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. 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. 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.

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. 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. 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.

<p>In the U.S., bioenergy accounts for about 50% of the total renewable energy that is generated. Every stage in the life cycle of using bioenergy (e.g., growing biomass, harvesting biomass, transporting biomass, and converting to fuels or materials) has consequences in terms of the three dimensions of sustainability: economy, environment, and society. An integrated sustainability model (ISM) using system dynamics is developed for a bioenergy system to understand how changes in a bioenergy system influence environmental measures, economic development, and social impacts.<br></p><p><br></p><p>Biomass may be used as a source of energy in a variety of ways. The U.S. corn ethanol system forest residue system for electricity generation, and cellulosic ethanol system have been investigated. Predictions, such as greenhouse gas (GHG) savings, soil carbon sequestration, monetary gain, employment, and social cost of carbon are made for a given temporal scale. For the corn ethanol system, the annual tax revenue created by the ethanol industry can offer a significant benefit to society. For the forest residue system for electricity generation, different policy scenarios varying the bioenergy share of the total electricity generation were identified and examined via the ISM. The results of the scenario analysis indicate that an increase in the bioenergy contribution toward meeting the total electricity demand will stimulate the bioenergy market for electricity generation. For the cellulosic ethanol system, the compliance of cellulosic ethanol can be achieved under the advanced bioconversion technologies and the expansion of energy crops. However, nitrate leaching and biodiversity change should be considered when expanding energy crops on marginal land, pasture, and cropland. Moreover, three bioenergy systems reduce GHG emissions significantly, relative to fossil fuel sources that are displaced, and create economic benefits (e.g., GDP and employment). Additionally, a spatial agent-based modeling is developed to understand farmers’ behaviors of energy crop adoption and the viability of cellulosic biofuel commercialization.<br></p>