Interval-fuzzy municipal-scale energy model for identification of optimal strategies for energy management A case study of Tianjin, China

RENEWABLE ENERGY

卷:86

Article

Chen, C.^1,2  Li, Y. P.^2,3  Huang, G. H.^2,3 

[1] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China

[2] North China Elect Power Univ, Sino Canada Resources & Environm Res Acad, MOE Key Lab Reg Energy Syst Optimizat, Beijing 102206, Peoples R China

[3] Univ Regina, Fac Engn & Appl Sci, Environm Syst Engn Program, Regina, SK S4S 0A2, Canada

关键词: Energy system;    Fuzzy programming;    Modeling;    Planning;    Municipal-scale;    Renewable energy;   

DOI  :  10.1016/j.renene.2015.09.040

来源: Elsevier

【 摘 要 】

An interval-fuzzy municipal-scale energy model (IF-MEM) is developed for energy systems planning. IF-MEM can effectively tackle uncertainties presented in terms of fuzzy sets and discrete intervals. IF-MEM is applied to a real case of energy systems planning in the City of Tianjin, which is the economic and industrial center of northern China. IF-MEM allows in-depth analyses for tradeoffs among economic objective, satisfaction degree, and constraint-violation risk. The results indicate that reasonable solutions have been generated. The results suggest that: (i) coal would be the dominant sources in city's energy systems due to the increasing energy demand; however, its consumption ratio would decrease over the planning horizon due to requirements of environment protection and sustainable development; (ii) clean (e.g., natural gas, liquefied petroleum gas) and renewable energies (e.g., wind power, solar power) would be vigorously encouraged during the planning periods; (iii) coal-fired technology (i.e., coal-fired power and heat) would be the major contributor for the city's atmosphere pollution; (iv) the pollutant emissions would be decreased over the planning horizon due to the restriction of fossil fuels and the development of renewable energy. Promoting the utilization of renewable energy sources is crucial for supporting pollutant emission reduction and solving energy shortage crisis. (C) 2015 Published by Elsevier Ltd.

【 授权许可】

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