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GIS and location theory based bioene...

University of Waterloo (Canada).

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GIS and location theory based bioenergy systems planning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	GIS and location theory based bioenergy systems planning./
作者:	Dong, Jingyuan.
面頁冊數:	138 p.
附註:	Source: Masters Abstracts International, Volume: 47-03, page: 1847.
Contained By:	Masters Abstracts International47-03.
標題:	Engineering, System Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR43635
ISBN:	9780494436356

GIS and location theory based bioenergy systems planning.
Dong, Jingyuan.

GIS and location theory based bioenergy systems planning. - 138 p.

Source: Masters Abstracts International, Volume: 47-03, page: 1847.

Thesis (M.A.Sc.)--University of Waterloo (Canada), 2008.

This research is concerned with bioenergy systems planning and optimization modelling in the context of locating biomass power plants and allocating available biomass feedstock to the active plants. Bioenergy, a promising renewable energy resource, has potentially significant benefits to climate change, global warming, and alternative energy supplies. As modern bioenergy applications in power production have the ability to generate cleaner electricity and reduce Green House Gas (GHG) emissions compared with traditional fossil fuels, many researchers have proposed various approaches to obtain competitive power generation prices from biomass in different ways. However, the highly dispersed geographical distribution of biomass is a big challenge for regional bioenergy systems planning.

ISBN: 9780494436356Subjects--Topical Terms:

1018128
Engineering, System Science.

GIS and location theory based bioenergy systems planning.
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502 $a Thesis (M.A.Sc.)--University of Waterloo (Canada), 2008.
520 $a This research is concerned with bioenergy systems planning and optimization modelling in the context of locating biomass power plants and allocating available biomass feedstock to the active plants. Bioenergy, a promising renewable energy resource, has potentially significant benefits to climate change, global warming, and alternative energy supplies. As modern bioenergy applications in power production have the ability to generate cleaner electricity and reduce Green House Gas (GHG) emissions compared with traditional fossil fuels, many researchers have proposed various approaches to obtain competitive power generation prices from biomass in different ways. However, the highly dispersed geographical distribution of biomass is a big challenge for regional bioenergy systems planning.
520 $a This thesis introduces an integrated methodology combining Geographic Information Systems (GIS) and discrete location theories for biomass availability assessment, biomass power plant candidate selection, and location-allocation of power plants and biomass supplies. Firstly, a well known discrete location model---the p-Median Problem (PMP) model is employed to minimize the weighted transportation costs of delivering all collectable biomass to active power plants. Then, a p-Uncapacitated Facility Location Problem (p-UFLP) model for minimizing the Levelized Unit Costs of Energy (LUCE) is proposed and genetic algorithms (GAs) for solving these optimization problems are investigated. To find the most suitable sites for constructing biomass power plants, the Analytic Hierarchy Process (AHP) and GIS based suitability analysis are employed subject to economical, societal, public health, and environmental constraints and factors. These methods and models are aimed at evaluating available biomass, optimally locating biomass power plants and distributing all agricultural biomass to the active power plants.
520 $a The significance of this dissertation is that a fully comprehensive approach mixed with the applications of GIS, spatial analysis techniques, an AHP method and discrete location theories has been developed to address regional bioenergy systems planning, involving agricultural biomass potential estimation, power plants siting, and facility locations and supplies allocation scenarios. With the availability of the spatial and statistical data, these models are capable of evaluating and identifying electric power generation from renewable bioenergy on the regional scale optimally. It thus provides the essential information to decision makers in bioenergy planning and renewable bioenergy management. An application sited in the Region of Waterloo, Ontario Canada is presented to demonstrate the analysis and modelling process.
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