Bioenergy refers to energy that is produced from biological sources or it is simply a form of renewable energy (Alberta Energy, 2016). Biofuel exists in the solid, liquid and gaseous forms where they can be used in industrial processes or for residential and commercial consumption (Calvert & Mabee, 2014; Williams, 2011). A major source of feedstock for bioenergy is lignocellulosic plant material which can be derived from agricultural residues, forested wood, dedicated wood, biological components of municipal solid waste and herbaceous crops (Calvert & Mabee, 2014). Biofuel is important since it has a fungible nature, thus, it produces a renewable form of energy (Calvert & Mabee, 2014). It is also an alternative to the non-renewable forms of energy such as coal, oil and gas.
Biofuel is transported via ship, truck, pipeline and rail shipping routes (Searcy et al., 2007). Raw biomass has a higher financial transportation cost per unit of material than energy products (Delzeit & Kellner, 2011; Searcy et al., 2007). Therefore, due to its bulky raw biomass and low energy density, the travel times and distances from the supply sources to areas of bioenergy conversion need to be limited (Calvert & Mabee, 2014; Delzeit & Kellner, 2011). This ensures that there is a high financial return with the maximum energy output (Calvert & Mabee, 2014). This study focuses on truck transportation cost and the implementation of geographic information systems (GIS) to determine the best locations for bioenergy production facilities, as well as the shortest transport routes from supply sites to conversion sites (Calvert & Mabee, 2014).
Accurately assessing the cost of transporting biofuel in Ontario is a growing concern in the energy subsector. Supply-cost curves were generated to compare the cost of production of biofuel resources versus the annual aggregated supply for certain areas. These supply-cost curves were generated in an Excel spreadsheet using yield and distance values from GIS supply-distance models (Calvert & Mabee, 2014). The comparison of vector-based data and raster-based data using supply-cost curves will help overcome inaccuracies and provide a better understanding of spatial distribution for bioenergy products in eastern Ontario.
This study addressed whether or not there is a significant difference between supply-cost curves using raster and vector data analysis. Consequently, this helped to decide which method is best for determining the most cost-effective routes for biomass resource transportation from raw material sites to bio-refineries, within eastern Ontario, Canada. The biomass resources considered in this research were categorized as forest harvest residues, forest thinning material, associated roundwood, stover, switchgrass and straw (Calvert & Mabee, 2014). An immense amount of knowledge exists in the field of network analysis in road planning. However, the main issue affecting the progress and accuracy of spatial analysis is the lack of data availability (Calvert & Mabee, 2014). For example, a limited amount of logging road data is available (Calvert & Mabee). This information would allow for a better estimation of supply costs when using raster data if it were to be available (Calvert & Mabee). A lack of slope data also results in the exclusion of its effects on costs and distance estimation in past research (Calvert & Mabee). An important consideration in such analyses is that the data are very detailed and of a high resolution such as 25 m (Moller & Nielsen, 2007). Another consideration involves the cost of harvesting biomass residues and transporting them to the bioenergy conversion plant (Ralevic, Ryans & Cornier, 2010).
Topological features within vector data, such as road networks, create a major discrepancy between vector and raster data when producing models of bioenergy supply-cost curves (Moller & Nielsen, 2007). Raster data accounts for road networks on the basis of continuous straight-line or Euclidean distances, which account for the entire area of adjacent cells. This overestimation affects locational accuracy and, therefore, does not consider real-world distances (Yoshioka et al., 2011). Detailed data are necessary for road network analysis and GIS requires a suitable spatial scale for accurate analysis (Yoshioka et al., 2011). Many studies have converted vector data to raster data to perform raster functions and map algebra (Alam et al., 2012; Moller & Nielsen, 2007; Yoshioka et al., 2011). However, based on the previously mentioned shortcomings, vector data is expected to provide more accurate information for the supply-cost curves.
GIS is important in addressing the discrepancy between vector and raster data. GIS applications such as Spatial Analyst (SA) and Network Analyst (NA) were used in this study for raster and vector data, respectively. Both analyses helped determine the quantity and type of biomass within a 200 km radius around the road networks for each facility, within the study area of eastern Ontario (Calvert & Mabee, 2014). Raster and vector data methods are used to calculate the least cost-distance which helped determine the best route for transporting biomass resources and advancing the research of Dr. Calvert (University of Guelph). This information will be beneficial to the areas of planning and addressing policy issues. Mapping the most cost-effective route for biomass transportation from the source to the destination will aid in improved transportation planning. Policy issues with biomass harvesting and storage location/duration before transportation could also be determined when the least-cost pathway is generated. Supply-cost curves can aid in creating deployment-fostering policies such as tax benefits and tariffs when economic potentials are assessed (Izquierdo et al., 2010). Therefore, this research will be beneficial in accurately assessing the cost of transporting bioenergy in eastern Ontario.
Purpose of Research
The aim of this research project is to access any potential differences that arise when modelling supply-cost curves for bio-energy production in eastern Ontario using GIS vector and raster analysis techniques.
To identify variables and factors necessary to model cost-supply curves for bio-energy production.
To use raster and vector based models in order to determine yields of different bio-energy feedstock sources at different distances from potential bio-refinery plants.
To determine the cost of extracting different bioenergy feedstock sources and bring this to bio-refinery plants by inputting results from objective 2 into excel spreadsheet formulas.
To assess the limitations and strengths of both models using statistical analysis to explain potentially significant differences between cost-supply curves.