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Perninge, M. (2018). A limited-feedback approximation scheme for optimal switching problems with execution delays. Mathematical Methods of Operations Research, 87(3), 347-382
Open this publication in new window or tab >>A limited-feedback approximation scheme for optimal switching problems with execution delays
2018 (English)In: Mathematical Methods of Operations Research, ISSN 1432-2994, E-ISSN 1432-5217, Vol. 87, no 3, p. 347-382Article in journal (Refereed) Published
Abstract [en]

We consider a type of optimal switching problems with non-uniform execution delays and ramping. Such problems frequently occur in the operation of economical and engineering systems. We first provide a solution to the problem by applying a probabilistic method. The main contribution is, however, a scheme for approximating the optimal control by limiting the information in the state-feedback. In a numerical example the approximation routine gives a considerable computational performance enhancement when compared to a conventional algorithm.

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Optimal switching, Impulse control, Real options, Delivery lag, Execution delay, Stopping time, Snell envelope, Numerical algorithm
National Category
Probability Theory and Statistics Control Engineering
Research subject
Mathematics, Applied Mathematics
Identifiers
urn:nbn:se:lnu:diva-68962 (URN)10.1007/s00186-017-0620-2 (DOI)000435382800002 ()
Funder
Swedish Research Council, 2014-03774
Available from: 2017-11-23 Created: 2017-11-23 Last updated: 2018-07-13Bibliographically approved
Perninge, M. & Eriksson, R. (2018). Frequency Control in Power Systems Based on a Regulating Market. IEEE Transactions on Control Systems Technology, 26(1), 27-37
Open this publication in new window or tab >>Frequency Control in Power Systems Based on a Regulating Market
2018 (English)In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 26, no 1, p. 27-37Article in journal (Refereed) Published
Abstract [en]

In power systems, the system frequency is a good indicator of the networks resilience to major disturbances. In many deregulated markets, eg the Nordic power market, the system operator controls the system frequency manually by calling off bids handed in to a market, called the regulating market. In this paper, we formulate the problem of optimal bid call-off on the regulating market that the system operator is faced with each operating period, as an optimal starting problem with delays. As general optimal starting problems with delays are computationally cumbersome, we present two alternative approximation schemes. First, we make simplifications to the problem that renders classical solution concepts tractable; then, in a second approach, we define a suboptimal solution scheme, based on limiting the feedback information.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Delayed reaction, frequency control, optimal switching, power systems, stochastic control
National Category
Control Engineering
Research subject
Physics, Electrotechnology
Identifiers
urn:nbn:se:lnu:diva-61445 (URN)10.1109/TCST.2017.2664723 (DOI)000418095900003 ()
Available from: 2017-03-16 Created: 2017-03-16 Last updated: 2018-01-12Bibliographically approved
Perninge, M. & Eriksson, R. (2017). Optimal Tertiary Frequency Control in Power Systems with Market-Based Regulation. In: IFAC-PapersOnLine: . Paper presented at The 20th World Congress of the International Federation of Automatic Control, 9-14 July 2017, Toulouse, France (pp. 4374-4381). Elsevier, 50
Open this publication in new window or tab >>Optimal Tertiary Frequency Control in Power Systems with Market-Based Regulation
2017 (English)In: IFAC-PapersOnLine, Elsevier, 2017, Vol. 50, p. 4374-4381Conference paper, Published paper (Refereed)
Abstract [en]

The system frequency of a power systems is a good indicator of the networks resilience to major disturbances. In a completely deregulated setting, for example in the Nordic power system, the system operator controls the system frequency manually by calling-off bids handed in to a market, called the regulating market.

In this paper we formulate the problem of optimal bid call-off on the regulating market, that the system operator is faced with each operating period, as an optimal switching problem with execution delays.

As general optimal switching problems with execution delays are computationally cumbersome we resort to a recently developed suboptimal solution scheme, based on limiting the feedback information in the control loop.

Place, publisher, year, edition, pages
Elsevier, 2017
Series
IFAC-PapersOnLine, E-ISSN 2405-8963 ; 50
National Category
Control Engineering
Research subject
Technology (byts ev till Engineering), Mechanical Engineering
Identifiers
urn:nbn:se:lnu:diva-61571 (URN)10.1016/j.ifacol.2017.08.881 (DOI)000423964800224 ()
Conference
The 20th World Congress of the International Federation of Automatic Control, 9-14 July 2017, Toulouse, France
Available from: 2017-03-21 Created: 2017-03-21 Last updated: 2018-04-20Bibliographically approved
Perninge, M. & Eriksson, R. (2016). A stochastic control formulation of the continuous-time power system operation problem. In: 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC): . Paper presented at IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), 7-10 June 2016. IEEE Press
Open this publication in new window or tab >>A stochastic control formulation of the continuous-time power system operation problem
2016 (English)In: 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), IEEE Press, 2016Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we show how to build an economically optimal feedback control strategy for the re-dispatch of electricity generation. We assume that the operator steers production in a set of controllable power plants by altering the active power set-point of each generator, within a set of predefined set-points. Theoperators strategy will be based on balancing the operating cost against the expected cost from unserved demand. Important aspects are ramp-rates at which production can approach the set-point, and switching costs arising from increased fuel consumption during ramping and wear and tear on production facilities.

Place, publisher, year, edition, pages
IEEE Press, 2016
National Category
Control Engineering
Research subject
Physics, Electrotechnology
Identifiers
urn:nbn:se:lnu:diva-61542 (URN)10.1109/EEEIC.2016.7555617 (DOI)000387085800191 ()978-1-5090-2320-2 (ISBN)978-1-5090-2319-6 (ISBN)
Conference
IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), 7-10 June 2016
Available from: 2017-03-21 Created: 2017-03-21 Last updated: 2017-03-23Bibliographically 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.

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Physics, Electrotechnology
Identifiers
urn:nbn:se:lnu:diva-61545 (URN)10.1016/j.epsr.2015.09.016 (DOI)000367126900002 ()
Available from: 2017-03-21 Created: 2017-03-21 Last updated: 2017-11-29Bibliographically approved
Hamon, C., Perninge, M. & Söder, L. (2016). Applying stochastic optimal power flow to power systems with large amounts of wind power nd detailed stability limits. In: 2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid (IREP): . Paper presented at 2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid (IREP), 25-30 Aug. 2013. IEEE Press
Open this publication in new window or tab >>Applying stochastic optimal power flow to power systems with large amounts of wind power nd detailed stability limits
2016 (English)In: 2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid (IREP), IEEE Press, 2016Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE Press, 2016
National Category
Control Engineering
Research subject
Physics, Electrotechnology
Identifiers
urn:nbn:se:lnu:diva-61539 (URN)10.1109/IREP.2013.6629407 (DOI)978-1-4799-0199-9 (ISBN)
Conference
2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid (IREP), 25-30 Aug. 2013
Available from: 2017-03-21 Created: 2017-03-21 Last updated: 2017-03-22Bibliographically 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-basedoperations 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.

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:lnu:diva-61553 (URN)10.1016/j.epsr.2014.09.007 (DOI)
Available from: 2017-03-21 Created: 2017-03-21 Last updated: 2017-11-29Bibliographically 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-basedoperations 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.

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:lnu:diva-61554 (URN)10.1016/j.epsr.2014.09.008 (DOI)
Available from: 2017-03-21 Created: 2017-03-21 Last updated: 2017-11-29Bibliographically approved
Perninge, M. (2015). Approximating the parameter-space stability boundary considering post-contingency corrective controls. Electric power systems research, 121, 313-324
Open this publication in new window or tab >>Approximating the parameter-space stability boundary considering post-contingency corrective controls
2015 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 121, p. 313-324Article in journal (Refereed) Published
Abstract [en]

Lately, much work in the area of voltage stability assessment has been focused on finding post-contingency corrective controls. In this article a contribution to this area will be presented where we investigate the surface of maximal loadability while allowing for post-contingency corrective controls. This objective is different from the usual, where the aim is to include the post-contingency controls in a security-constrained optimal power flow. Our aim is rather to find approximations of the post-contingency stability boundary, in pre-contingencyparameter space, while including the possibility for post-contingency corrective controls. These approximations can then be used in, for example, a chance-constrained optimal power flow routine.

National Category
Control Engineering
Research subject
Physics, Electrotechnology
Identifiers
urn:nbn:se:lnu:diva-61555 (URN)10.1016/j.epsr.2014.11.024 (DOI)
Available from: 2017-03-21 Created: 2017-03-21 Last updated: 2017-11-29Bibliographically 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
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:lnu:diva-61549 (URN)
Conference
13th Wind Integration Workshop,22 - 24 October 2013, London, UK
Available from: 2017-03-21 Created: 2017-03-21 Last updated: 2017-04-05Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3111-4820

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