IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i2p482-d1313792.html
   My bibliography  Save this article

Digital Twin of Microgrid for Predictive Power Control to Buildings

Author

Listed:
  • Hao Jiang

    (Engineering Cluster, Singapore Institute of Technology, 10 Dover Drive, Singapore 138683, Singapore)

  • Rudy Tjandra

    (Engineering Cluster, Singapore Institute of Technology, 10 Dover Drive, Singapore 138683, Singapore)

  • Chew Beng Soh

    (Engineering Cluster, Singapore Institute of Technology, 10 Dover Drive, Singapore 138683, Singapore)

  • Shuyu Cao

    (Engineering Cluster, Singapore Institute of Technology, 10 Dover Drive, Singapore 138683, Singapore)

  • Donny Cheng Lock Soh

    (Infocomm Cluster, Singapore Institute of Technology, 10 Dover Drive, Singapore 138683, Singapore)

  • Kuan Tak Tan

    (Engineering Cluster, Singapore Institute of Technology, 10 Dover Drive, Singapore 138683, Singapore)

  • King Jet Tseng

    (Engineering Cluster, Singapore Institute of Technology, 10 Dover Drive, Singapore 138683, Singapore)

  • Sivaneasan Bala Krishnan

    (Engineering Cluster, Singapore Institute of Technology, 10 Dover Drive, Singapore 138683, Singapore)

Abstract

The increased focus on sustainability in response to climate change has given rise to many new initiatives to meet the rise in building load demand. The concept of distributed energy resources (DER) and optimal control of supply to meet power demands in buildings have resulted in growing interest to adopt microgrids for a precinct or a university campus. In this paper, a model for an actual physical microgrid has been constructed in OPAL-RT for real-time simulation studies. The load demands for SIT@NYP campus and its weather data are collected to serve as input to run on the digital twin model of DERs of the microgrid. The dynamic response of the microgrid model in response to fluctuations in power generation due to intermittent solar PV generation and load demands are examined via real-time simulation studies and compared with the response of the physical assets. It is observed that the simulation results match closely to the performance of the actual physical asset. As such, the developed microgrid model offers plug-and-play capability, which will allow power providers to better plan for on-site deployment of renewable energy sources and energy storage to match the expected building energy demand.

Suggested Citation

  • Hao Jiang & Rudy Tjandra & Chew Beng Soh & Shuyu Cao & Donny Cheng Lock Soh & Kuan Tak Tan & King Jet Tseng & Sivaneasan Bala Krishnan, 2024. "Digital Twin of Microgrid for Predictive Power Control to Buildings," Sustainability, MDPI, vol. 16(2), pages 1-23, January.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:482-:d:1313792
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/2/482/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/2/482/
    Download Restriction: no
    ---><---

    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:jsusta:v:16:y:2024:i:2:p:482-:d:1313792. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.