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Middleware Architectures for the Smart Grid: Survey and Challenges in the Foreseeable Future

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  • José-Fernán Martínez

    (Research Center on Software Technologies and Multimedia Systems for Sustainability (CITSEM—Centro de Investigación en Tecnologías Software y Sistemas Multimedia para la Sostenibilidad), Campus Sur UPM, Ctra Valencia, Km 7, 28031 Madrid, Spain)

  • Jesús Rodríguez-Molina

    (Research Center on Software Technologies and Multimedia Systems for Sustainability (CITSEM—Centro de Investigación en Tecnologías Software y Sistemas Multimedia para la Sostenibilidad), Campus Sur UPM, Ctra Valencia, Km 7, 28031 Madrid, Spain)

  • Pedro Castillejo

    (Research Center on Software Technologies and Multimedia Systems for Sustainability (CITSEM—Centro de Investigación en Tecnologías Software y Sistemas Multimedia para la Sostenibilidad), Campus Sur UPM, Ctra Valencia, Km 7, 28031 Madrid, Spain)

  • Rubén De Diego

    (Research Center on Software Technologies and Multimedia Systems for Sustainability (CITSEM—Centro de Investigación en Tecnologías Software y Sistemas Multimedia para la Sostenibilidad), Campus Sur UPM, Ctra Valencia, Km 7, 28031 Madrid, Spain)

Abstract

The traditional power grid is just a one-way supplier that gets no feedback data about the energy delivered, what tariffs could be the most suitable ones for customers, the shifting daily needs of electricity in a facility, etc. Therefore, it is only natural that efforts are being invested in improving power grid behavior and turning it into a Smart Grid. However, to this end, several components have to be either upgraded or created from scratch. Among the new components required, middleware appears as a critical one, for it will abstract all the diversity of the used devices for power transmission (smart meters, embedded systems, etc. ) and will provide the application layer with a homogeneous interface involving power production and consumption management data that were not able to be provided before. Additionally, middleware is expected to guarantee that updates to the current metering infrastructure (changes in service or hardware availability) or any added legacy measuring appliance will get acknowledged for any future request. Finally, semantic features are of major importance to tackle scalability and interoperability issues. A survey on the most prominent middleware architectures for Smart Grids is presented in this paper, along with an evaluation of their features and their strong points and weaknesses.

Suggested Citation

  • José-Fernán Martínez & Jesús Rodríguez-Molina & Pedro Castillejo & Rubén De Diego, 2013. "Middleware Architectures for the Smart Grid: Survey and Challenges in the Foreseeable Future," Energies, MDPI, vol. 6(7), pages 1-29, July.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:7:p:3593-3621:d:27409
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    References listed on IDEAS

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    1. Rob J Hyndman & Shu Fan, 2008. "Density forecasting for long-term peak electricity demand," Monash Econometrics and Business Statistics Working Papers 6/08, Monash University, Department of Econometrics and Business Statistics.
    2. Rosario Miceli, 2013. "Energy Management and Smart Grids," Energies, MDPI, vol. 6(4), pages 1-29, April.
    3. Noshin Omar & Mohamed Daowd & Omar Hegazy & Grietus Mulder & Jean-Marc Timmermans & Thierry Coosemans & Peter Van den Bossche & Joeri Van Mierlo, 2012. "Standardization Work for BEV and HEV Applications: Critical Appraisal of Recent Traction Battery Documents," Energies, MDPI, vol. 5(1), pages 1-19, January.
    4. Alberto Sendin & Iñigo Berganza & Aitor Arzuaga & Xabier Osorio & Iker Urrutia & Pablo Angueira, 2013. "Enhanced Operation of Electricity Distribution Grids Through Smart Metering PLC Network Monitoring, Analysis and Grid Conditioning," Energies, MDPI, vol. 6(1), pages 1-18, January.
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    Cited by:

    1. Jesús Rodríguez-Molina & José-Fernán Martínez & Pedro Castillejo & Gregorio Rubio, 2017. "Development of Middleware Applied to Microgrids by Means of an Open Source Enterprise Service Bus," Energies, MDPI, vol. 10(2), pages 1-50, February.
    2. Hao Liang & Weihua Zhuang, 2014. "Stochastic Modeling and Optimization in a Microgrid: A Survey," Energies, MDPI, vol. 7(4), pages 1-24, March.
    3. Tarek A. Youssef & Ahmed T. Elsayed & Osama A. Mohammed, 2016. "Data Distribution Service-Based Interoperability Framework for Smart Grid Testbed Infrastructure," Energies, MDPI, vol. 9(3), pages 1-22, March.

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