Digital Twin-Based Modeling Platform Development for Complex Regulation Rules of Large Power Grids

被引:0
|
作者
Shi, Hui [1 ]
Zhu, Shu [1 ]
Zhang, Siyuan [1 ]
Wang, Qisheng [1 ]
Song, Jian [1 ]
机构
[1] State Grid Hunan Power Co Ltd, Elect Power Dispatching & Control Ctr, Changsha, Peoples R China
关键词
digital twin-based modeling; complex regulation rules; decision model and notation; decision table; friendly enough expression language;
D O I
10.1109/IAEAC54830.2022.9929895
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the rapid growth of new energy and cross-region interconnected power grids in China, the requirements for accuracy and real-time control of power grid operation are increasing, and the safety constraints are often associated with the data of multiple operation modes. Based on the demand for automatic matching, monitoring and decision making of complex regulation rules, this paper proposes a digital modeling platform for complex regulation rules of power grids, which can be used to transform various regulation rules into a common Decision Model and Notation (DMN) model for dispatch operation monitoring and safety checking modules. The logical description of rules is established using Friendly Enough Expression Language (FEEL) based definition of parameters and variables, simuModel object based common calculation formulas for values of parameters and variables, BOX expression based logical decision table, FEEL based description of operation modes, etc. The application shows that the platform is rich in modeling language, flexible tools, high degree of standardization, and the model comes with multi-level logical decision function, which is well adapted to the monitoring and calibration platform, and provides a solution for the digitization of complex regulation rules.
引用
收藏
页码:1059 / 1063
页数:5
相关论文
共 50 条
  • [1] Digital Twin-Based Modeling of Complex Systems for Smart Aging
    Deng, Yiyi
    [J]. DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2022, 2022
  • [2] Digital twin-based testing process management for large and complex equipment components
    Liu, Zhen
    Zhang, Qinglei
    Duan, Jianguo
    Liu, Dong
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2022, 121 (5-6): : 3143 - 3161
  • [3] Digital Twin-Based Energy Modeling of Industrial Robots
    Yan, Ke
    Xu, Wenjun
    Yao, Bitao
    Zhou, Zude
    Duc Truong Pham
    [J]. METHODS AND APPLICATIONS FOR MODELING AND SIMULATION OF COMPLEX SYSTEMS, 2018, 946 : 333 - 348
  • [4] A digital twin-based multidisciplinary collaborative design approach for complex engineering product development
    Wu, Youde
    Zhou, Linzhen
    Zheng, Pai
    Sun, Yanqing
    Zhang, Kaikai
    [J]. ADVANCED ENGINEERING INFORMATICS, 2022, 52
  • [5] A Reconfigurable Modeling Approach for Digital Twin-based Manufacturing System
    Zhang, Chenyuan
    Xu, Wenjun
    Liu, Jiayi
    Liu, Zhihao
    Zhou, Zude
    Duc Truong Pham
    [J]. 11TH CIRP CONFERENCE ON INDUSTRIAL PRODUCT-SERVICE SYSTEMS, 2019, 83 : 118 - 125
  • [6] Digital Twin-Based Blockchain for Power Support in Networked Microgrids
    Hong, Ying-Yi
    Alano, Francisco I.
    Lee, Yih-der
    Jiang, Jheng-Lun
    Yeh, Jin-Nan
    [J]. IEEE ACCESS, 2024, 12 : 86675 - 86689
  • [7] Digital twin-based lifetime estimation of SiC power modules
    Mathew, Anu
    Rzepka, Sven
    Heimler, Patrick
    Xie, Dong
    Alaluss, Mohamed
    Basler, Thomas
    [J]. 2024 36TH INTERNATIONAL SYMPOSIUM ON POWER SEMICONDUCTOR DEVICES AND IC S, ISPSD 2024, 2024, : 478 - 481
  • [8] DIGITWISE: Digital Twin-based Modeling of Adaptive Video Streaming Engagement
    Artioli, Emanuele
    Tashtarian, Farzad
    Timmerer, Christian
    [J]. PROCEEDINGS OF THE 2024 15TH ACM MULTIMEDIA SYSTEMS CONFERENCE 2024, MMSYS 2024, 2024, : 78 - 88
  • [9] Digital twin-based virtual modeling of the Poyang Lake wetland landscapes
    Chen, Hao
    Xiao, Xin
    Chen, Chao
    Chen, Min
    Li, Chaoyang
    Lu, Kai
    Lin, Hui
    Fang, Chaoyang
    [J]. ENVIRONMENTAL MODELLING & SOFTWARE, 2024, 181
  • [10] Digital twin-based fault detection for intelligent power production lines
    Zhou, You
    Qian, Xuefeng
    Xu, Dan
    Zhao, Can
    Qian, Kejun
    [J]. INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING, 2024, 27 (04)