Abstract

- With the continuous application of the big data technology, "Comprehensive Integration" theory is an approach to management based on Rooted theory, which has been successfully applied to the management of large enterprises and research organizations of all kinds. State Grid Corporation of China in the management process of the construction of a new energy system, with a strong innovative, large-scale investment, many participating units, urgent needs and other characteristics of its management capabilities put forward higher requirements. In this paper, a system integration principle based on the Bayesian probabilistic approach and the big data is proposed to realize the engineering application of the theory of "Comprehensive Integration". By splitting the management system of the new energy system construction, the unit composition of the system can be interpreted to form a complete system and node interaction Bayesian network structure. The algorithm proposed in this paper is implemented in Netica, a commercial statistical analysis software, and its effectiveness is verified, which provides an important theoretical basis for the engineering practice of the theory of "Comprehensive Integration". Finally, the effectiveness of the Dynamic probabilistic network method was quantitatively verified in a lithium battery pack energy storage management example.

Details

Title
"Comprehensive Integration" Method Based on the Bayesian Network and the Big Data Using in the Construction of New Energy System
Author
Chi, Zuowei 1 ; Tan, Bicheng 1 ; Chu, Yunfei 2 ; Wang, Daye 3 

 State Grid Jilin Electric Power Co., LTD, Changchun, Jilin, China 
 State Grid Jilin Electric Power Co., LTD. Economic and technological research institute, Changchun, Jilin, China 
 Jilin Power Trading Center Co., LTD, Changchun, Jilin, China 
Pages
870-877
Publication year
2024
Publication date
2024
Publisher
Engineering and Scientific Research Groups
e-ISSN
11125209
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3074172004
Copyright
© 2024. This work is published under https://creativecommons.org/licenses/by/4.0/legalcode (the“License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.