AD ALTA
JOURNAL OF INTERDISCIPLINARY RESEARCH
Another indicator shows the changes in biomass processing
costs. Costs are calculated based on one conditional unit, in this
case it is equal to tonnes of oil equivalent. It is assumed that a
certain amount of fuel is needed to process one biomass unit. In
this case it is estimated that this amount equals to 8 liters per
unit. The price of fuels varies depending on the time of therefore
companies have to plan fuel costs otherwise their logistics
system can result in higher supply costs. This should be
considered during fuel purchases If there is a tendency for rising
fuel costs. Fuel prices might differ from one region to another
and that also influences the final energy price.
DBPC
=∑FN*FP
DBPC – Daily Biomass Production Cost; FN – Fuel Needs
(L/TOE); FP – Fuel Price.
In the empirical study transportation costs are calculated first.
The are estimated based on the cost of bringing one biomass unit
to power plant. Due to changing fuel prices cost estimates might
differ. In this case two optimal transportation prices are selected
to reflect fuel price volatility. This indicates how important it is
for businesses to evaluate how fuel price will change and its
effects on the price of the final product. Data provided in Table 4
shows that transportation costs can vary significantly depending
on the distance and fuel price.
Table 4 Simulation of transportation costs.
Supply site
ID (First
area)
Distance from
supply site to power
plant, km.
TC (TP
= 0,55)
TC (TP
= 0,75)
1
26
28,6
39
2
12
13,2
18
3
4
4,4
6
4
22
24,2
33
5
23
25,3
34,5
6
24
26,4
36
7
20
22
30
Supply site
ID (Second
area)
Distance from
supply site to power
plant, km.
TC (TP
= 0,55)
TC (TP
= 0,75)
1
34
37,4
51
2
25
27,5
37,5
3
9
9,9
13,5
4
19
20,9
28,5
5
23
25,3
34,5
Source: created by the authors
In Table 4 two transportation prices are selected. Based on that
transportation costs of one biomass unit are calculated and
expressed in amount of trucks needed. Transportation costs are
calculated estimating the distance of a round trip therefor the
volatility of transportation costs is financially more significant. It
can be seen that average costs in the second area are slightly
higher compared to the first area. This is related to the
distribution of supply sites in the second area where the plant is
located in the northern side of the region and there are no
conditions to bring supplies from areas that are located to the
north from the plant. Transporting biomass from the supply sited
located in the southern part results in increased transportation
costs. In the first area transportation costs are distributed evenly.
Feedstock supply sites located closest to the power plant
generate lowest distribution costs. However the feedstock
production capacity is not high enough for them to fully cover
demand for biomass. For this reason the logistics system should
help manage the costs and balance the biomass transportation
from separate supply sites.
Another indicator is biomass processing coefficient that is
calculated for three different seasons lasting for different period
of time. The highest amount of biomass is used during the winter
months. The consumption of biomass is lower in other months
and the amount is calculated using the adopted coefficient. In
summertime coefficient is 0.2, during the interim period before
and during the end of the heating season the coefficient is 0.6.
Data in Table 5 show that indicators the first area vary
considerably when seasons change. The highest coefficient value
is generated in the 5th supply site because it supplies the power
plant with largest amount of biomass. In the event of an
unexpected jump in energy demand precisely this supply site can
quickly provide required amount of biomass to the power plant.
Meanwhile during the tactical period resources are supplied form
sites 3 and 7, as they are relatively close to the power plant and
can meet the lower energy needs.
Table 5 Biomass processing coefficient in the first area
Supply
site ID
(First
area
FPR
DBP
DBPC
(1)
DBPC
(2)
DBPC
(3)
1
12,05
6,15
74,03
44,42
14,81
2
16,03
8,18
131,03
78,62
26,21
3
11,40
5,82
66,32
39,79
13,26
4
14,82
7,56
112,02
67,21
22,40
5
18,53
9,45
175,15
105,09
35,03
6
17,38
8,86
154,00
92,40
30,80
7
9,80
5,00
48,96
29,37
9,79
Source: created by the authors
Same actions were taken in the second area. As Table 6 shows
the highest coefficient value is calculated in the 5th supply site.
Indicators in the table vary significantly and this variation is
related to season changes. Following the values calculated for
the warm season it can be seen that supply site exploitation is
symbolic and it is only used to fulfil the hot water needs for that
period of time. This allows maintaining the efficiency of the
logistics system in both active and passive operating periods.
Table 6 Biomass processing coefficient in the second area.
Supply
site ID
(Second
area
FPR
DBP
DBPC
(1)
DBPC
(2)
DBPC
(3)
1
27,84
1,41
39,28
23,57
7,86
2
13,24
0,67
8,89
5,33
1,78
3
11,62
0,59
6,85
4,11
1,37
4
18,11
0,92
16,62
9,97
3,32
5
29,19
1,48
43,18
25,91
8,64
Source: created by the authors
Finally, the costs of biomass production for each site and the
entire area are estimated. It is assumed that there are 2 fuel price
rates that can change the overall costs of the logistics system. In
the case of the first area, estimation of supply site operating costs
show that most distant sites and those supplying largest amounts
of biomass generate highest processing costs (Table 7). As diesel
is used for biomass processing it amounts to the largest part of
the processing costs.
Table 7 Daily biomass preparation cost in the first area
Supply
site ID
(First
area
Required
quantity
of
biomass,
toe
Sum
(NF)
DBPC,
(FP =
1,10)
DBPC (FP
= 1,20)
1
2243
17944
19738,4
21532,8
2
2984
23872
26259,2
28646,4
3
2123
16984
18682,4
20380,8
4
2759
22072
24279,2
26486,4
5
3450
27600
30360
33120
6
3235
25880
28468
31056
7
1824
14592
16051,2
17510,4
Total:
18618
148944
163838,4
178732,8
Source: created by the authors
Similar trends are also apparent in the second area. Data
provided in Table 8 shows that changes in costs are relatively
small when production output is low. This enables a more stable
(3)
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