EconPapers    
Economics at your fingertips  
 

Big data twin recombination networks for grid low-carbon economic dispatch decision optimization

Chang Liu, Jianfeng Wu, Yu Chen, Jianguo Wang, Tao Wang, Kairui Hu and Jianchao Wu

International Journal of Low-Carbon Technologies, 2025, vol. 20, 384-393

Abstract: To improve the low-carbon economic dispatch, we introduced a big data twin recombination network for grid low-carbon economic dispatch decision optimization. we quantified the energy structure and corrected the linear regression of power loads to boost grid dispatch efficiency, and optimized the correlation between the scheduling of power generation facilities and economic operational strategies by mapping and decomposing, expediting the cyclic relevance of the dispatch decision model. Results demonstrated that our method can optimize decision-making for grid economic dispatch and establish reliable correlation analysis models concerning carbon emissions, grid operational costs, energy utilization efficiency, and power load matching precision.

Keywords: power grid; low carbon; economic dispatch; decision optimization; big data (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1093/ijlct/ctaf014 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:384-393.

Access Statistics for this article

International Journal of Low-Carbon Technologies is currently edited by Saffa B. Riffat

More articles in International Journal of Low-Carbon Technologies from Oxford University Press
Bibliographic data for series maintained by Oxford University Press ().

 
Page updated 2025-04-02
Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:384-393.