Regional energy wood logistics – optimizing local fuel supply
Kanzian C., Holzleitner F., Stampfer K., Ashton S. (2009). Regional energy wood logistics – optimizing local fuel supply. Silva Fennica vol. 43 no. 1 article id 464. https://doi.org/10.14214/sf.464
Abstract
The promotion of electric energy production from solid biomass by the Austrian government has lead to a boom in the construction of new combined heat and power plants. The current total demand for wood chips in the research area for energy purposes is 70 400 m3 of loose volume chips per year. The expected increase in demand due to these new plants is more than 4 times greater than current demand: up to 302 700 m3 of loose volume per year. Even if the energy wood feedstock potential is satisfactory, the design of the supply chain is still unresolved. The aim of this study is to give decision-makers a base for further development. To accomplish this, we designed and tested four different supply scenarios: one for 9 plants and one for 16 plants. The scenarios were developed using a combination of geographic information systems (GIS) and linear programming methods. The results indicate that direct transport of solid fuel wood as round wood and chipping at the plant is the cheapest supply system with a resulting cost of 5.6–6.6 EUR/m3 loose. Using harvesting residues can only be recommended for large plants because of poor fuel quality. In this case, residues would be chipped at or near the landing, piled and transported via self-loading trucks at a cost between 8.4 and 9.1 EUR/m3 loose. In order to meet increasing demand and to ensure a continuous supply, especially during the winter and spring seasons it is necessary to optimize the supply chain by including storage terminals. However, using terminals and increased demand both lead to higher logistical costs. For example, if the total volume is handled via terminals, the average supply costs including storage will increase by 26%. Higher demand increases the costs by 24%.
Keywords
logistics;
energy wood;
transport optimization;
GIS
Received 5 October 2007 Accepted 20 January 2009 Published 31 December 2009
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