IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v6y2013i7p3593-3621d27409.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/6/7/3593/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/6/7/3593/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Omar, Noshin & Monem, Mohamed Abdel & Firouz, Yousef & Salminen, Justin & Smekens, Jelle & Hegazy, Omar & Gaulous, Hamid & Mulder, Grietus & Van den Bossche, Peter & Coosemans, Thierry & Van Mierlo, J, 2014. "Lithium iron phosphate based battery – Assessment of the aging parameters and development of cycle life model," Applied Energy, Elsevier, vol. 113(C), pages 1575-1585.
    2. Alexandros Nikolian & Yousef Firouz & Rahul Gopalakrishnan & Jean-Marc Timmermans & Noshin Omar & Peter Van den Bossche & Joeri Van Mierlo, 2016. "Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion," Energies, MDPI, vol. 9(5), pages 1-23, May.
    3. Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, vol. 11(6), pages 1-18, June.
    4. Miroslav Navratil & Andrea Kolkova, 2019. "Decomposition and Forecasting Time Series in the Business Economy Using Prophet Forecasting Model," Central European Business Review, Prague University of Economics and Business, vol. 2019(4), pages 26-39.
    5. Loßner, Martin & Böttger, Diana & Bruckner, Thomas, 2017. "Economic assessment of virtual power plants in the German energy market — A scenario-based and model-supported analysis," Energy Economics, Elsevier, vol. 62(C), pages 125-138.
    6. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    7. repec:hum:wpaper:sfb649dp2014-030 is not listed on IDEAS
    8. Syed Aziz Ur Rehman & Yanpeng Cai & Rizwan Fazal & Gordhan Das Walasai & Nayyar Hussain Mirjat, 2017. "An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan," Energies, MDPI, vol. 10(11), pages 1-23, November.
    9. Joao C. Ferreira & Ana Lucia Martins, 2018. "Building a Community of Users for Open Market Energy," Energies, MDPI, vol. 11(9), pages 1-21, September.
    10. Shixin Song & Feng Xiao & Silun Peng & Chuanxue Song & Yulong Shao, 2018. "A High-Efficiency Bidirectional Active Balance for Electric Vehicle Battery Packs Based on Model Predictive Control," Energies, MDPI, vol. 11(11), pages 1-24, November.
    11. Nyoni, Thabani, 2019. "Modeling and forecasting demand for electricity in Zimbabwe using the Box-Jenkins ARIMA technique," MPRA Paper 96903, University Library of Munich, Germany.
    12. Dutta, Goutam & Mitra, Krishnendranath, 2015. "Dynamic Pricing of Electricity: A Survey of Related Research," IIMA Working Papers WP2015-08-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    13. Lefeng Cheng & Zhiyi Zhang & Haorong Jiang & Tao Yu & Wenrui Wang & Weifeng Xu & Jinxiu Hua, 2018. "Local Energy Management and Optimization: A Novel Energy Universal Service Bus System Based on Energy Internet Technologies," Energies, MDPI, vol. 11(5), pages 1-38, May.
    14. Andrzej Ożadowicz, 2017. "A New Concept of Active Demand Side Management for Energy Efficient Prosumer Microgrids with Smart Building Technologies," Energies, MDPI, vol. 10(11), pages 1-22, November.
    15. Shantanu Pardhi & Sajib Chakraborty & Dai-Duong Tran & Mohamed El Baghdadi & Steven Wilkins & Omar Hegazy, 2022. "A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions," Energies, MDPI, vol. 15(24), pages 1-55, December.
    16. Dileep, G., 2020. "A survey on smart grid technologies and applications," Renewable Energy, Elsevier, vol. 146(C), pages 2589-2625.
    17. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
    18. Mohamed Abdel-Monem & Omar Hegazy & Noshin Omar & Khiem Trad & Sven De Breucker & Peter Van Den Bossche & Joeri Van Mierlo, 2016. "Design and Analysis of Generic Energy Management Strategy for Controlling Second-Life Battery Systems in Stationary Applications," Energies, MDPI, vol. 9(11), pages 1-25, October.
    19. Fan, Shu & Hyndman, Rob J., 2011. "The price elasticity of electricity demand in South Australia," Energy Policy, Elsevier, vol. 39(6), pages 3709-3719, June.
    20. Jing Liu & Yongping Li & Guohe Huang & Cai Suo & Shuo Yin, 2017. "An Interval Fuzzy-Stochastic Chance-Constrained Programming Based Energy-Water Nexus Model for Planning Electric Power Systems," Energies, MDPI, vol. 10(11), pages 1-23, November.
    21. Aniela Kaminska & Andrzej Ożadowicz, 2018. "Lighting Control Including Daylight and Energy Efficiency Improvements Analysis," Energies, MDPI, vol. 11(8), pages 1-18, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:6:y:2013:i:7:p:3593-3621:d:27409. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.