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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.12
- 수록면
- 452 - 457 (6page)
- DOI
- 10.5207/JIEIE.2024.38.6.452
이용수
초록· 키워드
오류제보하기
Digital twin technology creates models that reflect reality in a virtual environment, enabling analysis of both the physical and virtual worlds. It is a technology that allows for the understanding of past and present system operations and the prediction of future states. This study presents a method for modeling four types of facilities using both physics-based and data-driven modeling techniques through digital twins. First, for the battery energy storage system, a physics-based equivalent circuit modeling method was applied to simulate the chemical reactions and electrical relationships of the battery cells through internal parameters. This approach was then expanded to the battery pack level to evaluate the accuracy of the digital twin about the terminal voltage. For the electrolyzers, a physics-based equivalent circuit modeling method was initially used to reflect the electrochemical and thermodynamic characteristics of the electrolysis mechanism. This approach was then expanded from the cell level to the system level, and a data-driven deep neural network model was additionally developed to evaluate the accuracy of the digital twin regarding output voltage. The hydrogen fueling facility was modeled using a physics-based gas equation of state that considers the energy and enthalpy of hydrogen gas. Additionally, a data-driven random forest model was employed to determine the accuracy regarding the internal pressure of the hydrogen tank. Lastly, the electric vehicle charging facility was modeled using a physics-based equivalent circuit model to simulate the nonlinear charging profile, and a data-driven support vector machine model was built to estimate charging demand patterns. The performance of the digital twin modeling for the four facilities was evaluated based on the normalized mean absolute error metric, yielding results of 1.26%, 2.50%, 2.93%, and 5.47%, demonstrating highly favorable outcomes.
목차
Abstract
1. 서론
2. 디지털 트윈 모델링
3. 시뮬레이션 및 검증
4. 결론
References
참고문헌 (10)
참고문헌 신청
F. Tao, H. Zhang, A. Liu, and A.Y.C Nee, “Digital twin in industry: State-of-the-art,” IEEE Transactions on industrial informatics, vol. 15, no. 4, pp. 2405-2415, 2018.
D. Won, “Technology trends on power system digital twin,” KIEE Magazine, vol. 70, no. 6, pp. 9-14, 2021.
G. L. Plett, “Battery management systems, Volume I: Battery modeling,” Artech House, 1st Ed., pp. 29-38, 2015.
W. Pei, et al., “Review of operational control strategy for DC microgrids with electric-hydrogen hybrid storage systems,” CSEE Journal of Power and Energy Systems, vol. 8, no. 2, pp. 329-346, 2022.
F. Gambou, D. Guilbert, M. Zasadzinski, and H. Rafaralahy, “A comprehensive survey of alkaline electrolyzer modeling: electrical domain and specific electrolyte conductivity,” Energies, vol. 15, no. 9, p. 3452, 2022.
D. Won, “Technology trends on power system digital twin,” KIEE Magazine, vol. 70, no. 6, pp. 9-14, 2021.
G. L. Plett, “Battery management systems, Volume I: Battery modeling,” Artech House, 1st Ed., pp. 29-38, 2015.
W. Pei, et al., “Review of operational control strategy for DC microgrids with electric-hydrogen hybrid storage systems,” CSEE Journal of Power and Energy Systems, vol. 8, no. 2, pp. 329-346, 2022.
F. Gambou, D. Guilbert, M. Zasadzinski, and H. Rafaralahy, “A comprehensive survey of alkaline electrolyzer modeling: electrical domain and specific electrolyte conductivity,” Energies, vol. 15, no. 9, p. 3452, 2022.
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