@article{Zhou2024, 
author = {Yong-wang Zhou and Zheng-tong Cai and Can-cheng Xu and Qiang Ni},
title = {Probabilistic Multi-energy Flow Calculation and Analysis for Electricity-heating-gas Integrated Energy System Based on Data-driven},
year = {2024},
journal = {Journal of Guangdong University of Technology},
volume = {41},
number = {5},
pages = {1-12},
keywords = {integrated energy system, compressor working modes, uncertainty quantification, probabilistic multi-energy flow, data-driven},
url = {https://www.sciopen.com/article/10.12052/gdutxb.240084},
doi = {10.12052/gdutxb.240084},
abstract = {In order to quantify the uncertainty of multi-energy flow distribution in the integrated energy system, a probabilistic multi-energy flow calculation method of integrated energy system based on data-driven is proposed. Firstly, a unified multi-energy flow calculation model suitable for different working modes of compressors in integrated energy system is established, and the impact of different operating modes of compressors on the multi-energy flow distribution is also discussed. Secondly, a probabilistic multi-energy flow calculation method based on support vector regression is developed. The method first constructs a data set by calculating deterministic multi-energy flow repeatedly, and then the support vector regression is used to mine the nonlinear mapping relationship between known loads, network node information and unknown node parameters in the integrated energy system. Finally, through case analysis, it is verified that the proposed unified multi-energy flow model can be applied to different compressor working conditions. By comparing with traditional probabilistic multi-energy flow calculation methods, it is shown that the proposed data-driven probabilistic multi-energy flow calculation method has higher computational accuracy and efficiency.}
}