A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty

Energies

Yong Zeng²  Yanpeng Cai¹  Guohe Huang³ 

[1] State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China;State Key Laboratory of Petroleum Resource and Prospecting, College of Geosciences, China Petroleum University, Beijing 102249, China; E-Mail:;Faculty of Engineering and Applied Science, University of Regina, Regina, S4S 0A2, Canada

关键词: optimization;    GHG emission mitigation;    energy systems;    planning;    uncertainty;   

DOI  :  10.3390/en4101624

来源: mdpi

【 摘 要 】

Energy is crucial in supporting people’s daily lives and the continual quest for human development. Due to the associated complexities and uncertainties, decision makers and planners are facing increased pressure to respond more effectively to a number of energy-related issues and conflicts, as well as GHG emission mitigation within the multiple scales of energy management systems (EMSs). This quandary requires a focused effort to resolve a wide range of issues related to EMSs, as well as the associated economic and environmental implications. Effective systems analysis approaches under uncertainty to successfully address interactions, complexities, uncertainties, and changing conditions associated with EMSs is desired, which require a systematic investigation of the current studies on energy systems. Systems analysis and optimization modeling for low-carbon energy systems planning with the consideration of GHG emission reduction under uncertainty is thus comprehensively reviewed in this paper. A number of related methodologies and applications related to: (a) optimization modeling of GHG emission mitigation; (b) optimization modeling of energy systems planning under uncertainty; and (c) model-based decision support tools are examined. Perspectives of effective management schemes are investigated, demonstrating many demanding areas for enhanced research efforts, which include issues of data availability and reliability, concerns in uncertainty, necessity of post-modeling analysis, and usefulness of development of simulation techniques.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.