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

Hyang-A Park, Gilsung Byeon, Wanbin Son, Hyung-Chul Jo, Jongyul Kim and Sungshin Kim
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Hyang-A Park: Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea
Gilsung Byeon: Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea
Wanbin Son: Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea
Hyung-Chul Jo: Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea
Jongyul Kim: Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea
Sungshin Kim: The School of Electrical Engineering, Pusan National University, Pusan 46241, Korea

Energies, 2020, vol. 13, issue 20, 1-15

Abstract: 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.

Keywords: machine learning; digital twin; energy storage system; optimal scheduling; microgrid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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