Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin
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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-10-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/20/5504 |
| _version_ | 1797550429024813056 |
|---|---|
| author | Hyang-A Park Gilsung Byeon Wanbin Son Hyung-Chul Jo Jongyul Kim Sungshin Kim |
| author_facet | Hyang-A Park Gilsung Byeon Wanbin Son Hyung-Chul Jo Jongyul Kim Sungshin Kim |
| author_sort | Hyang-A Park |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:29:10Z |
| format | Article |
| id | doaj.art-fca1216c7eab463e90d953cf19035272 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T15:29:10Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-fca1216c7eab463e90d953cf190352722023-11-20T17:51:52ZengMDPI AGEnergies1996-10732020-10-011320550410.3390/en13205504Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital TwinHyang-A Park0Gilsung Byeon1Wanbin Son2Hyung-Chul Jo3Jongyul Kim4Sungshin Kim5Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, KoreaDigital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, KoreaDigital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, KoreaDigital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, KoreaDigital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, KoreaThe School of Electrical Engineering, Pusan National University, Pusan 46241, KoreaDue 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.https://www.mdpi.com/1996-1073/13/20/5504machine learningdigital twinenergy storage systemoptimal schedulingmicrogrid |
| spellingShingle | Hyang-A Park Gilsung Byeon Wanbin Son Hyung-Chul Jo Jongyul Kim Sungshin Kim Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin Energies machine learning digital twin energy storage system optimal scheduling microgrid |
| title | Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin |
| title_full | Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin |
| title_fullStr | Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin |
| title_full_unstemmed | Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin |
| title_short | Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin |
| title_sort | digital twin for operation of microgrid optimal scheduling in virtual space of digital twin |
| topic | machine learning digital twin energy storage system optimal scheduling microgrid |
| url | https://www.mdpi.com/1996-1073/13/20/5504 |
| work_keys_str_mv | AT hyangapark digitaltwinforoperationofmicrogridoptimalschedulinginvirtualspaceofdigitaltwin AT gilsungbyeon digitaltwinforoperationofmicrogridoptimalschedulinginvirtualspaceofdigitaltwin AT wanbinson digitaltwinforoperationofmicrogridoptimalschedulinginvirtualspaceofdigitaltwin AT hyungchuljo digitaltwinforoperationofmicrogridoptimalschedulinginvirtualspaceofdigitaltwin AT jongyulkim digitaltwinforoperationofmicrogridoptimalschedulinginvirtualspaceofdigitaltwin AT sungshinkim digitaltwinforoperationofmicrogridoptimalschedulinginvirtualspaceofdigitaltwin |