes in the way power systems are planned and operated.

    The power transfers across the electrical grid are determined by the load and the production. A secure operation of power systems requires that these power transfers stay within certain limits. Frequency control schemes are crucial for ensuring the balance between the electric demand and the production. They enable system operators to re-dispatch the production (for example via the activation of balancing bids) during real-time operations to follow the load variations. With wind power, these frequency control schemes must not only meet the variations of the load but also those of the wind.

    An optimal use of the frequency control reserves would allow system operators to operate the system in the most cost effective and secure manner, that is, using the cheapest available resources while taking into account the stability limits of the system and the uncertainty. With no wind power, the load is the main source of uncertainty, and it can be forecasted accurately. This enables system operators to dispatch the generation in the most cost-effective way to meet the load while keeping the system within its stability limits. Adding wind power to power systems, on the other hand, introduces a new source of uncertainty on the production side, which is more difficult to forecast. The tools used today for computing the stability limits and operating the system do not consider the whole range of possible future load and wind power production levels, but only pick a few likely values in this range.

    In this work, we propose a new approach which accounts for the whole uncertainty in the load and wind power, and gives the optimal re-dispatch which ensures a given level of system security given this uncertainty. The approach is a so-called Stochastic Optimal Power Flow (S-OPF) formulation, developed in the scope of this project for the optimal activation of balancing bids. It is a nonlinear optimization problem with one probabilitistic constraint ensuring a certain level of system security -- computed as the probability that the system stays within its stability limits -- and whose objective function is the minimization of the generation re-dispatch. Compared to what is done today, the S-OPF formulation enables system operators to consider the uncertainty when making decisions.

    An approximation of the proposed S-OPF formulation is developed to render the problem tractable. In particular, the stability boundary, defined as the set of stability limits, is approximated by second-order approximations. The accuracy of these second-order approximations are analyzed in the IEEE 9 bus system by computing the distance between the actual boundary and its approximation. The S-OPF problem is then solved in the IEEE 39 bus system using the approximated stability boundaries. Monte Carlo simulations are run in order to assess the accuracy of the approximation and check whether the optimal solution of the approximation does ensure the specified level of system security.

  • 3.
    Hamon, Camille
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Elkington, Katherine
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Ghandhari, Mehrdad
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Doubly-fed Induction Generator Modeling and Control in DigSilent PowerFactory2010In: 2010 International Conference on Power System Technology: Technological Innovations Making Power Grid Smarter, POWERCON2010, IEEE , 2010, 1-7 p.Conference paper (Refereed)
    Abstract [en]

    Several computer programs exist to carry out dynamicsimulations and this study will focus on one of them,namely DigDilent PowerFactory. It offers two built-in modelsof doubly-fed induction generator. A new model has also beendeveloped, based upon a controllable voltage source. Thesethree models are compared, in terms of dynamic behavior andsimulation time. One of them is then used to study the impact ofan input control signal based on the single machine equivalentmethod. This signal provides power oscillation damping.

  • 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 Stochastic Optimal Power Flow Problem With Stability Constraints-Part I: Approximating the Stability Boundary2013In: IEEE Transactions on Power Systems, ISSN 0885-8950, Vol. 28, no 2, 1839-1848 p.Article 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.

  • 5.
    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, 6837148- p.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.

  • 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, 11-18 p.Article 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.
    A computational framework for risk-based power system operations under uncertainty. Part II: Case studies2015In: Electric power systems research, ISSN 0378-7796, Vol. 119, 66-75 p.Article 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.

  • 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.
    A computational framework for risk-based power systems operations under uncertainty. Part I: Theory2015In: Electric power systems research, ISSN 0378-7796, Vol. 119, 45-53 p.Article 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.

  • 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.
    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.

  • 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
    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.

  • 12.
    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.

  • 13.
    Hamon, Camille
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Shayesteh, Ebrahim
    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.
    Two partitioning methods for multi-area studies in large power systems2015In: International Transactions on Electrical Energy Systems, E-ISSN 2050-7038, Vol. 25, no 4, 648-660 p.Article in journal (Refereed)
    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.

  • 14.
    Hamon, Camille
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Review Paper on Wind Power Impact on Operation of Reserves2011In: 2011 8th International Conference on the European Energy Market, EEM 11, 2011, 895-903 p.Conference paper (Refereed)
    Abstract [en]

    This paper reviews studies concerning new challengesfor European transmission system operators (TSOs) when operating primary, secondary and tertiary reserves in a systemwith large amounts of wind power. The review adopts three perspectives. First, the impact on existing markets is discussed and it is shown that need for additional reserve requirements does not necessarily mean need for new reserve capacity. Secondly, possible designs of improved load-frequency control schemes are presented.The proposed solutions exhibit a trend towards market-based procurement mechanisms and automation of reserve operations. Finally, participation of wind power in load-frequency control is examined. Technical designs are presented for participation inprimary control.

  • 15.
    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, Vol. 28, no 2, 1849-1857 p.Article 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.

  • 16.
    Shayesteh, Ebrahim
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Hamon, Camille
    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.
    REI method for multi-area modeling of power systems2014In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 60, 283-292 p.Article in journal (Refereed)
    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.

  • 17.
    Söder, Lennart
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Abildgaard, H.
    Estanqueiro, A.
    Hamon, Camille
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Holttinen, H.
    Lannoye, E.
    Gómez-Lázaro, E.
    O'Malley, M.
    Zimmermann, U.
    Experience and challenges with short-term balancing in European systems with large share of wind power2012In: IEEE Transactions on Sustainable Energy, ISSN 1949-3029, Vol. 3, no 4, 853-861 p.Article in journal (Refereed)
    Abstract [en]

    The amount of wind power in the world is quickly increasing. The background for this development is improved technology, decreased costs for the units, and increased concern regarding environmental problems of competing technologies such as fossil fuels. Some areas are starting to experience very high penetration levels of wind and there have been many instances when wind power has exceeded 50% of the electrical energy production in some balancing areas. The aims of this paper are to show the increased need for balancing, caused by wind power in the minutes to hourly time scale, and to show how this balancing has been performed in some systems when the wind share was higher than 50%. Experience has shown that this is possible, but that there are some challenges that have to be solved as the amount of wind power increases.

1 - 17 of 17
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