Change search
Refine search result
1 - 21 of 21
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1. Enyioha, Chinwendu
    et al.
    Magnusson, Sindri
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering. KTH - Royal Institute of Technology.
    Heal, Kathryn
    Na, Li
    Carlo, Fischione
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Tarokh, Vahid
    On Variability of Renewable Energy and Online Power Allocation2018In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 33, no 1, p. 451-462Article in journal (Refereed)
    Abstract [en]

    As electric power system operators shift from conventional energy to renewable energy sources, power distribution systems will experience increasing fluctuations in supply. These fluctuations present the need to not only design online decentralized power allocation algorithms, but also characterize how effective they are given fast-changing consumer demand and generation. In this paper, we present an online decentralized dual descent (OD3) power allocation algorithm and determine (in the worst case) how much of observed social welfare can be explained by fluctuations in generation capacity and consumer demand. Convergence properties and performance guarantees of the OD3 algorithm are analyzed by characterizing the difference between the online decision and the optimal decision. We demonstrate validity and accuracy of the theoretical results in the paper through numerical experiments using real power generation data.

  • 2.
    Enyioha, Chinwendu
    et al.
    Harvard University.
    Sindri, Magnusson
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Heal, Kathryn
    Harvard University.
    Li, Na
    Harvard University.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Tarokh, Vahid
    Robustness Analysis for an Online Decentralized Descent Power allocation algorithm2016In: 2016 IEEE Information Theory and Applications Workshop (ITA), IEEE conference proceedings, 2016Conference paper (Refereed)
    Abstract [en]

    As independent service providers increasingly inject power (from renewable sources like wind) into the power distribution system, the power distribution system will likely experience increasingly significant fluctuations in power supply. Fluctuations in power generation, coupled with time-varying consumption of electricity on the demand side and the massive scale of power distribution networks present the need to not only design decentralized power allocation policies, but also understand how robust they are to dynamic demand and supply. In this paper, via an Online Decentralized Dual Descent (OD3) Algorithm, with communication for decentralized coordination, we design power allocation policies in a power distribution system. Based on the OD3 algorithm, we determine and characterize (in the worst case) how much of observed social welfare andprice volatility can be explained by fluctuations in consumption utilities of users and capacities of suppliers. In coordinating the power allocation, the OD3 algoritihm uses a protocol in which the users’ consumption at each time-step depends on the coordinating (price) signal, which is iteratively updated based on aggregate power consumption. Convergence properties and performance guarantees of the OD3 algorithm is analyzed by characterizing the difference between the online decision and the optimal decision. As more renewable energy sources are integrated into the power grid, the results in this paper providea framework to understand how volatility in the power systems propagate to markets. The theoretical results in the paper are validated and illustrated by numerical experiments.

  • 3. Jakobsson, Martin
    et al.
    Magnusson, Sindri
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Weeraddana, Pradeep Chathuranga
    Sri Lankan Institute of Information Technology.
    Extensions of Fast-Lipschitz Optimization2016In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 61, no 4, p. 861-876Article in journal (Refereed)
    Abstract [en]

    The need of fast distributed solvers for optimizationproblems in networked systems has motivated the recent developmentof the Fast-Lipschitz optimization framework. In such an optimization, problems satisfying certain qualifying conditions,such as monotonicity of the objective function and contractivityof the constraints, have a unique optimal solution obtained via fast distributed algorithms that compute the fixed point of the constraints. This paper extends the set of problems for which the Fast-Lipschitz framework applies. Existing assumptions on the problem form are relaxed and new and generalized qualifying conditions are established by novel results based on Lagrangianduality. It is shown for which cases of more constraints thandecision variables, and less constraints than decision variables Fast-Lipschitz optimization applies. New results are obtained by imposing non strict monotonicity of the objective functions. The extended Fast-Lipschitz framework is illustrated by a number ofexamples, including network optimization and optimal control problems.

  • 4.
    Magnusson, Sindri
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering. KTH - Royal Institute of Technology.
    Enyioha, Chinwendu
    Li, Na
    Carlo, Fischione
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Tarokh, Vahid
    Convergence of Limited Communication Gradient Methods2018In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 63, no 5, p. 1356-1371Article in journal (Refereed)
    Abstract [en]

    Distributed optimization increasingly plays a centralrole in economical and sustainable operation of cyber-physicalsystems. Nevertheless, the complete potential of the technologyhas not yet been fully exploited in practice due to communicationlimitations posed by the real-world infrastructures. This workinvestigates fundamental properties of distributed optimizationbased on gradient methods, where gradient information iscommunicated using limited number of bits. In particular, ageneral class of quantized gradient methods are studied wherethe gradient direction is approximated by a finite quantizationset. Sufficient and necessary conditions are provided on sucha quantization set to guarantee that the methods minimize anyconvex objective function with Lipschitz continuous gradient anda nonempty and bounded set of optimizers. A lower bound on thecardinality of the quantization set is provided, along with specificexamples of minimal quantizations. Convergence rate results areestablished that connect the fineness of the quantization andthe number of iterations needed to reach a predefined solutionaccuracy. Generalizations of the results to a relevant class ofconstrained problems using projections are considered. Finally,the results are illustrated by simulations of practical systems.

  • 5.
    Magnusson, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Enyioha, Chinwendu
    Li, Na
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Practical Coding Schemes For Bandwidth Limited One-Way Communication Resource Allocation2016In: 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 221-226, article id 7798273Conference paper (Refereed)
    Abstract [en]

    This paper investigates resource allocation algorithms that use limited communication - where the supplier of a resource broadcasts a coordinating signal using one bit of information to users per iteration. Rather than relay anticipated consumption to the supplier, the users locally compute their allocation, while the supplier measures the total resource consumption. Since the users do not compare their local consumption against the supplier’s capacity at each iteration, they can easily overload the system and cause an outage (for example blackout in power networks). To address this challenge, this paper investigates pragmatic coding schemes, called PFcodes (Primal-Feasible codes), that not only allow the restriction of communication to a single bit of information, but also avoid system overload due to users’ heavy consumption. We derive a worst case lower bound on the number of bits needed to achieve any desired accuracy using PF-codes. In addition, we demonstrate how to construct time-invariant and time-varying PF-codes. We provide an upper bound on the number of bits needed to achieve any desired solution accuracy using time-invariant PF-codes. Remarkably, the difference between the upper and lower bound is only 2 bits. It is proved that the time-varying PF-codes asymptotically converge to the true primal/dual optimal solution. Simulations demonstrating accuracy of our theoretical analyses are presented.

  • 6.
    Magnusson, Sindri
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Enyioha, Chinwendu
    Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA..
    Li, Na
    Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA..
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Tarokh, Vahid
    Duke Univ, Elect & Comp Engn, Durham, NC 27708 USA..
    Communication Complexity of Dual Decomposition Methods for Distributed Resource Allocation Optimization2018In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 12, no 4, p. 717-732Article in journal (Refereed)
    Abstract [en]

    Dual decomposition methods are among the most prominent approaches for finding primal/dual saddle point solutions of resource allocation optimization problems. To deploy these methods in the emerging Internet of things networks, which will often have limited data rates, it is important to understand the communication overhead they require. Motivated by this, we introduce and explore twomeasures of communication complexity of dual decomposition methods to identify the most efficient communication among these algorithms. The first measure is epsilon-complexity, which quantifies the minimal number of bits needed to find an epsilon-accurate solution. The second measure is b-complexity, which quantifies the best possible solution accuracy that can be achieved from communicating b bits. We find the exact epsilon -and b-complexity of a class of resource allocation problems where a single supplier allocates resources to multiple users. For both the primal and dual problems, the epsilon-complexity grows proportionally to log(2) (1/epsilon) and the b-complexity proportionally to 1/2(b). We also introduce a variant of the epsilon- and b-complexity measures where only algorithms that ensure primal feasibility of the iterates are allowed. Such algorithms are often desirable because overuse of the resources can overload the respective systems, e.g., by causing blackouts in power systems. We provide upper and lower bounds on the convergence rate of these primal feasible complexity measures. In particular, we show that the b-complexity cannot converge at a faster rate than O(1/b). Therefore, the results demonstrate a tradeoff between fast convergence and primal feasibility. We illustrate the result by numerical studies.

  • 7.
    Magnusson, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Network and Systems engineering.
    Heal, Kathryn
    Enyioha, Chinwendu
    Li, Na
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Convergence of Limited Communications Gradient Methods2016In: 2016 American Control Conference (ACC), IEEE, 2016, p. 1421-1426Conference paper (Refereed)
    Abstract [en]

    Distributed control and decision making increasingly play a central role in economical and sustainable operation of cyber-physical systems. Nevertheless, the full potential of the technology has not yet been fully exploited in practice due to communication limitations of real-world infrastructures. This work investigates the fundamental properties of gradient methods for distributed optimization, where gradient information is communicated at every iteration, when using limited number of communicated bits. In particular, a general class of quantized gradient methods are studied where the gradient direction is approximated by a finite quantization set. Conditions on the quantization set are provided that are necessary and sufficient to guarantee the ability of these methods to minimize any convex objective function with Lipschitz continuous gradient and a nonempty, bounded set of optimizers. Moreover, a lower bound on the cardinality of the quantization set is provided, along with specific examples of minimal quantizations. Furthermore, convergence rate results are established that connect the fineness of the quantization and number of iterations needed to reach a predefined solution accuracy. The results provide a bound on the number of bits needed to achieve the desired accuracy. Finally, an application of the theory to resource allocation in power networks is demonstrated, and the theoretical results are substantiated by numerical simulations.

  • 8.
    Magnusson, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Heal, Kathryn
    Enyioha, Chinwendu
    Li, Na
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Tarokh, Vahie
    Convergence of Limited Communications Gradient MethodsManuscript (preprint) (Other academic)
  • 9.
    Magnusson, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Heal, Kathryn
    Enyioha, Chinwendu
    Li, Na
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Tarokh, Vahie
    Distributed Resource Allocation Using One-Way Communication with Applications to Power NetworksManuscript (preprint) (Other academic)
    Abstract [en]

    Nonconvex and structured optimization problemsarise in many engineering applications that demand scalableand distributed solution methods. The study of the convergenceproperties of these methods is in general difficult due to thenonconvexity of the problem. In this paper, two distributedsolution methods that combine the fast convergence propertiesof augmented Lagrangian-based methods with the separabilityproperties of alternating optimization are investigated. The firstmethod is adapted from the classic quadratic penalty functionmethod and is called the Alternating Direction Penalty Method(ADPM). Unlike the original quadratic penalty function method,in which single-step optimizations are adopted, ADPM uses analternating optimization, which in turn makes it scalable. Thesecond method is the well-known Alternating Direction Methodof Multipliers (ADMM). It is shown that ADPM for nonconvexproblems asymptotically converges to a primal feasible pointunder mild conditions and an additional condition ensuringthat it asymptotically reaches the standard first order necessary conditions for local optimality are introduced. In thecase of the ADMM, novel sufficient conditions under whichthe algorithm asymptotically reaches the standard first ordernecessary conditions are established. Based on this, completeconvergence of ADMM for a class of low dimensional problemsare characterized. Finally, the results are illustrated by applyingADPM and ADMM to a nonconvex localization problem inwireless sensor networks.

  • 10.
    Magnusson, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Weeraddana, Pradeep Chathuranga
    Rabbat, Michael
    McGill University.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    On the Convergence of Alternating Direction Lagrangian Methods for Nonconvex Structured Optimization Problems2015In: IEEE Transactions on Control of Network Systems, ISSN 2325-5870, Vol. 3, no 3, p. 296-309, article id 7239586Article in journal (Refereed)
    Abstract [en]

    Nonconvex and structured optimization problemsarise in many engineering applications that demand scalableand distributed solution methods. The study of the convergenceproperties of these methods is in general difficult due to thenonconvexity of the problem. In this paper, two distributedsolution methods that combine the fast convergence propertiesof augmented Lagrangian-based methods with the separabilityproperties of alternating optimization are investigated. The firstmethod is adapted from the classic quadratic penalty functionmethod and is called the Alternating Direction Penalty Method(ADPM). Unlike the original quadratic penalty function method,in which single-step optimizations are adopted, ADPM uses analternating optimization, which in turn makes it scalable. Thesecond method is the well-known Alternating Direction Methodof Multipliers (ADMM). It is shown that ADPM for nonconvexproblems asymptotically converges to a primal feasible pointunder mild conditions and an additional condition ensuringthat it asymptotically reaches the standard first order necessary conditions for local optimality are introduced. In thecase of the ADMM, novel sufficient conditions under whichthe algorithm asymptotically reaches the standard first ordernecessary conditions are established. Based on this, completeconvergence of ADMM for a class of low dimensional problemsare characterized. Finally, the results are illustrated by applyingADPM and ADMM to a nonconvex localization problem inwireless sensor networks.

  • 11.
    Magnússon, Sindri
    KTH, School of Electrical Engineering (EES), Network and Systems engineering.
    Bandwidth Limited Distributed Optimization with Applications to Networked Cyberphysical Systems2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The emerging technology of Cyberphysical systems consists of networked computing, sensing, and actuator devices used to monitor, connect, and control physical phenomena. In order to economically and sustainably operate Cyberphysical systems, their devices need to cooperate over a communication network to solve optimization problems. For example, in smart power grids, smart meters cooperatively optimize the grid performance, and in wireless sensor networks a number of sensors cooperate to find optimal estimators of real-world parameters. A challenging aspect in the design of distributed solution algorithms to these optimization problems is that while the technology advances and the networks grow larger, the communication bandwidth available to coordinate the solution remains limited. Motivated by this challenge, this thesis investigates the convergence of distributed solution methods for resource allocation optimization problems, where gradient information is communicated at every iteration, using limited communication. This problem is approached from three different perspectives, each presented in a separate paper.  The investigation of the three papers demonstrate promises and limits of solving distributed resource allocation problems using limited communication bandwidth. Future work will consider how even more general problems can be solved using limited communication bandwidth and also study different communication constraints.

  • 12.
    Magnússon, Sindri
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Distributed Optimization with Nonconvexities and Limited Communication2016Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    In economical and sustainable operation of cyber-physical systems, a number of entities need to often cooperate over a communication network to solve optimization problems. A challenging aspect in the design of robust distributed solution algorithms to these optimization problems is that as technology advances and the networks grow larger, the communication bandwidth used to coordinate the solution is limited. Moreover, even though most research has focused distributed convex optimization, in cyberphysical systems nonconvex problems are often encountered, e.g., localization in wireless sensor networks and optimal power flow in smart grids, the solution of which poses major technical difficulties. Motivated by these challenges this thesis investigates distributed optimization with emphasis on limited communication for both convex and nonconvex structured problems. In particular, the thesis consists of four articles as summarized below.

    The first two papers investigate the convergence of distributed gradient solution methods for the resource allocation optimization problem, where gradient information is communicated at every iteration, using limited communication. In particular, the first paper investigates how distributed dual descent methods can perform demand-response in power networks by using one-way communication. To achieve the one-way communication, the power supplier first broadcasts a coordination signal to the users and then updates the coordination signal by using physical measurements related to the aggregated power usage. Since the users do not communicate back to the supplier, but instead they only take a measurable action, it is essential that the algorithm remains primal feasible at every iteration to avoid blackouts. The paper demonstrates how such blackouts can be avoided by appropriately choosing the algorithm parameters. Moreover, the convergence rate of the algorithm is investigated. The second paper builds on the work of the first paper and considers more general resource allocation problem with multiple resources. In particular, a general class of quantized gradient methods are studied where the gradient direction is approximated by a finite quantization set. Necessary and sufficient conditions on the quantization set are provided to guarantee the ability of these methods to solve a large class of dual problems. A lower bound on the cardinality of the quantization set is provided, along with specific examples of minimal quantizations. Furthermore, convergence rate results are established that connect the fineness of the quantization and number of iterations needed to reach a predefined solution accuracy. The results provide a bound on the number of bits needed to achieve the desired accuracy of the optimal solution.

    The third paper investigates a particular nonconvex resource allocation problem, the Optimal Power Flow (OPF) problem, which is of central importance in the operation of power networks. An efficient novel method to address the general nonconvex OPF problem is investigated, which is based on the Alternating Direction Method of Multipliers (ADMM) combined with sequential convex approximations. The global OPF problem is decomposed into smaller problems associated to each bus of the network, the solutions of which are coordinated via a light communication protocol. Therefore, the proposed method is highly scalable. The convergence properties of the proposed algorithm are mathematically and numerically substantiated. The fourth paper builds on the third paper and investigates the convergence of distributed algorithms as in the third paper but for more general nonconvex optimization problems. In particular, two distributed solution methods, including ADMM, that combine the fast convergence properties of augmented Lagrangian-based methods with the separability properties of alternating optimization are investigated. The convergence properties of these methods are investigated and sufficient conditions under which the algorithms asymptotically reache the first order necessary conditions for optimality are established. Finally, the results are numerically illustrated on a nonconvex localization problem in wireless sensor networks.

    The results of this thesis advocate the promising convergence behaviour of some distributed optimization algorithms on nonconvex problems. Moreover, the results demonstrate the potential of solving convex distributed resource allocation problems using very limited communication bandwidth. Future work will consider how even more general convex and nonconvex problems can be solved using limited communication bandwidth and also study lower bounds on the bandwidth needed to solve general resource allocation optimization problems.

  • 13.
    Magnússon, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chathuranga Weeraddana, Pradeep
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    A Distributed Approach for the Optimal Power Flow Problem Based on ADMM and Sequential Convex Approximations2015In: IEEE Transactions on Control of Network Systems, ISSN 2325-5870, Vol. 2, no 3, p. 238-253Article in journal (Refereed)
    Abstract [en]

    The optimal power flow (OPF) problem, which playsa central role in operating electrical networks is considered. Theproblem is nonconvex and is in fact NP hard. Therefore, designingefficient algorithms of practical relevance is crucial, thoughtheir global optimality is not guaranteed. Existing semi-definiteprogramming relaxation based approaches are restricted to OPFproblems where zero duality holds. In this paper, an efficientnovel method to address the general nonconvex OPF problemis investigated. The proposed method is based on alternatingdirection method of multipliers combined with sequential convexapproximations. The global OPF problem is decomposed intosmaller problems associated to each bus of the network, thesolutions of which are coordinated via a light communicationprotocol. Therefore, the proposed method is highly scalable. Theconvergence properties of the proposed algorithm are mathematicallysubstantiated. Finally, the proposed algorithm is evaluatedon a number of test examples, where the convergence propertiesof the proposed algorithm are numerically substantiated and theperformance is compared with a global optimal method.

  • 14.
    Magnússon, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Network and Systems engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chinwendu, Enyioha
    Li, Na
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Network and Systems engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Tarokh, Vahid
    Communication Complexity of Resource Allocation OptimizationManuscript (preprint) (Other academic)
  • 15.
    Magnússon, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Enyioha, Chinwendu
    Harvard University.
    Hea, Kathryn
    Li, Na
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Tarokh, Vahid
    Distributed Resource Allocation Using One-Way Communication with Applications to Power Networks2016In: 2016 50th Annual Conference on Information Systems and Sciences, CISS 2016, IEEE conference proceedings, 2016, p. 631-636, article id 7460576Conference paper (Refereed)
    Abstract [en]

    Typical coordination schemes for future powergrids require two-way communications. Since the number of end power-consuming devices is large, the bandwidth requirements for such two-way communication schemes may be prohibitive. Motivated by this observation, we study distributed coordination schemes that require only one-way limited communications. In particular, we investigate how dual descent distributed optimization algorithm can be employed in power networks using one-way communication. In this iterative algorithm, system coordinators broadcast coordinating (or pricing) signals to the users/devices who update power consumption based on the received signal. Then system coordinators update the coordinating signals based on the physical measurement of the aggregate power usage. We provide conditions to guarantee the feasibility of the aggregated power usage at each iteration so as to avoid blackout. Furthermore, we prove the convergence of algorithms under these conditions, and establish its rate of convergence. We illustrate the performance of our algorithms using numerical simulations. These results show that one-way limited communication may be viable for coordinating/operating the future smart grids.

  • 16.
    Magnússon, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Network and Systems engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Enyioha, Chinwendu
    Li, Na
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Network and Systems engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Tarokh, Vahid
    Convergence of Limited Communications Gradient MethodsArticle in journal (Other academic)
    Abstract [en]

    Distributed optimization increasingly plays a centralrole in economical and sustainable operation of cyber-physicalsystems. Nevertheless, the complete potential of the technologyhas not yet been fully exploited in practice due to communicationlimitations posed by the real-world infrastructures. This workinvestigates fundamental properties of distributed optimizationbased on gradient methods, where gradient information iscommunicated using limited number of bits. In particular, ageneral class of quantized gradient methods are studied wherethe gradient direction is approximated by a finite quantizationset. Sufficient and necessary conditions are provided on sucha quantization set to guarantee that the methods minimize anyconvex objective function with Lipschitz continuous gradient anda nonempty and bounded set of optimizers. A lower bound on thecardinality of the quantization set is provided, along with specificexamples of minimal quantizations. Convergence rate results areestablished that connect the fineness of the quantization andthe number of iterations needed to reach a predefined solutionaccuracy. Generalizations of the results to a relevant class ofconstrained problems using projections are considered. Finally,the results are illustrated by simulations of practical systems.

  • 17.
    Magnússon, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Network and Systems engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Network and Systems engineering.
    Li, N.
    Voltage Control Using Limited Communication2017In: IFAC-PapersOnLine, ISSN 2405-8963, Vol. 50, no 1, p. 1-6Article in journal (Refereed)
    Abstract [en]

    In electricity distribution networks, the increasing penetration of renewable energy generation necessitates faster and more sophisticated voltage controls. Unfortunately, recent research shows that local voltage control fails in achieving the desired regulation, unless there is some communication between the controllers. However, the communication infrastructure for distribution systems are less reliable and less ubiquitous as compared to that for the bulk transmission system. In this paper, we design distributed voltage control that use limited communication. That is, only neighboring buses need to communicate few bits between each other before each control step. We investigate how these controllers can achieve the desired asymptotic behavior of the voltage regulation and we provide upper bounds on the number of bits that are needed to ensure a predefined accuracy of the regulation. Finally, we illustrate the results by numerical simulations.

  • 18.
    Magnússon, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Network and Systems engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jakobsson, Martin
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Network and Systems engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Weeraddana, Pradeep Chathuranga
    On some extensions of Fast-Lipschitz optimization2016In: 2016 European Control Conference (ECC), IEEE, 2016, p. 172-177, article id 7810282Conference paper (Refereed)
    Abstract [en]

    The need of fast distributed solvers for optimization problems in networked systems has motivated the recent development of the Fast-Lipschitz optimization framework. In such an optimization, problems satisfying certain qualifying conditions, such as monotonicity of the objective function and contractivity of the constraints, have a unique optimal solution obtained via fast distributed algorithms that compute the fixed point of the constraints. This paper extends the set of problems for which the Fast-Lipschitz framework applies. Existing assumptions on the problem form are relaxed and new qualifying conditions are established. It is shown for which cases of more constraints than decision variables, and less constraints than decision variables Fast-Lipschitz optimization applies. New results are obtained by imposing non strict monotonicity of the objective functions. The extended Fast-Lipschitz framework is illustrated by a number of examples, including network optimization and optimal control problems

  • 19.
    Magnússon, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Network and Systems engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Li, Na
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Network and Systems engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Voltage Control Using Limited CommunicationManuscript (preprint) (Other academic)
  • 20.
    Magnússon, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Network and Systems engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Weeraddana, Pradeep Chathuranga
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Network and Systems engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    A distributed approach for the optimal power flow problem2016In: 2016 European Control Conference (ECC), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1940-1945, article id 7810575Conference paper (Refereed)
    Abstract [en]

    For operating electrical power networks, the Optimal Power Flow (OPF) problem plays a central role. The problem is nonconvex and NP hard. Therefore, designing efficient solution algorithms is crucial, though their global optimality is not guaranteed. Existing semi-definite programming relaxation based approaches are restricted to OPF problems for which zero duality holds, whereas for non-convex problems there is a lack of solution methods of provable performance. In this paper, an efficient novel method to address the general nonconvex OPF problem is investigated. The proposed method is based on alternating direction method of multipliers combined with sequential convex approximations. The global OPF problem is decomposed into smaller problems associated to each bus of the network, the solutions of which are coordinated via a light communication protocol. Therefore, the proposed method is highly scalable. The convergence properties of the proposed algorithm are mathematically and numerically substantiated.

  • 21.
    Magnússon, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Weeraddana, Pradeep Chathuranga
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Rabbat, Michael
    McGill University, Department of Electrical and Computer Engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    On the Convergence of an Alternating Direction Penalty Method for Nonconvex Problems2015In: Signals, Systems and Computers, 2015 Asilomar Conference on, IEEE Computer Society, 2015Conference paper (Other academic)
    Abstract [en]

    This paper investigates convergence properties ofscalable algorithms for nonconvex and structured optimization.We consider a method that is adapted from the classic quadraticpenalty function method, the Alternating Direction PenaltyMethod (ADPM). Unlike the original quadratic penalty functionmethod, in which single-step optimizations are adopted, ADPMuses alternating optimization, which in turn is exploited toenable scalability of the algorithm. A special case of ADPM isa variant of the well known Alternating Direction Method ofMultipliers (ADMM), where the penalty parameter is increasedto infinity. We show that due to the increasing penalty, theADPM asymptotically reaches a primal feasible point undermild conditions. Moreover, we give numerical evidence thatdemonstrates the potential of the ADPM for computing localoptimal points when the penalty is not updated too aggressively.

1 - 21 of 21
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf