Planning regional energy system in association with greenhouse gas mitigation under uncertainty

• Published in: Applied energy Vol. 88; no. 3; pp. 599 - 611
• Main Authors: Li, Y.P., Huang, G.H., Chen, X.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.03.2011; Elsevier
• Series: Applied Energy
• Subjects: Air pollution caused by fuel industries; Applied sciences; case studies; cost effectiveness; Energy; energy demand; Energy economics; Energy policy; Energy systems; Energy systems Greenhouse gas Mitigation Multiple uncertainties Optimization Planning; Energy. Thermal use of fuels; Exact sciences and technology; fossil fuels; General, economic and professional studies; General. Regulations. Norms. Economy; Greenhouse gas; greenhouse gas emissions; greenhouse gases; integer programming; issues and policy; Methodology. Modelling; Mitigation; Multiple uncertainties; Optimization; Planning; probability distribution; uncertainty; Mitigation; Energy systems; Multiple uncertainties; Planning; Greenhouse gas; Optimization
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2010.07.037

Greenhouse gas (GHG) concentrations are expected to continue to rise due to the ever-increasing use of fossil fuels and ever-boosting demand for energy. This leads to inevitable conflict between satisfying increasing energy demand and reducing GHG emissions. In this study, an integrated fuzzy-stochastic optimization model (IFOM) is developed for planning energy systems in association with GHG mitigation. Multiple uncertainties presented as probability distributions, fuzzy-intervals and their combinations are allowed to be incorporated within the framework of IFOM. The developed method is then applied to a case study of long-term planning of a regional energy system, where integer programming (IP) technique is introduced into the IFOM to facilitate dynamic analysis for capacity-expansion planning of energy-production facilities within a multistage context to satisfy increasing energy demand. Solutions related fuzzy and probability information are obtained and can be used for generating decision alternatives. The results can not only provide optimal energy resource/service allocation and capacity-expansion plans, but also help decision-makers identify desired policies for GHG mitigation with a cost-effective manner.