Evaluation of cloud-based real-time simulation of smart grids
Ilustraciones
- Autores:
-
Noreña Monsalve, Juan Pablo
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/84185
- Palabra clave:
- 620 - Ingeniería y operaciones afines::621 - Física aplicada
Distribución de energía eléctrica
Real-time simulation
Smart grids
Cloud tecnologies
Simulación en tiempo real
Tecnologías en la nube
Computación en la nube
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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Evaluation of cloud-based real-time simulation of smart grids |
| dc.title.translated.eng.fl_str_mv |
Evaluación de simulación en tiempo real de redes inteligentes en la nube |
| title |
Evaluation of cloud-based real-time simulation of smart grids |
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Evaluation of cloud-based real-time simulation of smart grids 620 - Ingeniería y operaciones afines::621 - Física aplicada Distribución de energía eléctrica Real-time simulation Smart grids Cloud tecnologies Simulación en tiempo real Tecnologías en la nube Computación en la nube |
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Evaluation of cloud-based real-time simulation of smart grids |
| title_full |
Evaluation of cloud-based real-time simulation of smart grids |
| title_fullStr |
Evaluation of cloud-based real-time simulation of smart grids |
| title_full_unstemmed |
Evaluation of cloud-based real-time simulation of smart grids |
| title_sort |
Evaluation of cloud-based real-time simulation of smart grids |
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Noreña Monsalve, Juan Pablo |
| dc.contributor.advisor.none.fl_str_mv |
Pérez González, Ernesto Mirz, Markus |
| dc.contributor.author.none.fl_str_mv |
Noreña Monsalve, Juan Pablo |
| dc.contributor.researchgroup.spa.fl_str_mv |
Programa de Investigacion sobre Adquisicion y Analisis de Señales Paas-Un Grupo de Automática de la Universidad Nacional Gaunal |
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0000-0003-1488-0507 |
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NOREÑA MONSALVE, JUAN PABLO |
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Juan-Norena-Monsalve |
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ujyRPFMAAAAJ |
| dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines::621 - Física aplicada |
| topic |
620 - Ingeniería y operaciones afines::621 - Física aplicada Distribución de energía eléctrica Real-time simulation Smart grids Cloud tecnologies Simulación en tiempo real Tecnologías en la nube Computación en la nube |
| dc.subject.lemb.spa.fl_str_mv |
Distribución de energía eléctrica |
| dc.subject.proposal.eng.fl_str_mv |
Real-time simulation Smart grids Cloud tecnologies |
| dc.subject.proposal.spa.fl_str_mv |
Simulación en tiempo real Tecnologías en la nube |
| dc.subject.wikidata.spa.fl_str_mv |
Computación en la nube |
| description |
Ilustraciones |
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2022 |
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2022-10-26 |
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2023-07-17T16:45:39Z |
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2023-07-17T16:45:39Z |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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Universidad Nacional de Colombia |
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eng |
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LaReferencia |
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[1] Maria Luisa Di Silvestre, Salvatore Favuzza, Eleonora Riva Sanseverino, and Gaetano Zizzo. How decarbonization, digitalization and decentralization are changing key power infrastructures. Renewable and Sustainable Energy Reviews, 93:483–498, 2018. [2] Dongdong Zhang, Chunjiao Li, Hui Hwang Goh, Tanveer Ahmad, Hongyu Zhu, Hui Liu, and Thomas Wu. A comprehensive overview of modeling approaches and optimal control strategies for cyber-physical resilience in power systems. Renewable Energy, 189:1383–1406, 2022. [3] A. Benigni, T. Strasser, G. De Carne, M. Liserre, M. Cupelli, and A. Monti. Real-Time Simulation-Based Testing of Modern Energy Systems: A Review and Discussion. IEEE Industrial Electronics Magazine, 14(2):28–39, 2020. [4] C. Gavriluta, C. Boudinet, F. Kupzog, A. Gomez-Exposito, and R. Caire. Cyber- physical framework for emulating distributed control systems in smart grids. Interna- tional Journal of Electrical Power and Energy Systems, 114, 2020. [5] K. Heussen, C. Steinbrink, I.F. Abdulhadi, N. Van Hoa, M.Z. Degefa, J. Merino, T.V. Jensen, H. Guo, O. Gehrke, D.E.M. Bondy, F.P. Andrén, and T.I. Strasser. Erigrid holistic test description for validating cyber-physical energy systems. Energies, 12(14), 2019. [6] Dan C Marinescu. Chapter 8 - Cloud Hardware and Software. In Dan C Marinescu, editor, Cloud Computing (Second Edition), pages 281–319. Morgan Kaufmann, second edition edition, 2018. [7] C. Hauser, E. Litvinov, X. Luo, Q. Zhang, D. Anderson, T. Gkountouvas, M. Meng, K. Birman, and A. Bose. Gridcloud: Infrastructure for cloud-based wide area monitoring of bulk electric power grids. IEEE Transactions on Smart Grid, 10(2):2170–2179, 2019. [8] E. Harmon, U. Ozgur, M.H. Cintuglu, R. De Azevedo, K. Akkaya, and O.A. Moham- med. The Internet of Microgrids: A Cloud-Based Framework for Wide Area Networked Microgrids. IEEE Transactions on Industrial Informatics, 14(3):1262–1274, 2018. [9] M. Pau, E. Patti, L. Barbierato, A. Estebsari, E. Pons, F. Ponci, and A. Monti. A cloud-based smart metering infrastructure for distribution grid services and automation. Sustainable Energy, Grids and Networks, 15:14–25, 2018. [10] N.D. Popovi ́c, D.S. Popovíc, and I. Seskar. A novel cloud-based advanced distribution management system solution. IEEE Transactions on Industrial Informatics, 14(8):3469– 3476, 2018. [11] Markus Mirz, Steffen Vogel, Georg Reinke, and Antonello Monti. SoftwareX DPsim — A dynamic phasor real-time simulator for power systems. SoftwareX, 10:100253, 2019. [12] Markus Mirz, Steffen Vogel, Bettina Schafer, and Antonello Monti. Distributed real- time co-simulation as a service. In 2018 IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), pages 534–539. IEEE, jan 2018. [13] Federico Reghenzani, Giuseppe Massari, and William Fornaciari. The real-time linux kernel: A survey on PreemptRT.CMComputingSurveys, 52(1), 2019. [14] Lu Jian-feng, Wang Chun-yi, and Hu Jie. A High Performance Data Storage Method for Embedded Linux Real-time Database in Power Systems. Energy Procedia, 16:883–888, 2012. [15] G Bruzzone, M Caccia, G Ravera, and A Bertone. Standard Linux for embedded real- time robotics and manufacturing control systems. Robotics and Computer-Integrated Manufacturing, 25(1):178–190, 2009. [16] Yorick De Bock, Siegfried Mercelis, Jan Broeckhove, and Peter Hellinckx. Real-time virtualization with Xvisor. Internet of Things, 11:100238, 2020. [17] Tomasz Kloda, Marco Solieri, Renato Mancuso, Nicola Capodieci, Paolo Valente, and Marko Bertogna. Deterministic memory hierarchy and virtualization for modern multi- core embedded systems. Proceedings of the IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS, 2019-April:1–14, 2019. [18] Luca Abeni and Dario Faggioli. Using Xen and KVM as real-time hypervisors. Journal of Systems Architecture, 106(January), 2020. [19] Vaclav Struhar, Moris Behnam, Mohammad Ashjaei, and Alessandro V. Papadopoulos. Real-time containers: A survey. OpenAccess Series in Informatics, 80(7):1–7, 2020. [20] CEN/CENELEC/ETSI Joint Working Group on Standards for Smart Grids. CEN- CENELEC-ETSI Smart Grid Coordination Group: Smart Grid Information Security. (November):1–107, 2012. [21] X. Luo, S. Zhang, and E. Litvinov. Practical design and implementation of cloud computing for power system planning studies. IEEE Transactions on Smart Grid, 10(2):2301–2311, 2019. [22] Deepak Kumar Panda and Saptarshi Das. Smart grid architecture model for control, optimization and data analytics of future power networks with more renewable energy. Journal of Cleaner Production, 301:126877, 2021. [23] Mathias Uslar, Sebastian Rohjans, Christian Neureiter, Filip Pro ̈stl Andr ́en, Jorge Ve- lasquez, Cornelius Steinbrink, Venizelos Efthymiou, Gianluigi Migliavacca, Seppo Hors- manheimo, Helfried Brunner, and Thomas I. Strasser. Applying the smart grid archi- tecture model for designing and validating system-of-systems in the power and energy domain: A European perspective. Energies, 12(2), 2019. [24] Kai Strunz, Christian Dufour, Vahid Jalili-Marandi, Xiaoyu Wang, Thomas Strasser, Venkata Dinavahi, Panos Kotsampopoulos, Maryam Saeedifard, David Shearer, Paul Forsyth, Mario Paolone, Georg Lauss, M. D. Omar Faruque, Antonello Monti, and Juan A. Martinez. Real-Time Simulation Technologies for Power Systems Design, Tes- ting, and Analysis. IEEE Power and Energy Technology Systems Journal, 2(2):63–73, 2015. [25] Markus Mirz, Jan Dinkelbach, and Antonello Monti. DPsim—Advancements in po- wer electronics modelling using shifted frequency analysis and in real-time simulation capability by parallelization. Energies, 13(15), 2020. [26] Markus Mirz. Dynamic Phasor Real-Time Simulation-Based Digital Twin for Power Systems. [27] Marco Pagani, Alessandro Biondi, Mauro Marinoni, Lorenzo Molinari, Giuseppe Lipari, and Giorgio Buttazzo. A Linux-based support for developing real-time applications on heterogeneous platforms with dynamic FPGA reconfiguration. Future Generation Computer Systems, 129:125–140, 2022. [28] Manuel F Dolz, Francisco D Igual, Thomas Ludwig, Luis Pin ̃uel, and Enrique S Quintana-Ort ́ı. Balancing task- and data-level parallelism to improve performance and energy consumption of matrix computations on the Intel Xeon Phi. Computers Electrical Engineering, 46:95–111, 2015. [30] Samuel Williams, Andrew Waterman, and David Patterson. Roofline: An insightful model for Performance Visual multicore Architectures conVentional. Communications of the ACM, 52(4):65–76, 2009. [31] Dong Zhong, Qinglei Cao, George Bosilca, and Jack Dongarra. Using Advanced Vector Extensions AVX-512 for MPI Reductions. PervasiveHealth: Pervasive Computing |
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Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Pérez González, Ernesto9e6927ede4a872bcf9337317b10efe9a600Mirz, Markuscc431d23bf57ff7e5c6d873ac3097437600Noreña Monsalve, Juan Pablo2107a10350617aae48306d34ac19d196Programa de Investigacion sobre Adquisicion y Analisis de Señales Paas-UnGrupo de Automática de la Universidad Nacional Gaunal0000-0003-1488-0507NOREÑA MONSALVE, JUAN PABLO57213686941Juan-Norena-MonsalveujyRPFMAAAAJ2023-07-17T16:45:39Z2023-07-17T16:45:39Z2022-10-26https://repositorio.unal.edu.co/handle/unal/84185Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/IlustracionesPower systems are suffering profound transformations in the transition toward modern energy systems, facing significant challenges regarding the decentralization, decarbonization, and digitalization of power grids. The literature generally presents approaches requiring optimal coordination between the power grid and Information and Communication Technologies. The research of these solutions requires realistic testing environments, which is why Digital Real-Time Simulators have gained popularity in power system laboratories. Furthermore, cloud computing has enabled a cost-efficient alternative for the sector’s digitalization. However, cloud technologies are not used for real-time constrained workloads traditionally. This thesis evaluates a proposed cloud infrastructure as a computational tool to serve digital twins modeling power systems. To this end, this thesis implements a private cloud based on Kubernetes. It sets a laboratory for cloud-native applications interacting with a cloud-based real-time simulator based on an open-source real-time simulation framework. Additionally, it presents an application model that other researchers can replicate to test with the simulation loop, their software-based solutions for the automation of power systems. The results presented in this thesis evidence the capabilities and limitations of abstracting physical compute resources to execute real-time workloads in the context of digital twins for smart grids. Although the evaluation is addressed as a soft-constrained real-time application for a testing environment, this work opens a discussion to address firm-constrained real-time applications in production-grade environments.Los sistemas eléctricos están sufriendo transformaciones profundas como parte del proceso del transición a sistemas modernos de energía. La denominada transición energética enfrenta grandes retos de cara a la descentralización, descarbonización, digitalización y democratización de los sistemas eléctricos. Para afrontarlos, la industria y la academia han propuesto soluciones que en general requieren de una coordinación optima entre la red eléctrica y los sistemas de tecnologías de la información y comunicaciones. La investigación de dichas soluciones depende de ambientes realistas de pruebas, por eso los simuladores digitales de tiempo real se han vuelto muy populares en los laboratorios. Por otra parte, la computación en la nube se ha vuelto un habilitador tecnológico para el sector eléctrico. Sin embargo, las tecnologías de nube no suelen ser utilizadas para cargas de trabajo con restricciones de tiempo real. Esta tesis tiene como objetivo evaluar una infraestructura de nube propuesta, como herramienta de computo para servir gemelos digitales de sistemas de potencia. En esta tesis se implementó una nube privada basada en un cluster de Kubernetes, que sirve como laboratorio para aplicaciones nativas en nube que interactuan con el lazo de simulación de un framework de simulación en tiempo real de código abierto. Adicionalmente, se presenta una modelo de aplicación que podría ser replicado para probar soluciones basadas en software orientadas a la automatización de sistemas de potencia. Los resultados presentados en esta tesis evidencian las capacidades y las limitaciones de la abstracción de recursos computacionales para la ejecución de cargas de trabajo con restricciones de tiempo real, enfocado en los gemelos digitales de sistemas de potencia. Todos las evaluaciones se desarrollaron en un ambiente de laboratorio donde se podría considerar que las restricciones de tiempo real son suaves, pero se abre una discusión a la posibilidad de llevarlo a ambientes productivos con restricciones firmes. (texto tomado de la fuente)MaestríaMagíster en Ingeniería - Ingeniería EléctricaAutomatización de Sistemas de PotenciaÁrea Curricular de Ingeniería Eléctrica e Ingeniería de Control67 páginasapplication/pdfengUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Ingeniería EléctricaFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín620 - Ingeniería y operaciones afines::621 - Física aplicadaDistribución de energía eléctricaReal-time simulationSmart gridsCloud tecnologiesSimulación en tiempo realTecnologías en la nubeComputación en la nubeEvaluation of cloud-based real-time simulation of smart gridsEvaluación de simulación en tiempo real de redes inteligentes en la nubeTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMLaReferencia[1] Maria Luisa Di Silvestre, Salvatore Favuzza, Eleonora Riva Sanseverino, and Gaetano Zizzo. How decarbonization, digitalization and decentralization are changing key power infrastructures. Renewable and Sustainable Energy Reviews, 93:483–498, 2018.[2] Dongdong Zhang, Chunjiao Li, Hui Hwang Goh, Tanveer Ahmad, Hongyu Zhu, Hui Liu, and Thomas Wu. A comprehensive overview of modeling approaches and optimal control strategies for cyber-physical resilience in power systems. Renewable Energy, 189:1383–1406, 2022.[3] A. Benigni, T. Strasser, G. De Carne, M. Liserre, M. Cupelli, and A. Monti. Real-Time Simulation-Based Testing of Modern Energy Systems: A Review and Discussion. IEEE Industrial Electronics Magazine, 14(2):28–39, 2020.[4] C. Gavriluta, C. Boudinet, F. Kupzog, A. Gomez-Exposito, and R. Caire. Cyber- physical framework for emulating distributed control systems in smart grids. Interna- tional Journal of Electrical Power and Energy Systems, 114, 2020.[5] K. Heussen, C. Steinbrink, I.F. Abdulhadi, N. Van Hoa, M.Z. Degefa, J. Merino, T.V. Jensen, H. Guo, O. Gehrke, D.E.M. Bondy, F.P. Andrén, and T.I. Strasser. Erigrid holistic test description for validating cyber-physical energy systems. Energies, 12(14), 2019.[6] Dan C Marinescu. Chapter 8 - Cloud Hardware and Software. In Dan C Marinescu, editor, Cloud Computing (Second Edition), pages 281–319. Morgan Kaufmann, second edition edition, 2018.[7] C. Hauser, E. Litvinov, X. Luo, Q. Zhang, D. Anderson, T. Gkountouvas, M. Meng, K. Birman, and A. Bose. Gridcloud: Infrastructure for cloud-based wide area monitoring of bulk electric power grids. IEEE Transactions on Smart Grid, 10(2):2170–2179, 2019.[8] E. Harmon, U. Ozgur, M.H. Cintuglu, R. De Azevedo, K. Akkaya, and O.A. Moham- med. The Internet of Microgrids: A Cloud-Based Framework for Wide Area Networked Microgrids. IEEE Transactions on Industrial Informatics, 14(3):1262–1274, 2018.[9] M. Pau, E. Patti, L. Barbierato, A. Estebsari, E. Pons, F. Ponci, and A. Monti. A cloud-based smart metering infrastructure for distribution grid services and automation. Sustainable Energy, Grids and Networks, 15:14–25, 2018.[10] N.D. Popovi ́c, D.S. Popovíc, and I. Seskar. A novel cloud-based advanced distribution management system solution. IEEE Transactions on Industrial Informatics, 14(8):3469– 3476, 2018.[11] Markus Mirz, Steffen Vogel, Georg Reinke, and Antonello Monti. SoftwareX DPsim — A dynamic phasor real-time simulator for power systems. SoftwareX, 10:100253, 2019.[12] Markus Mirz, Steffen Vogel, Bettina Schafer, and Antonello Monti. Distributed real- time co-simulation as a service. In 2018 IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), pages 534–539. IEEE, jan 2018.[13] Federico Reghenzani, Giuseppe Massari, and William Fornaciari. The real-time linux kernel: A survey on PreemptRT.CMComputingSurveys, 52(1), 2019.[14] Lu Jian-feng, Wang Chun-yi, and Hu Jie. A High Performance Data Storage Method for Embedded Linux Real-time Database in Power Systems. Energy Procedia, 16:883–888, 2012.[15] G Bruzzone, M Caccia, G Ravera, and A Bertone. Standard Linux for embedded real- time robotics and manufacturing control systems. Robotics and Computer-Integrated Manufacturing, 25(1):178–190, 2009.[16] Yorick De Bock, Siegfried Mercelis, Jan Broeckhove, and Peter Hellinckx. Real-time virtualization with Xvisor. Internet of Things, 11:100238, 2020.[17] Tomasz Kloda, Marco Solieri, Renato Mancuso, Nicola Capodieci, Paolo Valente, and Marko Bertogna. Deterministic memory hierarchy and virtualization for modern multi- core embedded systems. Proceedings of the IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS, 2019-April:1–14, 2019.[18] Luca Abeni and Dario Faggioli. Using Xen and KVM as real-time hypervisors. Journal of Systems Architecture, 106(January), 2020.[19] Vaclav Struhar, Moris Behnam, Mohammad Ashjaei, and Alessandro V. Papadopoulos. Real-time containers: A survey. OpenAccess Series in Informatics, 80(7):1–7, 2020.[20] CEN/CENELEC/ETSI Joint Working Group on Standards for Smart Grids. CEN- CENELEC-ETSI Smart Grid Coordination Group: Smart Grid Information Security. (November):1–107, 2012.[21] X. Luo, S. Zhang, and E. Litvinov. Practical design and implementation of cloud computing for power system planning studies. IEEE Transactions on Smart Grid, 10(2):2301–2311, 2019.[22] Deepak Kumar Panda and Saptarshi Das. Smart grid architecture model for control, optimization and data analytics of future power networks with more renewable energy. Journal of Cleaner Production, 301:126877, 2021.[23] Mathias Uslar, Sebastian Rohjans, Christian Neureiter, Filip Pro ̈stl Andr ́en, Jorge Ve- lasquez, Cornelius Steinbrink, Venizelos Efthymiou, Gianluigi Migliavacca, Seppo Hors- manheimo, Helfried Brunner, and Thomas I. Strasser. Applying the smart grid archi- tecture model for designing and validating system-of-systems in the power and energy domain: A European perspective. Energies, 12(2), 2019.[24] Kai Strunz, Christian Dufour, Vahid Jalili-Marandi, Xiaoyu Wang, Thomas Strasser, Venkata Dinavahi, Panos Kotsampopoulos, Maryam Saeedifard, David Shearer, Paul Forsyth, Mario Paolone, Georg Lauss, M. D. Omar Faruque, Antonello Monti, and Juan A. Martinez. Real-Time Simulation Technologies for Power Systems Design, Tes- ting, and Analysis. IEEE Power and Energy Technology Systems Journal, 2(2):63–73, 2015.[25] Markus Mirz, Jan Dinkelbach, and Antonello Monti. DPsim—Advancements in po- wer electronics modelling using shifted frequency analysis and in real-time simulation capability by parallelization. Energies, 13(15), 2020.[26] Markus Mirz. Dynamic Phasor Real-Time Simulation-Based Digital Twin for Power Systems. [27] Marco Pagani, Alessandro Biondi, Mauro Marinoni, Lorenzo Molinari, Giuseppe Lipari, and Giorgio Buttazzo. A Linux-based support for developing real-time applications on heterogeneous platforms with dynamic FPGA reconfiguration. Future Generation Computer Systems, 129:125–140, 2022.[28] Manuel F Dolz, Francisco D Igual, Thomas Ludwig, Luis Pin ̃uel, and Enrique S Quintana-Ort ́ı. Balancing task- and data-level parallelism to improve performance and energy consumption of matrix computations on the Intel Xeon Phi. Computers Electrical Engineering, 46:95–111, 2015.[30] Samuel Williams, Andrew Waterman, and David Patterson. Roofline: An insightful model for Performance Visual multicore Architectures conVentional. Communications of the ACM, 52(4):65–76, 2009.[31] Dong Zhong, Qinglei Cao, George Bosilca, and Jack Dongarra. Using Advanced Vector Extensions AVX-512 for MPI Reductions. 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