Parametric analysis of energy quality management for district in China using multi-objective optimization approach

被引：0

作者：

Lu, Hai ^{[1
,2
]}

Yu, Zitao ^{[3
]}

Alanne, Kari ^{[4
]}

Xu, Xu ^{[5
]}

Fan, Liwu ^{[3
]}

Yu, Han ^{[3
]}

Zhang, Liang ^{[3
]}

Martinac, Ivo ^{[1
]}

机构：

[1] Dept. of Civil and Architectural Engineering, KTH Royal Institute of Technology, Brinellvägen 23, 100 44 Stockholm, Sweden

[2] Key Laboratory of Efficient Utilization of Low and Medium Grade Energy (Tianjin University), Ministry of Education of China, Tianjin 300072, China

[3] Institute of Thermal Science and Power System, Department of Energy Engineering, Zhejiang University, Hangzhou 310027, China

[4] Dept. of Energy Technology, Aalto University, P.O. Box 14100, 00076 Aalto, Finland

[5] Institute of Energy Engineering, College of Metrological and Measurement Engineering, China Jiliang University, Hangzhou 310018, China

来源：

Energy Conversion and Management | 2014年 / 87卷

基金：

中国国家自然科学基金;

关键词：

Life cycle - Quality control - Multiobjective optimization - Exergy - Quality management - Energy efficiency - Global warming - Pareto principle;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Due to the increasing energy demands and global warming, energy quality management (EQM) for districts has been getting importance over the last few decades. The evaluation of the optimum energy systems for specific districts is an essential part of EQM. This paper presents a deep analysis of the optimum energy systems for a district sited in China. A multi-objective optimization approach based on Genetic Algorithm (GA) is proposed for the analysis. The optimization process aims to search for the suitable 3E (minimum economic cost and environmental burden as well as maximum efficiency) energy systems. Here, life cycle CO2 equivalent (LCCO2), life cycle cost (LCC) and exergy efficiency (EE) are set as optimization objectives. Then, the optimum energy systems for the Chinese case are presented. The final work is to investigate the effects of different energy parameters. The results show the optimum energy systems might vary significantly depending on some parameters. © 2014 Elsevier B.V. All rights reserved.

引用

页码：636 / 646