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Dr. Bernd Moeller, Aalborg University Denmark Dr. Per S. Nielsen, Forest Research PowerPoint Presentation
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Dr. Bernd Moeller, Aalborg University Denmark Dr. Per S. Nielsen, Forest Research

Dr. Bernd Moeller, Aalborg University Denmark Dr. Per S. Nielsen, Forest Research

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Dr. Bernd Moeller, Aalborg University Denmark Dr. Per S. Nielsen, Forest Research

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  1. Geographical analyses of wood chip potentials, costs and supply for sustainable energy production in Denmark Dr. Bernd Moeller, Aalborg University Denmark Dr. Per S. Nielsen, Forest Research

  2. Acknowledgement • Bruce Talbot, Hans Skov-Petersen and Niels Heding of the Danish Centre for Forest, Landscape and Planning – KVL

  3. Introduction • Determine the transport costs of wood chips from forest to location of energy plants in Denmark • Spatial relation between supply transportation, and costs • Spatial models with raster GIS

  4. Biomass in the Danish system • Wood covered 3.5% of primary fuel consumption in Denmark 2002 • 350,000 wet tons/year • In 80 energy plants • Very little un-used • 9 US$/GJ

  5. Biomass from forests • Forests cover 11.3% of land area • 20%<5 ha, 50%<50ha • Chips are from either summer dried logs or thinnings with a required moisture content of 40-55% (wet basis)

  6. Transportation of wood chips • Bin containers 40m3 • maximum load assumed • Costs includes in-forest transportation. • Costs includes costs independent on location (loading, chipping etc) • Does not include revenue a forest owner might receive. • Which means that the final cost curves does not reflect the wood chips market price

  7. How GIS are applied In-forest biomass, costs of transportation, possible plant locations and other issues are mapped in raster-GIS. Using layers of raster data, each geographically distributed aspect is analysed using cell-to-cell maths, neighborhood statistics and zonal geometry. The results are intensity maps or distributions of site-specific costs.

  8. Biomass resource mapping

  9. Annual recoverable resources

  10. Selected energy plants

  11. Transport cost modeling

  12. Interpretation of results

  13. Conclusions • Forest owners can assess the value of un-used residues • Hauling companies can use it for improve efficiency • Energy plants can use it to assess resources availability for new investments or upgrades (cogen) • Policy makers can use it to assess environmental and socio-economic aspects of local wood resources

  14. Conclusions (continued) • Although transportation cost is important other issues may be more important for the individual operator • The reality does not always the most optimal solution • Many players with different prices • Harvesting intervals of many small forests - challenge long term fuel supply demand from energy plants.