TY  - EJOU
AU  - Liang, Xiaowen 
AU  - Sun, Xiaomin 
AU  - Liu, Chunfang 
AU  - Wu, Hongyao 
AU  - Xie, Haonan 
AU  - Zhang, Fengming 
AU  - Zhong, Linpo 
AU  - Wang, Xiaojing 

TI  - A Physico-Data Hybrid Driven Approach to Digital Twin Modeling and Coordinated Optimization of Generation-Grid-Load-Storage Systems
T2  - Energy Engineering

PY  - 
VL  - 
IS  - 
SN  - 1546-0118

AB  - 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.
KW  - Generation-grid-load-storage; physics-data hybrid driving; improved firefly algorithm; digital twin; adaptive LSTM

DO  - 10.32604/ee.2026.079361