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

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. Acknowledgement.

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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.

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