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Sustainability
Volume 16
Issue 2
10.3390/su16020482
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Article

Digital Twin of Microgrid for Predictive Power Control to Buildings

by
Hao Jiang
1,
Rudy Tjandra
1,
Chew Beng Soh
1,*,
Shuyu Cao
1,
Donny Cheng Lock Soh
2,
Kuan Tak Tan
1,
King Jet Tseng
1 and
Sivaneasan Bala Krishnan
1
1
Engineering Cluster, Singapore Institute of Technology, 10 Dover Drive, Singapore 138683, Singapore
2
Infocomm Cluster, Singapore Institute of Technology, 10 Dover Drive, Singapore 138683, Singapore
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(2), 482; https://doi.org/10.3390/su16020482
Submission received: 3 December 2023 / Revised: 28 December 2023 / Accepted: 2 January 2024 / Published: 5 January 2024
(This article belongs to the Special Issue Digital Twinning of Energy and Thermal Systems for Urban Sustainability)
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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.
Keywords: Matlab/Simulink; load demands; microgrid; DER; OPAL-RT; digital twin; energy optimization; Gurobi; sustainable building Matlab/Simulink; load demands; microgrid; DER; OPAL-RT; digital twin; energy optimization; Gurobi; sustainable building

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MDPI and ACS Style

Jiang, H.; Tjandra, R.; Soh, C.B.; Cao, S.; Soh, D.C.L.; Tan, K.T.; Tseng, K.J.; Krishnan, S.B. Digital Twin of Microgrid for Predictive Power Control to Buildings. Sustainability 2024, 16, 482. https://doi.org/10.3390/su16020482

AMA Style

Jiang H, Tjandra R, Soh CB, Cao S, Soh DCL, Tan KT, Tseng KJ, Krishnan SB. Digital Twin of Microgrid for Predictive Power Control to Buildings. Sustainability. 2024; 16(2):482. https://doi.org/10.3390/su16020482

Chicago/Turabian Style

Jiang, Hao, Rudy Tjandra, Chew Beng Soh, Shuyu Cao, Donny Cheng Lock Soh, Kuan Tak Tan, King Jet Tseng, and Sivaneasan Bala Krishnan. 2024. "Digital Twin of Microgrid for Predictive Power Control to Buildings" Sustainability 16, no. 2: 482. https://doi.org/10.3390/su16020482

APA Style

Jiang, H., Tjandra, R., Soh, C. B., Cao, S., Soh, D. C. L., Tan, K. T., Tseng, K. J., & Krishnan, S. B. (2024). Digital Twin of Microgrid for Predictive Power Control to Buildings. Sustainability, 16(2), 482. https://doi.org/10.3390/su16020482

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