4 research outputs found

    13th International Conference on Modeling, Optimization and Simulation - MOSIM 2020

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    Comité d’organisation: Université Internationale d’Agadir – Agadir (Maroc) Laboratoire Conception Fabrication Commande – Metz (France)Session RS-1 “Simulation et Optimisation” / “Simulation and Optimization” Session RS-2 “Planification des Besoins Matières Pilotée par la Demande” / ”Demand-Driven Material Requirements Planning” Session RS-3 “Ingénierie de Systèmes Basées sur les Modèles” / “Model-Based System Engineering” Session RS-4 “Recherche Opérationnelle en Gestion de Production” / "Operations Research in Production Management" Session RS-5 "Planification des Matières et des Ressources / Planification de la Production” / “Material and Resource Planning / Production Planning" Session RS-6 “Maintenance Industrielle” / “Industrial Maintenance” Session RS-7 "Etudes de Cas Industriels” / “Industrial Case Studies" Session RS-8 "Données de Masse / Analyse de Données” / “Big Data / Data Analytics" Session RS-9 "Gestion des Systèmes de Transport” / “Transportation System Management" Session RS-10 "Economie Circulaire / Développement Durable" / "Circular Economie / Sustainable Development" Session RS-11 "Conception et Gestion des Chaînes Logistiques” / “Supply Chain Design and Management" Session SP-1 “Intelligence Artificielle & Analyse de Données pour la Production 4.0” / “Artificial Intelligence & Data Analytics in Manufacturing 4.0” Session SP-2 “Gestion des Risques en Logistique” / “Risk Management in Logistics” Session SP-3 “Gestion des Risques et Evaluation de Performance” / “Risk Management and Performance Assessment” Session SP-4 "Indicateurs Clés de Performance 4.0 et Dynamique de Prise de Décision” / ”4.0 Key Performance Indicators and Decision-Making Dynamics" Session SP-5 "Logistique Maritime” / “Marine Logistics" Session SP-6 “Territoire et Logistique : Un Système Complexe” / “Territory and Logistics: A Complex System” Session SP-7 "Nouvelles Avancées et Applications de la Logique Floue en Production Durable et en Logistique” / “Recent Advances and Fuzzy-Logic Applications in Sustainable Manufacturing and Logistics" Session SP-8 “Gestion des Soins de Santé” / ”Health Care Management” Session SP-9 “Ingénierie Organisationnelle et Gestion de la Continuité de Service des Systèmes de Santé dans l’Ere de la Transformation Numérique de la Société” / “Organizational Engineering and Management of Business Continuity of Healthcare Systems in the Era of Numerical Society Transformation” Session SP-10 “Planification et Commande de la Production pour l’Industrie 4.0” / “Production Planning and Control for Industry 4.0” Session SP-11 “Optimisation des Systèmes de Production dans le Contexte 4.0 Utilisant l’Amélioration Continue” / “Production System Optimization in 4.0 Context Using Continuous Improvement” Session SP-12 “Défis pour la Conception des Systèmes de Production Cyber-Physiques” / “Challenges for the Design of Cyber Physical Production Systems” Session SP-13 “Production Avisée et Développement Durable” / “Smart Manufacturing and Sustainable Development” Session SP-14 “L’Humain dans l’Usine du Futur” / “Human in the Factory of the Future” Session SP-15 “Ordonnancement et Prévision de Chaînes Logistiques Résilientes” / “Scheduling and Forecasting for Resilient Supply Chains

    Consumer behavior modeling for electrical energy systems : a complex systems approach

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    Orientador: Alexandre Rasi AokiCoorientador: Germano Lambert-TorresTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 27/02/2019Inclui referências: p. 141-154Resumo: Um sistema complexo é um sistema composto de muitas partes que interagem entre si, de modo que o comportamento coletivo emergente dessas partes é mais do que a soma de seus comportamentos individuais. O sistema elétrico de potência pode ser considerado um sistema complexo devido à sua diversidade de agentes heterogêneos inter-relacionados e a emergência de comportamento complexo. Sistemas de potência estão aumentando em complexidade com novos avanços relacionados à redes elétricas inteligentes tais como tecnologia de informação e comunicação, geração distribuída, veículos elétricos, armazenamento de energia e, especialmente, uma crescente interação e participação de um grande número de consumidores heterogêneos dispersos geograficamente. O sistema elétrico de potência pode ser estudado como um sistema técnico-socioeconômico complexo com múltiplas facetas, e a teoria de sistemas complexos pode fornecer uma base teórica sólida para seus desafios de modelagem e análise. O presente trabalho trata da aplicação da teoria de sistemas complexos em sistemas de potência, focando a análise no consumidor e no seu comportamento relacionado ao consumo de eletricidade, utilizando técnicas do campo da economia comportamental. Comportamentos complexos e emergentes sobre o consumo de eletricidade, bem como seu impacto nas redes elétricas, são analisados através da modelagem do comportamento dos cliente em uma simulação baseada em agentes, considerando quatro categorias de consumidores. A análise da simulação, aplicada a um estudo de caso em uma rede de distribuição de média tensão radial com dados reais, mostrou que premissas ligeiramente diferentes sobre o comportamento do consumidor no nível micro levam a resultados macro muito distintos e com comportamento não linear. Entender e modelar adequadamente o comportamento dos consumidores é de grande importância para o planejamento e operação de redes de energia, e a economia comportamental serve como uma base teórica promissora para modelar o comportamento no consumo de eletricidade. Os resultados deste trabalho mostraram que a teorias de sistemas complexos fornece ferramentas adequadas para lidar com sistemas de potência cada vez mais complexos, considerando-os não mais como um sistema independente agregado, mas como um sistema complexo integrado. Palavras-chave: distribuição de energia; consumo de eletricidade; teoria de sistemas complexos; simulação baseada em agentes; economia comportamental.Abstract: A complex system is a system composed of many interacting parts, such that the collective emergent behavior of those parts is more than the sum of their individual behaviors. Electrical energy systems may be considered a complex system due to its diversity of interrelated heterogeneous agents and emergent complex behavior. Energy systems are increasing in complexity with new advances related to the smart grid such as information and communication technology, distributed generation, electric vehicles, energy storage, and, especially, increasing interaction and participation of a large number of geographically distributed heterogeneous consumers. Power systems can be studied as a complex techno-socio-economical system with multiple facets, and Complex System Theory (CST) may provide a solid theoretical background for these modeling and analysis challenges. The present work deals with the application of CST into electrical energy systems, focusing the analysis on the consumer and their behavior on electricity consumption, using insights from the field of behavioral economics. Emergent complex behaviors on electricity consumption as well as its impact on power grids are analyzed by modeling customer behavior on an agent-based simulation, considering four different consumer categories. The analysis of the simulation, applied on a case study on a radial medium voltage distribution grid with real-world data, showed that slightly different assumptions on consumer behavior at the micro-level lead to very different and non-linear macro outcomes. To properly understand and model consumer behavior is of great importance to the planning and operation of electrical grids, and behavioral economics serves as a promising theoretical background to model behavior on electricity consumption. The results of this work showed that CST provides suitable tools to tackle electrical energy systems' increasing complexity, by considering the electrical power systems not as an aggregated independent system anymore, but as an integrated complex system. Keywords: power distribution; electricity consumption; complex systems theory; agent-based simulation; behavioral economics

    Current challenges and future trends in the field of communication architectures for microgrids

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    [EN] The concept of microgrid has emerged as a feasible answer to cope with the increasing number of distributed renewable energy sources which are being introduced into the electrical grid. The microgrid communication network should guarantee a complete and bidirectional connectivity among the microgrid resources, a high reliability and a feasible interoperability. This is in a contrast to the current electrical grid structure which is characterized by the lack of connectivity, being a centralized-unidirectional system. In this paper a review of the microgrids information and communication technologies (ICT) is shown. In addition, a guideline for the transition from the current communication systems to the future generation of microgrid communications is provided. This paper contains a systematic review of the most suitable communication network topologies, technologies and protocols for smart microgrids. It is concluded that a new generation of peer-to-peer communication systems is required towards a dynamic smart microgrid. Potential future research about communications of the next microgrid generation is also identified.This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Regional Development Fund (ERDF) under Grant ENE2015-64087-C2-2. This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant BES-2013-064539.Marzal-Romeu, S.; Salas-Puente, RA.; González Medina, R.; Garcerá, G.; Figueres Amorós, E. (2018). Current challenges and future trends in the field of communication architectures for microgrids. Renewable and Sustainable Energy Reviews. 82(2):3610-3622. https://doi.org/10.1016/j.rser.2017.10.101S3610362282

    A Novel Locality Algorithm and Peer-to-Peer Communication Infrastructure for Optimizing Network Performance in Smart Microgrids

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    [EN] Peer-to-Peer (P2P) overlay communications networks have emerged as a new paradigm for implementing distributed services in microgrids due to their potential benefits: they are robust, scalable, fault-tolerant, and they can route messages even with a large number of nodes which are frequently entering or leaving from the network. However, current P2P systems have been mainly developed for file sharing or cycle sharing applications where the processes of searching and managing resources are not optimized. Locality algorithms have gained a lot of attention due to their potential to provide an optimized path to groups with similar interests for routing messages in order to get better network performance. This paper develops a fully functional decentralized communication architecture with a new P2P locality algorithm and a specific protocol for monitoring and control of microgrids. Experimental results show that the proposed locality algorithm reduces the number of lookup messages and the lookup delay time. Moreover, the proposed communication architecture heavily depends of the lookup used algorithm as well as the placement of the communication layers within the architecture. Experimental results will show that the proposed techniques meet the network requirements of smart microgrids even with a large number of nodes on stream.This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Regional Development Fund (ERDF) under Grant ENE2015-64087-C2-2R. This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under BES-2013-064539.Marzal-Romeu, S.; González-Medina, R.; Salas-Puente, RA.; Figueres Amorós, E.; Garcerá, G. (2017). A Novel Locality Algorithm and Peer-to-Peer Communication Infrastructure for Optimizing Network Performance in Smart Microgrids. Energies. 10(9):1-25. https://doi.org/10.3390/en10091275S125109Khan, R. H., & Khan, J. Y. (2013). A comprehensive review of the application characteristics and traffic requirements of a smart grid communications network. Computer Networks, 57(3), 825-845. doi:10.1016/j.comnet.2012.11.002Dada, J. O. (2014). Towards understanding the benefits and challenges of Smart/Micro-Grid for electricity supply system in Nigeria. Renewable and Sustainable Energy Reviews, 38, 1003-1014. doi:10.1016/j.rser.2014.07.077Lidula, N. W. A., & Rajapakse, A. D. (2011). Microgrids research: A review of experimental microgrids and test systems. Renewable and Sustainable Energy Reviews, 15(1), 186-202. doi:10.1016/j.rser.2010.09.041Hussain, A., Arif, S. M., Aslam, M., & Shah, S. D. A. (2017). Optimal siting and sizing of tri-generation equipment for developing an autonomous community microgrid considering uncertainties. Sustainable Cities and Society, 32, 318-330. doi:10.1016/j.scs.2017.04.004Dehghanpour, K., Colson, C., & Nehrir, H. (2017). A Survey on Smart Agent-Based Microgrids for Resilient/Self-Healing Grids. Energies, 10(5), 620. doi:10.3390/en10050620Palizban, O., Kauhaniemi, K., & Guerrero, J. M. (2014). Microgrids in active network management – part II: System operation, power quality and protection. Renewable and Sustainable Energy Reviews, 36, 440-451. doi:10.1016/j.rser.2014.04.048Shi, W., Li, N., Chu, C.-C., & Gadh, R. (2017). Real-Time Energy Management in Microgrids. IEEE Transactions on Smart Grid, 8(1), 228-238. doi:10.1109/tsg.2015.2462294Deng, R., Yang, Z., Chow, M.-Y., & Chen, J. (2015). A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches. IEEE Transactions on Industrial Informatics, 11(3), 570-582. doi:10.1109/tii.2015.2414719Moazami Goodarzi, H., & Kazemi, M. (2017). A Novel Optimal Control Method for Islanded Microgrids Based on Droop Control Using the ICA-GA Algorithm. Energies, 10(4), 485. doi:10.3390/en10040485Erol-Kantarci, M., Kantarci, B., & Mouftah, H. (2011). Reliable overlay topology design for the smart microgrid network. IEEE Network, 25(5), 38-43. doi:10.1109/mnet.2011.6033034Hassan Youssef, K. (2016). Optimal management of unbalanced smart microgrids for scheduled and unscheduled multiple transitions between grid-connected and islanded modes. Electric Power Systems Research, 141, 104-113. doi:10.1016/j.epsr.2016.07.015Giotitsas, C., Pazaitis, A., & Kostakis, V. (2015). A peer-to-peer approach to energy production. Technology in Society, 42, 28-38. doi:10.1016/j.techsoc.2015.02.002Kazmi, S. A. A., Shahzad, M. K., Khan, A. Z., & Shin, D. R. (2017). Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective. Energies, 10(4), 501. doi:10.3390/en10040501Werth, A., Andre, A., Kawamoto, D., Morita, T., Tajima, S., Tokoro, M., … Tanaka, K. (2018). Peer-to-Peer Control System for DC Microgrids. IEEE Transactions on Smart Grid, 9(4), 3667-3675. doi:10.1109/tsg.2016.2638462Deconinck, G., Vanthournout, K., Beitollahi, H., Qui, Z., Duan, R., Nauwelaers, B., … Belmans, R. (2008). A Robust Semantic Overlay Network for Microgrid Control Applications. Architecting Dependable Systems V, 101-123. doi:10.1007/978-3-540-85571-2_5Bandara, H. M. N. D., & Jayasumana, A. P. (2012). Collaborative applications over peer-to-peer systems–challenges and solutions. Peer-to-Peer Networking and Applications, 6(3), 257-276. doi:10.1007/s12083-012-0157-3Palizban, O., & Kauhaniemi, K. (2015). Hierarchical control structure in microgrids with distributed generation: Island and grid-connected mode. Renewable and Sustainable Energy Reviews, 44, 797-813. doi:10.1016/j.rser.2015.01.008Khatibzadeh, A., Besmi, M., Mahabadi, A., & Reza Haghifam, M. (2017). Multi-Agent-Based Controller for Voltage Enhancement in AC/DC Hybrid Microgrid Using Energy Storages. Energies, 10(2), 169. doi:10.3390/en10020169Planas, E., Gil-de-Muro, A., Andreu, J., Kortabarria, I., & Martínez de Alegría, I. (2013). General aspects, hierarchical controls and droop methods in microgrids: A review. Renewable and Sustainable Energy Reviews, 17, 147-159. doi:10.1016/j.rser.2012.09.032Olivares, D. E., Mehrizi-Sani, A., Etemadi, A. H., Canizares, C. A., Iravani, R., Kazerani, M., … Hatziargyriou, N. D. (2014). Trends in Microgrid Control. IEEE Transactions on Smart Grid, 5(4), 1905-1919. doi:10.1109/tsg.2013.2295514Vandoorn, T. L., Vasquez, J. C., De Kooning, J., Guerrero, J. M., & Vandevelde, L. (2013). Microgrids: Hierarchical Control and an Overview of the Control and Reserve Management Strategies. IEEE Industrial Electronics Magazine, 7(4), 42-55. doi:10.1109/mie.2013.2279306Zhou, B., Li, W., Chan, K. W., Cao, Y., Kuang, Y., Liu, X., & Wang, X. (2016). Smart home energy management systems: Concept, configurations, and scheduling strategies. Renewable and Sustainable Energy Reviews, 61, 30-40. doi:10.1016/j.rser.2016.03.047Ancillotti, E., Bruno, R., & Conti, M. (2013). The role of communication systems in smart grids: Architectures, technical solutions and research challenges. Computer Communications, 36(17-18), 1665-1697. doi:10.1016/j.comcom.2013.09.004Llaria, A., Terrasson, G., Curea, O., & Jiménez, J. (2016). Application of Wireless Sensor and Actuator Networks to Achieve Intelligent Microgrids: A Promising Approach towards a Global Smart Grid Deployment. Applied Sciences, 6(3), 61. doi:10.3390/app6030061Luna, A. C., Diaz, N. L., Graells, M., Vasquez, J. C., & Guerrero, J. M. (2016). Cooperative energy management for a cluster of households prosumers. IEEE Transactions on Consumer Electronics, 62(3), 235-242. doi:10.1109/tce.2016.7613189Gungor, V. C., Lu, B., & Hancke, G. P. (2010). Opportunities and Challenges of Wireless Sensor Networks in Smart Grid. IEEE Transactions on Industrial Electronics, 57(10), 3557-3564. doi:10.1109/tie.2009.2039455Zhao, C., He, J., Cheng, P., & Chen, J. (2017). Consensus-Based Energy Management in Smart Grid With Transmission Losses and Directed Communication. IEEE Transactions on Smart Grid, 8(5), 2049-2061. doi:10.1109/tsg.2015.2513772Lo, C.-H., & Ansari, N. (2013). Decentralized Controls and Communications for Autonomous Distribution Networks in Smart Grid. IEEE Transactions on Smart Grid, 4(1), 66-77. doi:10.1109/tsg.2012.2228282Li, C., Savaghebi, M., Guerrero, J., Coelho, E., & Vasquez, J. (2016). Operation Cost Minimization of Droop-Controlled AC Microgrids Using Multiagent-Based Distributed Control. Energies, 9(9), 717. doi:10.3390/en9090717Wu, X., Jiang, P., & Lu, J. (2014). Multiagent-Based Distributed Load Shedding for Islanded Microgrids. Energies, 7(9), 6050-6062. doi:10.3390/en7096050Kantamneni, A., Brown, L. E., Parker, G., & Weaver, W. W. (2015). Survey of multi-agent systems for microgrid control. Engineering Applications of Artificial Intelligence, 45, 192-203. doi:10.1016/j.engappai.2015.07.005Lopes, A. L., & Botelho, L. M. (2008). Improving Multi-Agent Based Resource Coordination in Peer-to-Peer Networks. Journal of Networks, 3(2). doi:10.4304/jnw.3.2.38-47Cameron, A., Stumptner, M., Nandagopal, N., Mayer, W., & Mansell, T. (2015). Rule-based peer-to-peer framework for decentralised real-time service oriented architectures. Science of Computer Programming, 97, 202-234. doi:10.1016/j.scico.2014.06.005Zhang, C., Wu, J., Cheng, M., Zhou, Y., & Long, C. (2016). A Bidding System for Peer-to-Peer Energy Trading in a Grid-connected Microgrid. Energy Procedia, 103, 147-152. doi:10.1016/j.egypro.2016.11.264Malatras, A. (2015). State-of-the-art survey on P2P overlay networks in pervasive computing environments. Journal of Network and Computer Applications, 55, 1-23. doi:10.1016/j.jnca.2015.04.014Eng Keong Lua, Crowcroft, J., Pias, M., Sharma, R., & Lim, S. (2005). A survey and comparison of peer-to-peer overlay network schemes. IEEE Communications Surveys & Tutorials, 7(2), 72-93. doi:10.1109/comst.2005.1610546Xu, J., Kumar, A., & Yu, X. (2004). On the Fundamental Tradeoffs Between Routing Table Size and Network Diameter in Peer-to-Peer Networks. IEEE Journal on Selected Areas in Communications, 22(1), 151-163. doi:10.1109/jsac.2003.818805Stoica, I., Morris, R., Karger, D., Kaashoek, M. F., & Balakrishnan, H. (2001). Chord. ACM SIGCOMM Computer Communication Review, 31(4), 149-160. doi:10.1145/964723.383071Rowstron, A., & Druschel, P. (2001). Pastry: Scalable, Decentralized Object Location, and Routing for Large-Scale Peer-to-Peer Systems. Lecture Notes in Computer Science, 329-350. doi:10.1007/3-540-45518-3_18Yuh-Jzer Joung, Li-Wei Yang, & Chien-Tse Fang. (2007). Keyword search in DHT-based peer-to-peer networks. IEEE Journal on Selected Areas in Communications, 25(1), 46-61. doi:10.1109/jsac.2007.070106Stoica, I., Morris, R., Liben-Nowell, D., Karger, D. R., Kaashoek, M. F., Dabek, F., & Balakrishnan, H. (2003). Chord: a scalable peer-to-peer lookup protocol for internet applications. IEEE/ACM Transactions on Networking, 11(1), 17-32. doi:10.1109/tnet.2002.808407Gottron, C., König, A., & Steinmetz, R. (2010). A Survey on Security in Mobile Peer-to-Peer Architectures—Overlay-Based vs. Underlay-Based Approaches. Future Internet, 2(4), 505-532. doi:10.3390/fi2040505Seyedi, Y., Karimi, H., & Guerrero, J. M. (2017). Centralized Disturbance Detection in Smart Microgrids With Noisy and Intermittent Synchrophasor Data. IEEE Transactions on Smart Grid, 8(6), 2775-2783. doi:10.1109/tsg.2016.2539947Youssef, T., Elsayed, A., & Mohammed, O. (2016). Data Distribution Service-Based Interoperability Framework for Smart Grid Testbed Infrastructure. Energies, 9(3), 150. doi:10.3390/en9030150Liu, X., Xia, H., & Chien, A. A. (2004). Validating and Scaling the MicroGrid: A Scientific Instrument for Grid Dynamics. Journal of Grid Computing, 2(2), 141-161. doi:10.1007/s10723-004-4200-3Kansal, P., & Bose, A. (2012). Bandwidth and Latency Requirements for Smart Transmission Grid Applications. IEEE Transactions on Smart Grid, 3(3), 1344-1352. doi:10.1109/tsg.2012.2197229Kuo, M.-T., & Lu, S.-D. (2013). Design and Implementation of Real-Time Intelligent Control and Structure Based on Multi-Agent Systems in Microgrids. Energies, 6(11), 6045-6059. doi:10.3390/en6116045Del Val, E., Rebollo, M., & Botti, V. (2012). Enhancing decentralized service discovery in open service-oriented multi-agent systems. Autonomous Agents and Multi-Agent Systems, 28(1), 1-30. doi:10.1007/s10458-012-9210-0Howell, S., Rezgui, Y., Hippolyte, J.-L., Jayan, B., & Li, H. (2017). Towards the next generation of smart grids: Semantic and holonic multi-agent management of distributed energy resources. Renewable and Sustainable Energy Reviews, 77, 193-214. doi:10.1016/j.rser.2017.03.107Frey, S., Diaconescu, A., Menga, D., & Demeure, I. (2015). A Generic Holonic Control Architecture for Heterogeneous Multiscale and Multiobjective Smart Microgrids. ACM Transactions on Autonomous and Adaptive Systems, 10(2), 1-21. doi:10.1145/2700326Miers, C., Simplicio, M., Gallo, D., Carvalho, T., Bressan, G., Souza, V., … Damola, A. (2010). A Taxonomy for Locality Algorithms on Peer-to-Peer Networks. IEEE Latin America Transactions, 8(4), 323-331. doi:10.1109/tla.2010.5595121Porsinger, T., Janik, P., Leonowicz, Z., & Gono, R. (2017). Modelling and Optimization in Microgrids. Energies, 10(4), 523. doi:10.3390/en10040523Ali, M., Zakariya, M., Asif, M., & Ullah, A. (2012). TCP/IP Based Intelligent Load Management System in Micro-Grids Network Using MATLAB/Simulink. Energy and Power Engineering, 04(04), 283-289. doi:10.4236/epe.2012.44038Shin, I.-J., Song, B.-K., & Eom, D.-S. (2017). International Electronical Committee (IEC) 61850 Mapping with Constrained Application Protocol (CoAP) in Smart Grids Based European Telecommunications Standard Institute Machine-to-Machine (M2M) Environment. Energies, 10(3), 393. doi:10.3390/en10030393Loh, P. C., Li, D., Chai, Y. K., & Blaabjerg, F. (2013). Autonomous Operation of Hybrid Microgrid With AC and DC Subgrids. IEEE Transactions on Power Electronics, 28(5), 2214-2223. doi:10.1109/tpel.2012.2214792Overlay networks for smart gridshttp://users.atlantis.ugent.be/cdvelder/papers/2013/wauters2013sgv.pdfEugster, P. T., Felber, P. A., Guerraoui, R., & Kermarrec, A.-M. (2003). The many faces of publish/subscribe. ACM Computing Surveys, 35(2), 114-131. doi:10.1145/857076.857078Ali, I. (2012). High-speed Peer-to-peer Communication based Protection Scheme Implementation and Testing in Laboratory. International Journal of Computer Applications, 38(4), 16-24. doi:10.5120/4596-6793Yoo, B.-K., Yang, S.-H., Yang, H.-S., Kim, W.-Y., Jeong, Y.-S., Han, B.-M., & Jang, K.-S. (2011). Communication Architecture of the IEC 61850-based Micro Grid System. Journal of Electrical Engineering and Technology, 6(5), 605-612. doi:10.5370/jeet.2011.6.5.605Dou, X., Quan, X., Wu, Z., Hu, M., Yang, K., Yuan, J., & Wang, M. (2014). Hybrid Multi-Agent Control in Microgrids: Framework, Models and Implementations Based on IEC 61850. Energies, 8(1), 31-58. doi:10.3390/en801003
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