IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i14p2722-d248822.html
   My bibliography  Save this article

ERIGrid Holistic Test Description for Validating Cyber-Physical Energy Systems

Author

Listed:
  • Kai Heussen

    (Technical University of Denmark, DK2800 Kgs. Lyngby, Denmark)

  • Cornelius Steinbrink

    (OFFIS—Institute for Information Technology, 26121 Oldenburg, Germany)

  • Ibrahim F. Abdulhadi

    (Institute for Energy and Environment, Electronic and Electrical Engineering Department, University of Strathclyde, Glasgow G1 1XW, UK)

  • Van Hoa Nguyen

    (CEA, LITEN, Department of Solar Technologies INES, University Grenoble Alpes, F-73375 Le Bourget du Lac, France)

  • Merkebu Z. Degefa

    (SINTEF Energi AS, 7034 Trondheim, Norway)

  • Julia Merino

    (Tecnalia Research & Innovation, 48160 Derio, Spain)

  • Tue V. Jensen

    (Technical University of Denmark, DK2800 Kgs. Lyngby, Denmark)

  • Hao Guo

    (Institute for Energy and Environment, Electronic and Electrical Engineering Department, University of Strathclyde, Glasgow G1 1XW, UK)

  • Oliver Gehrke

    (Technical University of Denmark, DK2800 Kgs. Lyngby, Denmark)

  • Daniel Esteban Morales Bondy

    (Technical University of Denmark, DK2800 Kgs. Lyngby, Denmark
    Vestas Wind Systems A/S, DK8200 Aarhus, Denmark)

  • Davood Babazadeh

    (OFFIS—Institute for Information Technology, 26121 Oldenburg, Germany)

  • Filip Pröstl Andrén

    (AIT Austrian Institute for Technology—Electric Energy Systems, Center for Energy, 1210 Vienna, Austria)

  • Thomas I. Strasser

    (AIT Austrian Institute for Technology—Electric Energy Systems, Center for Energy, 1210 Vienna, Austria)

Abstract

Smart energy solutions aim to modify and optimise the operation of existing energy infrastructure. Such cyber-physical technology must be mature before deployment to the actual infrastructure, and competitive solutions will have to be compliant to standards still under development. Achieving this technology readiness and harmonisation requires reproducible experiments and appropriately realistic testing environments. Such testbeds for multi-domain cyber-physical experiments are complex in and of themselves. This work addresses a method for the scoping and design of experiments where both testbed and solution each require detailed expertise. This empirical work first revisited present test description approaches, developed a newdescription method for cyber-physical energy systems testing, and matured it by means of user involvement. The new Holistic Test Description (HTD) method facilitates the conception, deconstruction and reproduction of complex experimental designs in the domains of cyber-physical energy systems. This work develops the background and motivation, offers a guideline and examples to the proposed approach, and summarises experience from three years of its application.

Suggested Citation

  • Kai Heussen & Cornelius Steinbrink & Ibrahim F. Abdulhadi & Van Hoa Nguyen & Merkebu Z. Degefa & Julia Merino & Tue V. Jensen & Hao Guo & Oliver Gehrke & Daniel Esteban Morales Bondy & Davood Babazade, 2019. "ERIGrid Holistic Test Description for Validating Cyber-Physical Energy Systems," Energies, MDPI, vol. 12(14), pages 1-31, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2722-:d:248822
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/14/2722/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/14/2722/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mathias Uslar & Sebastian Rohjans & Christian Neureiter & Filip Pröstl Andrén & Jorge Velasquez & Cornelius Steinbrink & Venizelos Efthymiou & Gianluigi Migliavacca & Seppo Horsmanheimo & Helfried Bru, 2019. "Applying the Smart Grid Architecture Model for Designing and Validating System-of-Systems in the Power and Energy Domain: A European Perspective," Energies, MDPI, vol. 12(2), pages 1-40, January.
    2. Jack P.C. Kleijnen, 2015. "Design and Analysis of Simulation Experiments," International Series in Operations Research and Management Science, Springer, edition 2, number 978-3-319-18087-8, April.
    3. Van Hoa Nguyen & Yvon Besanger & Quoc Tuan Tran & Tung Lam Nguyen, 2017. "On Conceptual Structuration and Coupling Methods of Co-Simulation Frameworks in Cyber-Physical Energy System Validation," Energies, MDPI, vol. 10(12), pages 1-19, November.
    4. Colak, Ilhami & Fulli, Gianluca & Sagiroglu, Seref & Yesilbudak, Mehmet & Covrig, Catalin-Felix, 2015. "Smart grid projects in Europe: Current status, maturity and future scenarios," Applied Energy, Elsevier, vol. 152(C), pages 58-70.
    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. Michael H. Spiegel & Eric M. S. P. Veith & Thomas I. Strasser, 2020. "The Spectrum of Proactive, Resilient Multi-Microgrid Scheduling: A Systematic Literature Review," Energies, MDPI, vol. 13(17), pages 1-37, September.
    2. Steffen Vogel & Ha Thi Nguyen & Marija Stevic & Tue Vissing Jensen & Kai Heussen & Vetrivel Subramaniam Rajkumar & Antonello Monti, 2020. "Distributed Power Hardware-in-the-Loop Testing Using a Grid-Forming Converter as Power Interface," Energies, MDPI, vol. 13(15), pages 1-24, July.

    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. Thomas I. Strasser & Sebastian Rohjans & Graeme M. Burt, 2019. "Methods and Concepts for Designing and Validating Smart Grid Systems," Energies, MDPI, vol. 12(10), pages 1-5, May.
    2. Norouzi, Farshid & Hoppe, Thomas & Elizondo, Laura Ramirez & Bauer, Pavol, 2022. "A review of socio-technical barriers to Smart Microgrid development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    3. Rennen, G. & Husslage, B.G.M. & van Dam, E.R. & den Hertog, D., 2009. "Nested Maximin Latin Hypercube Designs," Discussion Paper 2009-06, Tilburg University, Center for Economic Research.
    4. Ehsan Mehdad & Jack P. C. Kleijnen, 2018. "Efficient global optimisation for black-box simulation via sequential intrinsic Kriging," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(11), pages 1725-1737, November.
    5. Wen Shi & Xi Chen & Jennifer Shang, 2019. "An Efficient Morris Method-Based Framework for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 745-770, October.
    6. Tim Voigt & Martin Kohlhase & Oliver Nelles, 2021. "Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge," Mathematics, MDPI, vol. 9(19), pages 1-26, October.
    7. Markard, Jochen & Erlinghagen, Sabine, 2017. "Technology users and standardization: Game changing strategies in the field of smart meter technology," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 226-235.
    8. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    9. Zhang, Wei & (Ato) Xu, Wangtu, 2017. "Simulation-based robust optimization for the schedule of single-direction bus transit route: The design of experiment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 203-230.
    10. Shi, Wen & Liu, Zhixue & Shang, Jennifer & Cui, Yujia, 2013. "Multi-criteria robust design of a JIT-based cross-docking distribution center for an auto parts supply chain," European Journal of Operational Research, Elsevier, vol. 229(3), pages 695-706.
    11. Alfieri, Arianna & Matta, Andrea, 2012. "Mathematical programming formulations for approximate simulation of multistage production systems," European Journal of Operational Research, Elsevier, vol. 219(3), pages 773-783.
    12. İhsan Yanıkoğlu & Erinç Albey & Serkan Okçuoğlu, 2022. "Robust Parameter Design and Optimization for Quality Engineering," SN Operations Research Forum, Springer, vol. 3(1), pages 1-36, March.
    13. Arianna Alfieri & Andrea Matta & Giulia Pedrielli, 2015. "Mathematical programming models for joint simulation–optimization applied to closed queueing networks," Annals of Operations Research, Springer, vol. 231(1), pages 105-127, August.
    14. Shuo Chen & Falko Ebe & Jeromie Morris & Heiko Lorenz & Christoph Kondzialka & Gerd Heilscher, 2022. "Implementation and Test of an IEC 61850-Based Automation Framework for the Automated Data Model Integration of DES (ADMID) into DSO SCADA," Energies, MDPI, vol. 15(4), pages 1-30, February.
    15. Asaad, Mohammad & Ahmad, Furkan & Alam, Mohammad Saad & Sarfraz, Mohammad, 2021. "Smart grid and Indian experience: A review," Resources Policy, Elsevier, vol. 74(C).
    16. Mehdad, E. & Kleijnen, Jack P.C., 2014. "Classic Kriging versus Kriging with Bootstrapping or Conditional Simulation : Classic Kriging's Robust Confidence Intervals and Optimization (Revised version of CentER DP 2013-038)," Other publications TiSEM 4915047b-afe4-4fc7-8a1c-4, Tilburg University, School of Economics and Management.
    17. Fernando Alarid-Escudero & Richard F. MacLehose & Yadira Peralta & Karen M. Kuntz & Eva A. Enns, 2018. "Nonidentifiability in Model Calibration and Implications for Medical Decision Making," Medical Decision Making, , vol. 38(7), pages 810-821, October.
    18. Dellino, Gabriella & Kleijnen, Jack P.C. & Meloni, Carlo, 2010. "Robust optimization in simulation: Taguchi and Response Surface Methodology," International Journal of Production Economics, Elsevier, vol. 125(1), pages 52-59, May.
    19. Auder, Benjamin & De Crecy, Agnès & Iooss, Bertrand & Marquès, Michel, 2012. "Screening and metamodeling of computer experiments with functional outputs. Application to thermal–hydraulic computations," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 122-131.
    20. Xi Chen & Kyoung-Kuk Kim, 2016. "Efficient VaR and CVaR Measurement via Stochastic Kriging," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 629-644, November.

    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:12:y:2019:i:14:p:2722-:d:248822. 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.