TY  - JOUR
AU  - Yuan, Jun
AU  - Fang, Zhizhao
AU  - Chen, Rongjun
AU  - Zhou, Jiayu
AU  - Liu, Qun
AU  - Zeng, Xianxian
AU  - Li, Jiawen
AU  - Ren, Jinchang
AU  - Zhao, Huimin
AU  - Yang, Wen
PY  - 2026
DA  - 2026/09/29
TI  - A Digital-Twin-Driven Cyber–Physical Framework for Real-Time Energy Management and Secure Operation of Renewable Energy Systems
JO  - Chinese Journal of Information Fusion
T2  - Chinese Journal of Information Fusion
JF  - Chinese Journal of Information Fusion
VL  - 3
IS  - 3
SP  - 226
EP  - 237
DO  - 10.62762/CJIF.2025.690291
UR  - https://www.icck.org/article/abs/CJIF.2025.690291
KW  - digital twins
KW  - cyber–physical fusion prediction
KW  - real-time management
KW  - secure operation
AB  - Real-time optimal operation and control acts as the intelligent core of renewable energy systems, yet intermittent generation and time-varying loads introduce uncertainties that hinder secure and efficient operation. To address this issue, this paper proposes a digital-twin-driven cyber–physical framework for real-time energy management and secure operation, enabling tight synchronization and closed-loop interaction between physical assets and their digital counterparts. A simplified multi-source data fusion Transformer is developed to improve forecasting accuracy by continuously integrating historical power data and key environmental factors (e.g., temperature, wind speed, and solar radiation). Based on the twin-enabled predictive information and state feedback, a Transformer–Adaptive Dynamic Programming (TM-ADP) method is further proposed for real-time optimal scheduling of grid-connected renewable energy systems. In addition, a YOLOv8-based multi-target detection module is incorporated to enhance operational safety through intelligent visual monitoring of photovoltaic panels. Experiments on a digital twin platform validate that the proposed framework improves operational efficiency, reliability, and safety under uncertain operating conditions.
SN  - 2998-3371
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Yuan2026A,
  author = {Jun Yuan and Zhizhao Fang and Rongjun Chen and Jiayu Zhou and Qun Liu and Xianxian Zeng and Jiawen Li and Jinchang Ren and Huimin Zhao and Wen Yang},
  title = {A Digital-Twin-Driven Cyber–Physical Framework for Real-Time Energy Management and Secure Operation of Renewable Energy Systems},
  journal = {Chinese Journal of Information Fusion},
  year = {2026},
  volume = {3},
  number = {3},
  pages = {226-237},
  doi = {10.62762/CJIF.2025.690291},
  url = {https://www.icck.org/article/abs/CJIF.2025.690291},
  abstract = {Real-time optimal operation and control acts as the intelligent core of renewable energy systems, yet intermittent generation and time-varying loads introduce uncertainties that hinder secure and efficient operation. To address this issue, this paper proposes a digital-twin-driven cyber–physical framework for real-time energy management and secure operation, enabling tight synchronization and closed-loop interaction between physical assets and their digital counterparts. A simplified multi-source data fusion Transformer is developed to improve forecasting accuracy by continuously integrating historical power data and key environmental factors (e.g., temperature, wind speed, and solar radiation). Based on the twin-enabled predictive information and state feedback, a Transformer–Adaptive Dynamic Programming (TM-ADP) method is further proposed for real-time optimal scheduling of grid-connected renewable energy systems. In addition, a YOLOv8-based multi-target detection module is incorporated to enhance operational safety through intelligent visual monitoring of photovoltaic panels. Experiments on a digital twin platform validate that the proposed framework improves operational efficiency, reliability, and safety under uncertain operating conditions.},
  keywords = {digital twins, cyber–physical fusion prediction, real-time management, secure operation},
  issn = {2998-3371},
  publisher = {Institute of Central Computation and Knowledge}
}