کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6681483 | 1428080 | 2018 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A new reliability assessment approach for integrated energy systems: Using hierarchical decoupling optimization framework and impact-increment based state enumeration method
ترجمه فارسی عنوان
یک رویکرد ارزیابی قابلیت اطمینان برای سیستم های انرژی یکپارچه: استفاده از چارچوب بهینه سازی سلسله مراتبی و روش شمارش وضعیت مبتنی بر افزایش تاثیر
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
مهندسی انرژی و فناوری های برق
چکیده انگلیسی
A new reliability assessment approach to Integrated Energy Systems (IESs) is introduced in this paper. The optimal load curtailment (OLC) algorithm and reliability assessment algorithm are both improved in the proposed approach. For the OLC problem, this paper develops a hierarchical decoupling optimization framework for both the energy hub optimal dispatch and the optimal power flow problems. This feasible solution can make the OLC calculation more efficient and accurate. For the reliability assessment algorithm, an impact-increment based state enumeration (IISE) method is accommodated for IESs to accelerate the reliability assessment process. Also, a reduction technique of higher order contingencies is presented for the reliability evaluation of IESs to further enhance the computational efficiency. Case studies are performed on an IESs test case combined the IEEE-33 bus system with 14-node gas system and a practical case combined the IEEE 118-bus power system with Belgian natural gas network Numerical results demonstrate the efficient and robust performance of the proposed approach. Besides, the impacts of energy conversion process and energy hubs on IESs reliability are analyzed in detail.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Energy - Volume 210, 15 January 2018, Pages 1237-1250
Journal: Applied Energy - Volume 210, 15 January 2018, Pages 1237-1250
نویسندگان
Yunkai Lei, Kai Hou, Yue Wang, Hongjie Jia, Pei Zhang, Yunfei Mu, Xiaolong Jin, Bingyan Sui,