Optimal Design of Sustainable Bioenergy Infrastructure System and Resource Allocation under Uncertainties.

详细信息   

• 作者：Chen ; Chien-Wei.
• 学历：Doctor
• 年：2011
• 导师：Fan, Yueyue,eadvisorOgden, Joan M.ecommittee memberD'Souza, Raissa M.ecommittee member
• 毕业院校：University of California
• Department：Transportation Technology and Policy
• ISBN：9781267238382
• CBH：3499413
• Country：USA
• 语种：English
• FileSize：4927379
• Pages：98

文摘

Biofuel has been recognized as one of the most feasible solutions for reducing oil dependence and greenhouse gas GHG) emissions and thus pressing issues of energy security and global climate change. From a sustainability standpoint, feedstock resources that have a lower impact on global food supplies and other natural resources and better efficiency in terms of life-cycle environmental performance are desired. Bio-waste resources as the ideal alternative to sugar and starch crops provide an excellent way of producing biofuel sustainably and are considered in this dissertation. A biofuel pathway concerns all the facilities and operations involved in the supply chain including feedstock resources, biofuel production and delivery infrastructures, and the end users. These components are interdependent and need to be considered as a whole through rigorous system analyses while designing a sustainable biofuel infrastructure system. In addition to the system dependencies, uncertainty is another major challenge in long-term strategic planning of sustainable biofuel supply systems. Cellulosic biofuels, compared with conventional fuels, face more uncertainties in future feedstock supply and biofuel demand, due to unpredictable weather conditions and changing regulations and policies. This dissertation attempts to establish mathematical models that can be used to evaluate the economic feasibility and system robustness of the sustainable bioethanol infrastructure system design, as well as provide system requirements and resource allocations. Potential future uncertainties from feedstock supply, biofuel demand, and technology evolvement are handled using a two-stage stochastic programming framework. To overcome the computational challenges from solving large-scale stochastic problem, a scenario based decomposition method&mdash；progression hedge, has been applied to the model. It is hoped this work will encourage further efforts in bridging the two communities of operations research and renewable energy.