Potentiale zur energetischen Nutzung von Biomasse in der ... - EPFL

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where:
DC ( i,
j ) : unit cost of biomass delivered from pixel i to pixel j (SFr/kWh)
FG : farm-gate price of biomass at pixel i (SFr/Sm 3
)
i
DC
( i,
j )
KD : distance dependent cost parameter (SFr/Sm 3
-km)
DIST ( i,
j ) : road distance between pixel i and pixel j (km)
KT : time dependent cost parameter (SFr/Sm 3
-hr)
TIME ( i,
j ) : travel time between pixel i and pixel j (hr)
KF : fixed cost of biomass loading, securing on track and unloading (SFr/Sm 3
)
KE : energy content parameter (kWh/Sm 3
)
=
( FG + KD×
DIST + KT × TIME + KF)
i
( i,
j ) ( i,
j )
KE
Knowing the cost of delivered biomass at specific locations and the quantity of available energy biomass
at each pixel, the following algorithm can be applied in order to calculate the marginal price of biomass
feedstock and to identify the forest stands that would supply biomass to the projected energy
installations. Suppose the demand (D) is satisfied in a least cost manner. The delivered costs are sorted
from the lowest to the highest, and then the available quantities are summed sequentially, starting from
the point with the lowest delivered costs, until the level of demand is met. Whenever the D th unit of
biomass is reached, the delivered costs associated with that point is considered as the marginal price for
demand D at the specific location of the projected energy facility. The forestry exploitations with
delivered cost lower and equal to the marginal price should be considered as the first-priority biomass
suppliers.
10.2.4 Optimal location of biomass fuelled energy facility
In order to determine optimal locations for deployment of biomass fuelled energy facilities it is proposed
to apply the “marginal price surface” method (Noon et al. 2002). This method consists in the
computation of marginal prices for each of the pixels by treating them as a demand point, while treating
all other pixels as supply points. The collection of the marginal prices at all pixels will form the surface
map of delivered marginal prices for a specified amount of biomass.
Different maps can be created by specifying varying amounts of biomass corresponding to the different
types and capacities of biomass fuelled energy installations. An illustration of the marginal price surface
method is given in Figure 49, which shows an example of two biofuel plants characterised by different
biomass consumption levels, and hence facing different marginal prices of biomass supply.
The optimal location of a biomass energy facility should be chosen in the area with the lowest marginal
price of biomass, provided there is a sufficient demand for plant’s energy services. Once the first optimal
location is determined and there is enough biomass resource for fuelling other installations, the
algorithm can be repeated in order to select second, third best locations and so on. In this case, the
pixels supplying biomass to the first optimal location should be excluded. According to Voivontas et al.
(2001) the total technological potential of biomass use for energy could be assessed, in such a way,
through the identification of all possible locations subject to availability of biomass resources.