Geographical analyses of wood chip potentials, costs and supply for sustainable energy production in Denmark Dr. Bernd Moeller, Aalborg University Denmark.

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

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Acknowledgement

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

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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

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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

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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)

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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

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How GIS are appliedIn-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.

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Biomass resource mapping

LCP code Description Comments Total area [ha] % of forest area14 Shrub / forest No wood chip production 83,534 16.815 Deciduous forest Tops and large branches from felling 137,718 27.816 Coniferous forest Tops and large branches from felling 71,404 14.426 Beech forest Tops and large branches from felling 49,011 9.927 Young trees Thinning 14,688 3.028 Spruce plantation Tops and large branches from felling 60,587 12.229 Mixed forest Assumed 35% deciduous, 65% coniferous 8,511 1.730 Mountain pine woodland Wood chips from whole trees 32,433 6.532 Larch forest Tops and large branches from felling 2,037 0.433 Recently felled forest No wood chip production 2,394 0.534 Thin coniferous forest No wood chip production 33,564 6.8

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Annual recoverable resources

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Selected energy plants

Plantname

Plant type Composition of fuel consumption Rated power andheat output

Annual wood chipconsumption

Hals District heating 64% wood, 31% straw, 5% oil 0 MW, 8 MJ/s 5,500 tons dry matterSkørping District heating 95% wood, 5% oil 0 MW, 7 MJ/s 9,000 tons dry matterEnsted Power plant 93% coal, 5% straw, 1% wood, 1% oil 600 MW, 80 MJ/s 15,000 tons dry matterHerning Cogeneration 55% wood, 45% natural gas 89 MW, 174 MJ/s 50,000 tons dry matter

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Transport cost modeling

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Average costs / accumulated supply

$20$18

$14

$19

0

5

10

15

20

25

30

0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000

Supply [tons dm]

Tra

nsp

ort

co

sts

[US

$/to

n d

m]

Herning Hals Skørping Ensted

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Interpretation of results

$13

$14

$15

$16

$17

$18

$19

$20

$21

-20% -10% 0% 10% 20%

Variation in fuel demand

Av

era

ge

fu

el t

ran

sp

ort

co

sts

Ensted

Herning

Hals

Skørping

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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

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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.