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Article

Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin

1
Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea
2
The School of Electrical Engineering, Pusan National University, Pusan 46241, Korea
*
Author to whom correspondence should be addressed.
Energies 2020, 13(20), 5504; https://doi.org/10.3390/en13205504
Received: 31 August 2020 / Revised: 3 October 2020 / Accepted: 16 October 2020 / Published: 20 October 2020
(This article belongs to the Special Issue Planning and Operation of Distributed Energy Resources in Smart Grids)
Due to the recent development of information and communication technology (ICT), various studies using real-time data are now being conducted. The microgrid research field is also evolving to enable intelligent operation of energy management through digitalization. Problems occur when operating the actual microgrid, causing issues such as difficulty in decision making and system abnormalities. Using digital twin technology, which is one of the technologies representing the fourth industrial revolution, it is possible to overcome these problems by changing the microgrid configuration and operating algorithms of virtual space in various ways and testing them in real time. In this study, we proposed an energy storage system (ESS) operation scheduling model to be applied to virtual space when constructing a microgrid using digital twin technology. An ESS optimal charging/discharging scheduling was established to minimize electricity bills and was implemented using supervised learning techniques such as the decision tree, NARX, and MARS models instead of existing optimization techniques. NARX and decision trees are machine learning techniques. MARS is a nonparametric regression model, and its application has been increasing. Its performance was analyzed by deriving performance evaluation indicators for each model. Using the proposed model, it was found in a case study that the amount of electricity bill savings when operating the ESS is greater than that incurred in the actual ESS operation. The suitability of the model was evaluated by a comparative analysis with the optimization-based ESS charging/discharging scheduling pattern. View Full-Text
Keywords: machine learning; digital twin; energy storage system; optimal scheduling; microgrid machine learning; digital twin; energy storage system; optimal scheduling; microgrid
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MDPI and ACS Style

Park, H.-A.; Byeon, G.; Son, W.; Jo, H.-C.; Kim, J.; Kim, S. Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin. Energies 2020, 13, 5504. https://doi.org/10.3390/en13205504

AMA Style

Park H-A, Byeon G, Son W, Jo H-C, Kim J, Kim S. Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin. Energies. 2020; 13(20):5504. https://doi.org/10.3390/en13205504

Chicago/Turabian Style

Park, Hyang-A, Gilsung Byeon, Wanbin Son, Hyung-Chul Jo, Jongyul Kim, and Sungshin Kim. 2020. "Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin" Energies 13, no. 20: 5504. https://doi.org/10.3390/en13205504

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