Coordinated Model-Predictive Control for Avoiding Voltage Collapse in an Electric Power Transmission Net

René Boel, Mohammad Moradzadeh, Lieven Vandevelde

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This essay deals with the coordination of the control actions in a network of interacting components, where actuator values for each component are calculated by its local control agent (CA). If the local CAs act independently, then the interaction between different control loops often leads to instability of the network as a whole. Using hierarchical control, including centralized solutions, requires very detailed global model knowledge and may not be robust against communications failures. In this essay, we introduce a coordination paradigm that considers only the hierarchical layer of the CAs. Each local CA implements a model-predictive control (MPC), but neighboring CAs, moreover, exchange their planned control actions in the near future. This information allows each local CA to improve its local anticipation, provided it knows an approximate model of its neighbor. This achieves coordination between the different CAs. We call this approach the coordinating MPC (CMPC) . In order to easily illustrate the advantages of CMPC, we use secondary voltage control in a large-scale multi-area electric power system as a case study. It is known that the electric power system may be destabilized when different neighboring CAs react in an uncoordinated way to incidents that cause the local voltages to temporarily leave their safe sets. In the CMPC approach, each CA sends information on its planned control actions to its neighbors. Simulations for a well-known test system have shown that CMPC significantly increases the size of the set of perturbations that can be tolerated without leading to global instability, as opposed to using anticipation only. In this case study, we use a very simple hybrid model of each area of the electrical power system and consider discrete control actions only. The case study, therefore, provides a good way of introducing CMPC for cyber-physical systems.
Original languageEnglish
Title of host publicationCoordination Control of Distributed Systems
EditorsJan H. van Schuppen, Tiziano Villa
PublisherSpringer Verlag
Pages69-77
Number of pages9
Volume456
ISBN (Electronic)9783319104072
ISBN (Print)9783319104065
DOIs
Publication statusPublished - 8 Oct 2014
Externally publishedYes

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Boel, R., Moradzadeh, M., & Vandevelde, L. (2014). Coordinated Model-Predictive Control for Avoiding Voltage Collapse in an Electric Power Transmission Net. In J. H. van Schuppen, & T. Villa (Eds.), Coordination Control of Distributed Systems (Vol. 456, pp. 69-77). Springer Verlag. Lecture Notes in Control and Information Sciences https://doi.org/10.1007/978-3-319-10407-2_9
Boel, René ; Moradzadeh, Mohammad ; Vandevelde, Lieven. / Coordinated Model-Predictive Control for Avoiding Voltage Collapse in an Electric Power Transmission Net. Coordination Control of Distributed Systems. editor / Jan H. van Schuppen ; Tiziano Villa. Vol. 456 Springer Verlag, 2014. pp. 69-77 (Lecture Notes in Control and Information Sciences).
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Boel, R, Moradzadeh, M & Vandevelde, L 2014, Coordinated Model-Predictive Control for Avoiding Voltage Collapse in an Electric Power Transmission Net. in JH van Schuppen & T Villa (eds), Coordination Control of Distributed Systems. vol. 456, Springer Verlag, Lecture Notes in Control and Information Sciences, pp. 69-77. https://doi.org/10.1007/978-3-319-10407-2_9

Coordinated Model-Predictive Control for Avoiding Voltage Collapse in an Electric Power Transmission Net. / Boel, René; Moradzadeh, Mohammad; Vandevelde, Lieven.

Coordination Control of Distributed Systems. ed. / Jan H. van Schuppen; Tiziano Villa. Vol. 456 Springer Verlag, 2014. p. 69-77 (Lecture Notes in Control and Information Sciences).

Research output: Chapter in Book/Report/Conference proceedingChapter

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N2 - This essay deals with the coordination of the control actions in a network of interacting components, where actuator values for each component are calculated by its local control agent (CA). If the local CAs act independently, then the interaction between different control loops often leads to instability of the network as a whole. Using hierarchical control, including centralized solutions, requires very detailed global model knowledge and may not be robust against communications failures. In this essay, we introduce a coordination paradigm that considers only the hierarchical layer of the CAs. Each local CA implements a model-predictive control (MPC), but neighboring CAs, moreover, exchange their planned control actions in the near future. This information allows each local CA to improve its local anticipation, provided it knows an approximate model of its neighbor. This achieves coordination between the different CAs. We call this approach the coordinating MPC (CMPC) . In order to easily illustrate the advantages of CMPC, we use secondary voltage control in a large-scale multi-area electric power system as a case study. It is known that the electric power system may be destabilized when different neighboring CAs react in an uncoordinated way to incidents that cause the local voltages to temporarily leave their safe sets. In the CMPC approach, each CA sends information on its planned control actions to its neighbors. Simulations for a well-known test system have shown that CMPC significantly increases the size of the set of perturbations that can be tolerated without leading to global instability, as opposed to using anticipation only. In this case study, we use a very simple hybrid model of each area of the electrical power system and consider discrete control actions only. The case study, therefore, provides a good way of introducing CMPC for cyber-physical systems.

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Boel R, Moradzadeh M, Vandevelde L. Coordinated Model-Predictive Control for Avoiding Voltage Collapse in an Electric Power Transmission Net. In van Schuppen JH, Villa T, editors, Coordination Control of Distributed Systems. Vol. 456. Springer Verlag. 2014. p. 69-77. (Lecture Notes in Control and Information Sciences). https://doi.org/10.1007/978-3-319-10407-2_9