晏鸣宇

个人信息Personal Information

研究员(自然科学)   博士生导师   硕士生导师  

性别:男

在职信息:在职

所在单位:电气与电子工程学院

学历:研究生(博士)毕业

学位:工学博士学位

毕业院校:伊利诺伊理工大学

学科:电力系统及其自动化

论文成果

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A low-carbon planning method for joint regional-district multi-energy systems: From the perspective of privacy protection

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论文类型:期刊论文

第一作者:Wei Gan

通讯作者:Mingyu Yan

合写作者:Jianfeng Wen,Wei Yao,Jing Zhang

发表刊物:Applied Energy

收录刊物:SCI

刊物所在地:英国

学科门类:工学

一级学科:电气工程

文献类型:J

卷号:311

页面范围:118595

ISSN号:0306-2619

关键字:Multi-energy system;Joint planning;Energy hub;Low-carbon energy system;Privacy protection;Enhanced Benders decomposition

DOI码:10.1016/j.apenergy.2022.118595

发表时间:2022-04-01

影响因子:11.446

摘要:The construction of the multi-energy system (MES) is regarded as one of the silver bullets that help construct a low-carbon and high-efficiency energy system. In addition to the synergy of multiple energy systems, the coordination of regional and district energy systems can further improve flexibility. However, current studies rarely focus on the joint planning of regional-district MES. Additionally, privacy protection has not been considered in multi-energy system planning yet. This paper proposes a novel low-carbon planning method for joint regional-district MES which ensures the privacy of regional and district energy systems based on the enhanced Benders decomposition. A new Benders cut generation method with refined iteration and improved convergence is designed for the planning model where the subproblem itself is the mixed-integer linear programming. To ensure convergence and optimality, supplementary Benders cuts for convergence restoration are also generated. Numerical results tested on a real-world MES in North China and a modified IEEE RTS-79 40-node MES show the effectiveness of the proposed planning method and solution technique. The simulation results validate that the proposed joint planning method can enhance the economic benefit of planning and reduce carbon emission, and the computational performance of the enhanced Benders decomposition is also validated from the perspectives of both computational accuracy and time. In the real-world MES, the joint planning method saves 8.8% of the total cost and reduces carbon emission by 11.1 % compared to the separate planning method.

发布期刊链接:https://www.sciencedirect.com/science/article/pii/S0306261922000745