Elsevier

Biomass and Bioenergy

Volume 139, August 2020, 105620
Biomass and Bioenergy

Feasibility of locating biomass-to-bioenergy conversion facilities using spatial information technologies: A case study on forest biomass in Queensland, Australia

https://doi.org/10.1016/j.biombioe.2020.105620Get rights and content

Highlights

  • A DSS developed in GIS to estimates available forest biomass and evaluates the feasibility of new bioenergy facility locations.

  • A suitability analysis based on LISA identifies strategic sites for biomass to bioenergy conversion.

  • An optimality analysis based on location-allocation identifies tactical locations for biomass to bioenergy conversion.

Abstract

There are large volumes of forest biomass available, distributed over extensive geographic areas in Australia. However, it is largely a low-value resource sensitive to high procurement costs. Transportation cost is typically the biggest factor in the cost of a forest biomass supply chain and is a critical factor in the planning of profitable bioenergy conversion facilities. This study presents an example of using geographical information systems (GIS) to 1) evaluate the feasibility of setting up new bioenergy facilities, 2) evaluate the location of existing bioenergy facilities, and 3) optimize the locations of facilities in Queensland, Australia. This study uses forest biomass availability estimated from 5-year harvest log volumes. The log volumes are refined to biomass energy (PJ) using a model that considers forest type, sustainable retention of residues on sites, residue proportions of total above-ground biomass and energy conversion factors. The strategic locations of bioenergy conversion facilities are defined using cluster and outlier analysis of biomass energy distribution and transportation distance using the local index of spatial autocorrelation (LISA) in a GIS environment. The tactical selection of bioenergy conversion facilities is then established based on the required number of facilities and capacity, together with the maximal distance for transporting the forest biomass. This study uses Queensland, Australia as the study area to demonstrate the effectiveness of modern GIS tools to achieve more scientific planning in bioenergy conversion facility networks and supply chain.

Introduction

A critical factor of using forest biomass for bioenergy is the economic viability. With the objective to increase bioenergy production from forest biomass, the cost of harvest, collecting, transport and processing of biomass need to be considered [[1], [2], [3]]. Each of these processes defines a step of the biomass supply chain. The biggest cost contribution comes from transport, due to the wide and dispersed distribution of forest resources relative to energy demand at the global level and due to the scattered nature of residues at the local level. Transport costs are affected by the biomass quality, biomass moisture content, transportation mode and distance [4]. Additionally, the supply of forest residues varies significantly, both spatially and temporally, depending on the type of forest, harvesting regime, tree species, and age of the stand [[5], [6], [7]]. Thus, transport cost and availability of biomass are highly deterministic in the design of a biomass supply chain and location of bioenergy facilities. Biomass is a high-volume resource with a low-profit margin and thus a little variation in the cost of the supply chain can affect the profit margin of the processing and transport to a bioenergy conversion facility [8].

The design and selection of bioenergy facilities in proximity to the biomass resources have been a focal point for many studies. Due to the dispersed nature of the forest, geographical information systems (GIS) are used increasingly for the evaluation of forest product supply chains [[9], [10], [11], [12], [13]]. Today, an array of tools and functions exist within GIS to deliver the best solution for the facility location problem. Solving complex facility location problems in GIS can generally be approached through a suitability or optimality analysis [13,14]. Suitability analysis uses different types of buffering and spatial overlap operations to eliminate unsuitable areas and prioritize areas based on a number of constraints and favoring factors often referred to as a multi-criteria assessment (MCA). The overlaying processes range from a simple Boolean overlay, eliminating constraining area, to classic weight linear combination, analytical hierarchy processing and ordered weighting averaging [15,16]. The use of GIS and MCA to include economic, environmental and social criteria affecting site selection has also been applied and reviewed in a number of studies [15,[17], [18], [19], [20], [21]].

An optimality analysis uses the typical business model between supply and demand to find optimal locations of facilities to minimize the cost. Several location-allocation approaches are available within GIS to address this problem. Each approach seeks to match supply with demand to identify the best location for a number of facilities based on an evaluation factor such as distance. Examples of such approaches are: the P-Median model that minimized the weighted impedance or distance; maximized coverage to satisfy the greatest demand within the cut-off distance; minimize facilities to cover the highest demand with the lowest number of facilities; or maximize capacitated coverage with a finite capacity for the facilities, where the capacity is met, and where the demand allocated to the facility, by the cut-off value, is minimized [22].

The combination of GIS with model-based location-allocation tools has led several decision support systems (DSS) to manage forest biomass for bioenergy supply [2,9,23]. DSS handles all the geographic parameters that influence biomass availability, selection of bioenergy facility sites, and can simulate the cost of supply chain [5,14,24]. The location-allocation model optimizes the location of bioenergy facilities based on the available biomass at any given time, even if this is beyond a reasonable transportation distance to the bioenergy facility to deliver all biomass [14]. A supply-area model is similar to a location-allocation model but puts bioenergy facilities in high-density biomass areas. A threshold for transportation cost cuts off biomass that is too far from the facility and becomes useless [14]. The Biomass Resource Assessment Version One (BRAVO), was the first kind GIS-based DSS for bioenergy facility location developed [9]. Voivontas et al. [2] created a variant of the BRAVO model to successfully implement the use of suitability and optimality analysis, in a GIS DSS, for locating facilities. The suitability analysis evaluated the centroids of administrative areas as potential facility locations. Other examples include Ranta [5] who applied a location-allocation model according to supply resources of logging residues in Finland. Shi et al. [14] converted remotely sensed biomass data for the supply of resources in a service-area model, using potential facility locations on a road network as demand points. Sultana and Kumar [25] selected locations based on a hierarchical process or suitability analysis to continue the optimized selection of facilities in an optimality analysis. At last, Comber et al. [13] highlighted the need to combine suitability and optimality approaches. Their research proposed and extended the location-allocation P-Median model that evaluates supply catchments, using rural villages as demand points [13]. The combination of suitability and optimality addresses critical concerns in bioenergy planning. For instance, suitability analysis needs to evaluate the spatial distribution of the bioenergy resource to identify suitable areas. Whilst the optimality analysis needs to select the optimal location based on the required demand of a facility, optimal use of the bioenergy resource supply, the maximum transport distance, and other constraining parameters.

The use of GIS offers considerable potential for addressing site selection and location-allocation problems of bioenergy conversion facilities, which is well demonstrated in case studies [2,14,18]. At the same time, there is increased attention on the use of forest biomass resources in Australia to increase the share of bioenergy. This case study, conducted in Queensland, Australia, uses location-allocation models in GIS to locate bioenergy conversion facilities. This study determines the need for additional biomass facilities along with their most appropriate strategic and tactical locations. Without being too specific about the output energy, this research indicates the capacities of bioenergy facilities as a unit of raw energy (Joules), unless existing facilities have a well-defined electrical capacity (megawatt). By presenting the raw energy value, the bioenergy facilities remain potential locations to convert forest biomass to electricity, heat, liquid or gaseous fuels, or wood pellets for further transport and conversion. This study maps the forest footprint and applies a generic model for biomass availability estimation. The estimated biomass potential from the forest resources is presented in an energy heat map that allows statistical comparison between elements of the heat map. Using a local index of spatial autocorrelation (LISA), this study identifies strategic locations for bioenergy facilities based on the availability of significant biomass energy. To perform an optimality analysis, this study applies a location-allocation model to identify tactical sites for bioenergy facilities based on energy efficiency and transport distance. Additionally, the feasibility of existing facilities in Queensland with the capacity to convert woody biomass to energy (electricity) is evaluated.

Section snippets

Study area

The study was conducted in Queensland, Australia, which is the second-largest state with a total forest area of 51 Mha or 39% of the national forest footprint [26]. Based on tenure types and potentially commercial species, an area of 20 Mha of state-owned native forests is commercially available for timber harvest together with 1 Mha of private native forests and 216,000 ha of plantations [27,28]. The total timber production in the financial year 2017–2018 equaled 3,153,000 m3 [29]. The

Methodology

An overview of the objects and attributes to estimate available forest biomass energy for strategic and tactical site selection is presented in Fig. 1. Forest type and total forest area define the forest footprint, and the availability of forest biomass was calculated according to timber log volumes and a series of residue assumptions. Both measures together with the administrative boundary of the study area allowed the creation of a heat map which is represented under measures of availability.

Availability analysis: biomass energy heat map

The availability analysis aimed to determine the amount of available biomass and their respective locations based on available information on biomass types for the region. In this study, using GIS we analyzed a combination of biomass production data and bioenergy resource footprint to create a heat map to represent the density of forest biomass in the region. The data sets used for the heatmap are specific to the research case.

Suitability analysis: strategic facility locations

The aggregated biomass energy per administrative area contains 69,764 meshblock polygon features in the state of Queensland each representing forest biomass energy in PJ per annum. In order to identify areas with a higher density of biomass across the state, spatial autocorrelation was used to identify hot spots of high biomass energy. The global Moran's index was used to evaluate to total autocorrelation in the study area of Queensland. The global Moran's I index is a measure of spatial

Optimality analysis: tactical facility locations

The tactical site selection was used as part of the optimality study following the suitability as described above. In this study, a combination of service area modeling and location-allocation modeling was used. Service area modeling puts bioenergy facilities at those locations surrounded by high biomass densities. Service area modeling is appropriate when there is a threshold for transportation cost or distance, in the philosophy that biomass that is too distant becomes useless for a

Evaluating existing wood-based electricity facilities in Queensland

There are already three bioenergy facilities in Queensland that create energy from wood waste and an additional three facilities have the capacity to deal with wood residues based on the used conversion technology. Together they have an installed electrical capacity that can convert up to 112.3 MW of energy (electricity) in total (Table 4) [63]. Because the facilities were in place with given technology and capacity, it was worth identifying whether these were in a suitable location to handle

Discussion

This paper presents a case study that utilizes GIS methods in planning biomass power facilities, combining detailed forest footprint analysis and estimations of usable biomass in a suitability analysis to define strategic locations with the help of LISA. In addition to the increased significance of the suitable facility location analysis, a decision support system (DSS) is developed to identify tactical facility locations in an optimality analysis using location-allocation. A process that can

Conclusion

This study provides an example of using spatial data with the available biomass inventory data and developed spatial information technologies to achieve more scientific planning for building biomass-to-bioenergy facilities in Queensland. The knowledge acquired from this research is valuable when the project gets expanded across Australia and includes additional sources of biomass other than forest biomass. Planning studies such as this one provide valuable insight into the distribution of

Acknowledgements

This project was funded and supported by Australian Biomass for Bioenergy Assessment (ABBA) as part of the Australian Renewable Energy Agency (ARENA) through a University of the Sunshine Coast Research Scholarship (USCRS-ABBA), grant number (PRJ-010376). The Gottstein Trust provided additional funding through a forest industry top-up scholarship. We want to thank, Prof Mark Brown, the anonymous reviewers and the journal editor for taking their time to review and provide valuable input and

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