کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4915853 | 1428085 | 2017 | 24 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Developing a multicriteria decision support framework for CHP based combined district heating systems
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
مهندسی انرژی و فناوری های برق
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چکیده انگلیسی
CHP based combined district heating (DH) systems with gas-fired boilers for peak load shaving have higher energy and environmental efficiencies compared to DH systems supplied heat by heat only boilers. However, proper multicriteria decision making method is lacking for them. This paper is dedicated to develop a decision support framework from economy, energy, technology and environment viewpoints, in order to facilitate the planning/retrofitting of the combined DH systems. Firstly, the installation strategy of gas-fired boilers is introduced, and then combined heating alternatives to be addressed are constructed by choosing different base load ratios of CHP. Secondly, a criterion aggregation system is developed, based on which weights can be elicited using complementary judgment matrix (CJM) plus feasible weight space (FWS) methods. Thirdly, an application-oriented, multicriteria decision support framework is demonstrated in a real-life DH system in Daqing, China. Stochastic multicriteria acceptability analysis (SMAA) is implemented to synthetically handle the decision problem, which is characterized by incommensurable measurements, conflicting preferences, uncertainties and imprecise information. The results indicate that the developed framework works well in the multicriteria decision making for the combined district heating systems. The optimal base load ratio in the demonstration case is between 0.66 and 0.77 with high confidence.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Energy - Volume 205, 1 November 2017, Pages 345-368
Journal: Applied Energy - Volume 205, 1 November 2017, Pages 345-368
نویسندگان
Haichao Wang, Lin Duanmu, Risto Lahdelma, Xiangli Li,