@Article{ee.2026.079361,
AUTHOR = {Xiaowen Liang, Xiaomin Sun, Chunfang Liu, Hongyao Wu, Haonan Xie, Fengming Zhang, Linpo Zhong, Xiaojing Wang},
TITLE = {A Physico-Data Hybrid Driven Approach to Digital Twin Modeling and Coordinated Optimization of Generation-Grid-Load-Storage Systems},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/26947},
ISSN = {1546-0118},
ABSTRACT = {As the penetration of renewable energy in power systems continues to increase, traditional simulation and modeling methods encounter challenges, including low accuracy and high computational costs, when addressing multi-timescale dynamics and complex control. To overcome these challenges, a hybrid physics- and data-driven digital twin modeling approach for source-grid-load-storage systems is proposed. The method introduces a two-stage correction model for the digital twin: first, an initial mechanistic model is established based on factory data of the equipment; second, the control system structure and parameters are refined by comparing actual operational data. Additionally, an adaptive long short-term memory network is employed, integrating physical models and virtual data through a bidirectional feedback mechanism. The adaptive mechanism dynamically adjusts model parameters, constructing a hybrid physics- and data-driven digital twin model for the power system’s generation-grid-load-storage framework, thereby mitigating overfitting. Furthermore, an improved firefly algorithm is adopted to solve the cost-minimization collaborative operation problem of generation-grid-load-storage systems, enhancing the utilization rate of renewable energy. Simulation results demonstrate that the proposed method significantly improves the fitting capability and real-time updating performance of the digital twin model. The enhanced algorithm also strengthens the collaborative operation of generation-grid-load-storage systems, exhibiting superior accuracy and robustness compared to traditional methods.},
DOI = {10.32604/ee.2026.079361}
}