ISRN Renewable Energy

Volume 2012 (2012), Article ID 107397, 12 pages

doi:10.5402/2012/107397

Research Article

Economic Analysis of Biomass Supply Chains: A Case Study of Four Competing Bioenergy Power Plants in Northwestern Ontario

Md. Bedarul Alam, Reino Pulkki, Chander Shahi, and Thakur Prasad Upadhyay

Faculty of Natural Resources Management, Lakehead University, 955 Oliver Road, Thunder Bay, ON, Canada, P7B 5E1

Received 9 April 2012; Accepted 11 July 2012

Academic Editors: A. Bosio and O. O. Fasina

Copyright © 2012 Md. Bedarul Alam et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on green energy sources. This paper develops and applies two optimization models to analyze the impacts of biomass competition on cost structures and gross margins for four competing biomass-based power plants in northwestern Ontario. Model scenarios are run to study the impacts of changes in parameters relevant to biomass type and processing technology, and prices of inputs and outputs on procurement costs. Cost minimization model shows that per unit procurement costs are directly proportional to the size of the power plants in all scenarios. Profit maximization model, on the other hand, shows that FMUs that are closer to the power plants make higher gross margins. However, the margins significantly increase for FMUs that are close to the power plants potentially offering higher prices. The variations in costs and gross margin structures under various model scenarios are explained by location of depletion cells relative to power plants, availability of each type of biomass in depletion cells, biomass demands, and differential processing costs for two types of biomass. These results can aid decision makers to make improved decisions related to biomass supply chains for bioenergy production.

1. Introduction

Forest biomass has been recognized as an alternative energy source since it is renewable and CO₂ neutral [1]. However, renewable energy production from forest biomass faces many challenges due to uncertainty of its continuous supply [2–4]. The ever increasing demand of biomass for bioenergy production has enormously increased the transportation distances and costs for forest biomass procurement, which is spread over large geographical locations at varying distances from the power plant [1, 5]. Further, the optimal harvest schedules become complicated if more power plants compete for available biomass feedstock over given space and time. This suggests a need for developing and using optimization models to analyze such problems for improved decision making in bioenergy production.

Canada’s dependence on fossil fuel has changed in recent times, and forest biomass has become an important part of its energy picture, supplying about 4.7% of our primary energy demand, the second largest source of renewable energy after hydroelectricity [6]. A major application of bioenergy is found in the forest products industry of Canada. Beyond the forest industry, several independent power plants generate electricity from forest biomass in Canada. Currently, three major combined heat and power (CHP) plants in northwestern Ontario (NWO) are using forest biomass feedstock. These include Thunder Bay CHP plant (with annual feedstock demand of 730,000 green tonnes (gt)), Fort Frances CHP plant (800,000 gt), and Dryden CHP plant (480,000 gt). Atikokan power-generating station, another power plant in NWO, is currently being converted to use forest biomass feedstock instead of coal requiring 200,000 gt annually [7, 8]. When all these plants become fully operational, the power plant managers (buyers of biomass) will need to have a sustainable and cost effective supply of biomass, and the contractors (suppliers of biomass) would like to increase their gross margins (profits) from biomass supply operations. Therefore, the optimization of forest biomass feedstock supply needs to be addressed both with cost optimization and profit maximization points of views.

Forest harvest residue (FHR), which includes tops and branches and unmerchantable wood left after stand harvesting, and underutilized wood (UW), which includes unharvested tree species that are not commercially important for timber, are the two most common forms of forest biomass used for bioenergy. Further, there are numerous options for comminuting forest biomass and several trucking and loading options having varying costs. Optimizing biomass procurement is, therefore, a complex problem with several supply and demand constraints.

A number of computational techniques from heuristics to advanced optimization models have been developed to model location specific biomass procurement problems [9–14]. These studies focus on bioenergy plant location problems for widespread forest biomass applications. The supply network normally consists of a single bioenergy production plant with many biomass supply regions [15–17]. In this context, forest biomass transportation costs constitute the majority of forest fuel supply chain costs for energy production. Further studies in biomass procurement, therefore, focused on reducing overall transportation costs [18–21]. Linear programming (LP) models, which are a common technique in operations research, have been used to optimize transportation distances and costs for forest biomass procurement for energy production at regional levels [22, 23]. Forest biomass feedstock data have also been analyzed using geographic information systems (GISs) techniques [24, 25]. LP models have also been developed using GIS-based forest biomass data both for estimating the feedstock availability and for reducing transportation distances and costs [26–29]. However, all these studies have been conducted either in Nordic or in central European countries.

We found no such optimization models dealing with biomass supply competition among various power plants in the Canadian context with large contiguous wood supply areas. The purpose of this study is, therefore, to develop and apply optimization models to analyze the impact of interpower plant competition for the available biomass feedstock on cost structures and gross margins of each power plant in NWO. Specifically, the two models (one based on cost minimization and another on profit maximization) are developed. The cost minimization model is applied from the power plant managers’ (buyers) perspective, and the profit maximization model is applied from the biomass suppliers’ perspective. Several sensitivity analyses are run to study the impact of changes in parameters relevant to forest biomass processing technology, biomass types, and prices of inputs and outputs.

2. Methods

The flow chart in Figure 1 presents the methodology for developing the LP models for supplying forest biomass feedstock to the four power-generating stations. The study area, 324 km × 516 km (167,184 km²), consists of 18 FMUs west of Lake Nipigon in NWO. GIS data are collected from Land Information Ontario, Sustainable Forest Licence (SFL) holders, and consultant companies in the formats of Shapefile and Geodatabase [30–33]. The original vector data is first converted to raster and finally to spatial database text files for the entire research area using ArcGIS software. Three main spatial layers (land use, forest depletion, and cost layers) are prepared on a raster grid size of 1 km × 1 km (1 km²), where each cell represents a feature [34, 35]. Different data input methods are followed for different types of features. The dominant data input method is used to create a raster layer of forest land use class [35], where a grid code entity is assigned to each feature (productive forest, water/lake, and other land use), which occupies more than 50% of the cell area (detailed methodology of data input methods is provided by Alam et al. [36]). The percent occurrence method is used to prepare the depletion layer, where a code number is assigned to the grid cell depending on its depletion percentage. Before developing the marginal transport cost layer for each power plant, a raster layer of the road network for the research area is also developed using the presence/absence method of data input [35]. A grid code is assigned depending on the presence of different types of roads in a cell. The raster layer of the road network is converted to ASCII (.txt) format. A network optimization model is used to prepare the minimum cost layer for transporting forest biomass through the road network to the four power plants. The location of each power plant is used as the sink node during the data input and optimization processes. The vehicle considered for transporting forest biomass feedstock is a tractor with a 53-foot semitrailer (with belly). In Ontario the allowable legal payload weight for this type of tractor-trailer is taken as 40.55 tonnes (t). The charge-out rate for biomass truck with operator is assumed to be 85 $ ·h⁻¹. A fixed time for loading, unloading, and delay of 2.5 hours per trip is assumed. In this cost layer, the cost of transporting forest biomass from each road cell (1 km²) of the research area to the four power plants is established. Minimum cost zones (tessellations) for transporting forest biomass from each of the 100,061 forest cells to each of the four power plants are established in order to create biomass catchment areas for each power plant.

Figure 1: Flow chart of optimization models for supplying woody biomass feedstock to four major biomass based power plants in northwestern Ontario (NWO).

Two optimization models are developed for analyzing optimal supply of forest biomass from depletion cells to the four power plants. The objective function in the first model is to minimize the total biomass feedstock procurement costs and in the second model is to maximize total profit (gross margin) of FMUs for supplying forest biomass feedstock from the forest cells to power plants. Even though these two models give the same solutions, the relative impacts of changing biomass prices are not captured by the cost minimization model. Changing biomass prices on part of bigger power plants will change the optimal solutions, which is best handled by the profit maximization model. The results of profit maximization are more relevant to the contractors (biomass suppliers) in order to evaluate the profitability of their operations in the given locations (FMUs). The parameters, indices, and variables used for developing the two models using the General Algebraic Modeling System (GAMS) programming language are listed as follows.

Indices:

 : power plants (: 1 = Thunder Bay CHP plant, 2 = Fort Frances CHP plant, 3 = Atikokan generating station and 4 = Dryden CHP plant), : forest cell in the depleted forest area ().

Parameters:

 HF: harvesting factor, PR: processing (harvesting and grinding/chipping) cost ($gt⁻¹) of FHR at roadside,  PU: processing (harvesting and grinding/chipping) cost ($gt⁻¹) of UW at roadside,  : annual forest biomass demand (gt) of each power plant,  : theoretical availability (gt) of FHR in a forest cell over 7 years (2002–2009),  : theoretical availability (gt) of UW in a forest cell over 7 years (2002–2009), : annual technical availability (gt) of FHR in a forest cell, calculated using (1)

 : annual technical availability (gt) of UW in a forest cell, calculated using (2)

 : biomass transportation cost ($gt⁻¹) from the th forest cell to the th power plant including loading and unloading costs.  : price ($gt⁻¹) of forest biomass at the th power plant.

Variables:

 : amount of annual FHR harvested (gt) from the th forest cell for the th power plant,  : amount of annual UW harvested (gt) from the th forest cell for the th power plant, : annual forest biomass (gt) brought in the th power plant,  : total annual cost ($) of biomass procurement, calculated using (3)

 : total annual revenue ($) from forest biomass supply to power plants, calculated using (4)

 : total annual profit ($) from forest biomass supply to power plants, calculated using (5)

The objective functions of two LP models, which minimize overall procurement costs and maximize total profit, are specified by (6) and (7), respectively. The constraints used in these models are specified by (8) to (12).

subject to

Equation (8) is an intermediate equation summing up the plantwise total biomass being harvested. Equations (9) and (10) are the FHR and UW harvesting constraints respectively, which specify that the total amount of FHR and UW harvested from th forest cell is less than or equal to the annual amount of technically available FHR and UW in the th forest cell, respectively. Equation (11) is the demand constraint, which ensures that the total amount of forest biomass harvested for th power plant should at least be equal to biomass feedstock demand for the power plant. Finally, (12) is nonnegativity constraint on decision variables. The estimates of parameters used in the model are presented in Table 1.

Table 1: Estimates of parameters used in BASE scenario of the model.

Descriptions
Unit
Estimates
Remarks

Harvesting and processing costs of FHR
$·gt⁻¹
26
Forest BioProducts Inc. (2006) [7]

Harvesting and processing costs of UW
$·gt⁻¹
31
Forest BioProducts Inc. (2006) [7]

Fixed cost due to load/unload overhead
$·gt⁻¹
5.24
Own estimate

Charge rate of biomass truck
$·hr⁻¹
85
Alam et al. (2012) [36]

Biomass demand of ABTB CHP plant
gt·yr⁻¹
730,000
Power plant data

Biomass demand of ABFF CHP plant
gt·yr⁻¹
800,000
Power plant data

Biomass demand of AGS Power plant
gt·yr⁻¹
200,000
Power plant data

Biomass demand of DDPP CHP plant
gt·yr⁻¹
480,000
Power plant data

Range of availability of FHR
gt/km²
525.6–5,256
Alam et al. (2012) [36]

Range of availability of UW
gt/km²
0–4,730.4
Alam et al. (2012) [36]

Harvesting factor of FHR
% of FHR
61
Own estimate

Harvesting factor of UW
% of UW
61
Own estimate

Price of biomass at power plants
$·gt⁻¹
39
Own estimate

Number of depleted forest cells
No
19,315
Alam et al. (2012) [36]

FHR: forest harvest residue, UW: underutilized wood, gt: green tonne, hr: hour, yr: year, ABTB: Abitibi-Bowater Thunder Bay Power Plant, CHP: combined heat and power, ABFF: Abitibi-Bowater Fort Frances Power Plant, AGS: Atikokan generating station, DDPP: Domtar Dryden Power Plant.

Model Scenarios

Table 2 presents the definitions and descriptions of 14 cost minimization model scenarios with their relevancy in relation to the expected changes in technological and economic parameters in the model. Out of 14 scenarios, six scenarios (DEPC, IHRW1, IHRW2, and IHFR) are cost decreasing and seven scenarios (IHOR, DHRW, INPC, DEPC, INDP, DEDP, and INTC) are cost increasing. Table 3 shows the definitions and descriptions of eight different model scenarios for the profit maximization model. The results of this model are presented in terms of per unit gross margins for each FMU by scenarios.

Table 2: Definition and description of scenarios of cost minimization model.

Scenario
Definition/description

BASE
The business-as-usual scenario captures the present reality of study area. The parameters used in this scenario are as described in Table 1. This scenario helps compare the model results for the rest of the scenarios.

Economic scenarios

INTC
BASE, but 20% increase in biomass truck charge rate. This scenario tests the impact of change in transportation costs on the biomass procurement cost structures which might be the case in future as truck charge rate has recently been increased in NWO.

INPC
BASE, but 10% increase in harvesting and processing costs of FHR and UW. This tests the impacts of increased harvesting and processing costs, likely to occur due to changes in economic factors, on procurement cost structures.

DEPC
BASE, but 10% decrease in harvesting and processing costs of FHR and UW. This scenario reflects potential improvements in technology in future, thereby reducing the processing costs.

Technological scenarios

INBA
BASE, but 10% increase in availability of both FHR and UW.

DEBA
BASE, but 10% decrease in availability of both FHR and UW.

These scenarios test the sensitivity of loosening and tightening of biomass availability constraints. This will help us understand the changes in costs structures over the error margins (±10%) of present estimates of biomass availability for both types as the present estimates of these variables are based on samples of selected areas in FMUs of NWO, which might have the range of errors tested by these scenarios as the present estimates of biomass availability are based on selected sample forest cells. We feel that these estimates need to be improved in future work.

IHR W1
BASE, but 10% increase in harvesting factors of both FHR and UW.

IHR W2
BASE, but 20% increase in harvesting factors of both FHR and UW.

IHFR
BASE, 20% increase in harvesting factor of FHR and no change in harvesting factor of UW.

This set of scenarios tests the impacts of loosening the biomass availability constraints due to improved biomass harvesting technology, likely to happen in future.

DHR W
BASE, but 10% decrease in harvesting factors of both FHR and UW.

DHFR
BASE, but 20% decrease in harvesting factor of FHR and no change in harvesting factor of UW.

This set of scenarios tests the impacts of tightening the biomass availability, which might occur due to ecological and environmental concerns in future.

IHOR
BASE, but 20% increase in harvesting factor of FHR, and only FHR is extracted. This scenario explores the situation of increased procurement costs due to present common practice of harvesting only FHR for bioenergy production instead of harvesting both FHR and UW.

INDP
BASE, but 10% increase in woody biomass feedstock demand. This explores the impacts of higher biomass demand, likely to happen due to expansion of power plants in future, on cost structures of biomass procurement.

DEDP
BASE, but 10% decrease in woody biomass feedstock demand of each of the four power plants. This scenario helps understand the costs structures due to loosening of constraints on the biomass feedstock demand of power plants, which might be the case in future. Selection of higher quality biomass and plants not operating in full capacity leads to lesser biomass demands.

Table 3: Definition and description of scenarios of profit maximization model.

Scenario
Description

BASE
This is the business-as-usual scenario of the model which reflects the current field situation. The parameters for this scenario are described in Table 1. The results of other model scenarios are compared with that of this scenario.

INTC
BASE, but 20% increase in biomass truck charge rate.

This scenario tests the impact of change in transportation costs, which is likely situation in future on gross margin structures for each biomass supplying FMU, as truck charge rate has recently been increased in NWO.

IRTB1
BASE, but 10% increase in price of biomass feedstock by Thunder Bay plant.

IRTB2
BASE, but 20% increase in price of biomass feedstock by Thunder Bay plant.

IRFF1
BASE, but 10% increase in price of biomass feedstock by Fort Frances plant.

IRFF2
BASE, but 20% increase in price of biomass feedstock by Fort Frances plant.

IRTF1
BASE, but 10% increase in price of biomass feedstock by both Thunder Bay and Fort Frances plants.

IRTF2
BASE, but 20% increase in price of biomass feedstock by both Thunder Bay and Fort Frances plants.

This set of price increasing scenarios tests the sensitivity of different levels of prices of biomass feedstock to gross margin structures of biomass supplying FMUs. These changes in prices are likely to be offered on the part of bigger power plants as they require huge amounts of biomass to operate their power plants smoothly. Price increases are, therefore, assigned to two bigger power plants, Thunder Bay and Fort Frances, in these scenarios.

While selecting the model scenarios, first we focus on impacts of changing economic parameters (transportation and processing costs and prices) on cost and gross margin structures. Secondly, the impacts of changing technological parameters (harvesting factors and biomass demand and availability) are investigated.

3. Results and Discussion

3.1. Cost Zones

Minimum transportation cost zones (tessellations) for transporting forest biomass from each forest cell of depleted forest area to the four power plants are shown in Figure 2. These minimum cost zones show the forest biomass catchment areas for each power plant. The minimum cost zone for the Dryden CHP plant is the largest, while the Thunder Bay CHP plant catchment area is the second largest among the four cost zones. The Dryden CHP plant is located near the middle of the research area, and there are many forest cells closer to this power plant along with a denser network of higher class and straighter roads. There is no competition for the Dryden CHP plant for forest biomass supply from any other power plant in the research area on the northern side. Similarly, Thunder Bay CHP plant is located in the southeastern part of research area. There is no competing power plant on its northern, eastern, and southern sides in the research area. The other two power plants, Atikokan and Fort Frances, which are located close to each other in the research area, might compete for the same forest biomass from some depletion cells depending upon their requirements. The cost zone map in Figure 2 shows the relative cost competitive areas for each power plant based on minimum transportation cost for each power plant from each forest cell. As more attributes (biomass density and economic) are introduced in the forest cells, the dynamics of competition among the four power plants would change.

Figure 2: Minimum cost zones for four power plants in NWO.

3.2. Cost Analysis

The results of the cost minimization model provide an optimal solution for supplying forest biomass feedstock from forest cells to the four power plants on an annual basis by minimizing the total annual procurement (harvesting, processing, and transportation) costs of biomass, subject to the availability of forest biomass in each depleted forest cell and demand of each power plant. The model selects 11,790 and 2,991 cells in total for supplying FHR and UW, respectively, for all four plants. The more FHR cells are selected because the processing cost for FHR is less as compared to UW forest biomass. Figure 3 represents the distribution of optimal depletion forest cells that supply FHR and UW biomass to each power plant in the BASE scenario. Similar distributions of optimal depletion cells for different model scenarios can be produced; these are not reproduced here because of space limitations.

Figure 3: Selected forest cells to harvest forest harvest residues (FHRs) and underutilized wood (UW) for four power plants (BASE scenario).

First, we discuss the results relating to the impacts of changing economic parameters (transportation and processing costs and prices) on cost structures. Results of the impacts of changing technological parameters (harvesting factors and biomass demand and availability) are discussed next. The number of cells selected for supplying FHR and UW forest biomass for each power plant, in each model scenario, is presented in Table 4. While observing number of selected cells in Table 4, we find more depletion cells for FHR in all scenarios for all plants, but lesser amount of biomass from FHR type is being harvested (Table 5) in most of the scenarios. This variation is explained by the fact that, on an average, the availability of FHR is about one-fourth of UW biomass type. The source of the information is the input dataset of the model.

Table 4: The number of forest cells selected in different scenarios for supplying woody biomass to four power plants.

Scenario
Cells for ABFF (no.)
Cells for ABTB (no.)
Cells for DDPP (no.)
Cells for AGS (no.)
Total cells for 4 plants (no.)

FHR
UW
FHR
UW
FHR
UW
FHR
UW
FHR
UW
Total

BASE
3204
1320
3972
1004
3507
404
1107
263
11790
2991
14781

INTC
3127
1338
3612
1093
3213
473
1034
283
10986
3187
14173

INPC
3239
1312
4152
950
3695
355
1142
249
12228
2866
15094

DEPC
3178
1327
3775
1056
3349
442
1055
276
11357
3101
14458

INBA
3138
1139
3816
860
3388
316
1135
197
11477
2512
13989

IHR W1
3138
1139
3816
860
3388
316
1135
197
11477
2512
13989

IHR W2
3090
981
3680
748
3285
242
1143
151
11198
2122
13320

IHFR
3164
1155
3815
853
3334
279
1120
193
11433
2480
13913

DEDP
3133
1120
3808
844
3373
307
1138
190
11452
2461
13913

DEBA
3257
1555
4157
1162
3655
513
1097
328
12166
3558
15724

DHR W
3257
1555
4157
1162
3655
513
1097
328
12166
3558
15724

DHFR
3219
1501
4142
1152
3729
536
1086
325
12176
3514
15690

IHOR
6726
—
6197
—
3728
—
1849
—
18500
—
18500

INDP
3254
1531
4132
1148
3642
502
1098
320
12126
3501
15627

ABFF: Abitibi-Bowater Fort Frances Power Plant, ABTB: Abitibi-Bowater Thunder Bay Power Plant, DDPP: Domtar Dryden Power Plant, AGS: Atikokan Generating Station, FHR: forest harvest residue, UW: underutilized wood.

Table 5: Annual amount of forest harvest residue and underutilized woody biomass supply to four power plants.

Scenario
Biomass for ABFF (ODt·yr⁻¹)
Biomass for ABTB (ODt·yr⁻¹)
Biomass for DDPP (ODt·yr⁻¹)
Biomass for AGS (ODt·yr⁻¹)

FHR
UW
FHR
UW
FHR
UW
FHR
UW

BASE
194,513
285,487
215,014
222,986
197,421
90,579
62,465
57,535

INTC
190,401
289,599
194,953
243,047
182,339
105,661
57,920
62,081

INPC
196,228
283,772
227,051
210,949
208,057
79,943
64,900
55,100

DEPC
192,864
287,136
203,419
234,581
189,287
98,713
59,541
60,459

INBA
209,309
270,691
227,206
210,795
209,525
78,475
71,251
48,749

IHR W1
209,309
270,691
227,206
210,795
209,525
78,475
71,251
48,749

IHR W2
225,238
254,762
237,835
200,165
222,566
65,434
78,718
41,282

IHFR
230,481
249,519
247,802
190,199
225,040
62,960
76,442
43,558

DEDP
189,924
242,077
206,110
188,090
189,754
69,446
65,021
42,979

DEBA
177,857
302,144
204,864
233,136
185,375
102,625
55,427
64,573

DHR W
177,857
302,144
204,864
233,136
185,375
102,625
55,427
64,573

DHFR
156,204
323,796
181,324
256,676
168,648
119,352
48,631
71,369

IHOR
480,000
—
438,000
—
288,000
—
120,000
—

INDP
197,481
330,519
226,116
255,684
204,956
111,844
61,695
70,305

ABFF: Abitibi-Bowater Fort Frances Power Plant, ABTB: Abitibi-Bowater Thunder Bay Power Plant, DDPP: Domtar Dryden Power Plant, AGS: Atikokan Generating Station, FHR: forest harvest residue, UW: underutilized wood.

Table 5 describes the distribution of biomass (both FHR and UW) amount being harvested from the optimal depletion cells for each plant under different model scenarios. The variations in number of depletion cells (Table 4) and biomass amount (Table 5) being selected by power plants are explained by amount of biomass requirement for each plant, availability of FHR and UW in each cell, relative processing costs of each biomass type, and the competition among the power plant for the biomass in the given cell. Further, these variations in distribution of optimal depletion cells and amount of biomass for both types explain the per unit biomass procurement cost structures for each power plant under different model scenarios (Table 6).

Table 6: Per unit procurement cost of woody biomass supply to four power plants.

Scenario
Unit procurement cost of biomass supply ($·ODt⁻¹)

ABFF
ABTB
DDPP
AGS

BASE
65.64
64.49
61.20
60.76

INTC
76.81 (17.03%)
75.37 (16.87%)
71.31 (16.52%)
70.39 (15.85%)

INPC
70.45 (7.34%)
69.24 (7.37%)
65.79 (7.51%)
65.47 (7.75%)

DEPC
60.81 (−7.34%)
59.72 (−7.40%)
56.57 (−7.56%)
56.03 (−7.79%)

INBA
65.26 (−0.58%)
64.17 (−0.49%)
60.86 (−0.54%)
60.21 (−0.90%)

IHR W1
65.26 (−0.58%)
64.17 (−0.49%)
60.86 (−0.54%)
60.21 (−0.90%)

IHR W2
64.89 (−1.13%)
63.89 (−0.93%)
60.57 (−1.03%)
59.86 (−1.48%)

IHFR
65.08 (−0.85%)
64.01 (−0.74%)
60.61 (−0.95%)
60.02 (−1.22%)

DEDP
65.21 (−0.64%)
64.14 (−0.54%)
60.83 (−0.60%)
60.17 (−0.97%)

DEBA
66.11 (0.72%)
64.86 (0.57%)
61.58 (0.63%)
61.23 (0.77%)

DHR W
66.11 (0.72%)
64.86 (0.57%)
61.58 (0.63%)
61.23 (0.77%)

DHFR
66.26 (0.96%)
65.02 (0.82%)
61.85 (1.07%)
61.46 (1.16%)

IHOR
70.41 (7.27%)
68.07 (5.56%)
67.30 (9.97%)
63.14 (3.93%)

INDP
66.06 (0.65%)
64.82 (0.52%)
61.55 (0.58%)
61.18 (0.70%)

ABFF: Abitibi-Bowater Fort Frances Power Plant, ABTB: Abitibi-Bowater Thunder Bay Power Plant, DDPP: Domtar Dryden Power Plant, AGS: Atikokan Generating Station; figures in brackets indicate the % change of per unit biomass procurement costs from the BASE scenario.

The 13 cost minimization model scenarios analyze the impacts of changing relevant economic (three scenarios) and technological (10 scenarios) parameters (as described in Table 2) on biomass procurement costs structures of the four power plants as shown in Table 6. The results of these scenarios are compared with that of the BASE scenario. The results in Table 6 show that per unit costs for Fort Frances, Thunder Bay and Dryden CHP plants, and Atikokan power plant are in descending order for all model scenarios, which indicate that the higher the biomass demand, the higher the per unit procurement cost. The reason for this is that power plants with more biomass demand have to collect biomass from much longer distances. The range of unit procurement cost ($ODt⁻¹) for Fort Frances, Thunder Bay and Dryden CHP plants, and Atikokan power plant is 60.81–76.81, 59.72–75.37, 56.57–71.31, and 56.03–70.39, respectively. This result shows that the highest per unit procurement cost (for all power plants on average a 16.77% increase from the BASE scenario) occurs with a 20% increase in transportation cost (INTC scenario). The lowest per unit procurement costs occurs with a 10% decrease in processing cost scenario (DEPC scenario). A decrease in processing cost by 10% resulted in a 7.44% decrease in per unit procurement costs for all power plants on average. Since transportation and processing costs form a major component of biomass supply chain, changes in these parameters have major impacts on per unit procurement cost for all power plants. These sensitivity results imply that policy should focus first on controlling transportation costs and then processing costs. The processing cost includes both productive machine time and idle time costs. The productive machine time can be improved by investing in improving the processing technology, and the idle time can be reduced by better scheduling of operations. Improving road network and vehicle configuration for hauling biomass can reduce the transportation costs. Consequently, one can achieve reduced procurements costs in biomass value chains with these improved measures.

After analyzing the sensitivities of changing economic parameters to the model solutions, we investigate further the impacts of changing relevant technological parameters in order to evaluate the resulting cost structures due to potential changing conditions of these parameters in future. In model scenarios INBA, IHRW1, IHRW2, IHFR, and DEDP, the per unit procurement costs decreased from the BASE scenario for all power plants with the highest decrease at the Atikokan power plant (Table 6). On the other hand, under the model scenarios DEBA, DHRW, DHFR, IHOR, and INDP the per unit procurement costs increased from the BASE scenario for all power plants with the highest increase at the Atikokan power plant. The explanation for these higher changes in per unit procurement costs for the Atikokan power plant is that it has the lowest biomass demand compared to other power plants; it is located near the favorable depletion cells and biomass availability (Figures 2 and 3). However, in IHOR (only FHR biomass with 20% more harvesting factor) scenario, the increase in per unit cost for Atikokan power plant is the least compared to others. Although more FHR biomass is available in the IHOR scenario, there is higher competition for this type of biomass because UW is not being collected, and hence there is an increase in per unit cost compared to the BASE scenario for all power plants. In this scenario, the Atikokan power plant has least increment in per unit cost due to its lower demand for biomass relative to the other power plants. After the Atikokan plant, Fort Frances, the biggest power plant, shows higher increases and decreases in per unit procurement costs for most of the scenarios (Table 6), albeit with small variations among the plants by scenarios. The more interesting results are found in the IHOR scenario, where Dryden CHP plant has the highest increase (9.97%) in per unit cost compared to the BASE scenario. Further, the aggregate impacts of the economics and technological model scenarios are reflected in Figure 4, where we present the power plantwise total biomass procurement costs by scenarios. The bars in Figure 4 contrast with the results of per unit costs by scenarios. For example, the highest total costs for each power plant are shown in the INDP scenario, whereas the highest per unit cost is found in the INTC scenario.

Figure 4: Total annual procurement cost of woody biomass supply to four power plants. ABFF: Abitibi-Bowater Fort Frances Power Plant, ABTB: Abitibi-Bowater Thunder Bay Power Plant, DDPP: Domtar Dryden Power Plant, AGS: Atikokan Generating Station.

Increasing harvesting factors causes more declines in per unit procurement costs suggesting that the present harvesting factor level needs to be increased for reduced per unit procurement cost. This is possible given that the depletion cells being used in this study are only from productive forest area, and it excludes the ecologically sensitive areas in NWO. Further, IHOR scenario showing us the highest per unit procurement cost, which suggests current emphasis on collecting only FHR for bioenergy purpose, would lead to higher per unit procurement cost. This, therefore, suggests for harvesting standing underutilized tree species to reduce per unit procurement cost.

3.3. Profit Analysis

The results of the profit maximization model provide an optimal solution for supplying forest biomass feedstock from forest cells to the four power plants on an annual basis by maximizing the total gross margin, subject to the availability of forest biomass in each depleted forest cell and meeting the biomass demand of each power plant. These results of gross margin structures are relevant from the biomass supplier’s (contractors) perspective as these are based on biomass supplying FMUs (Table 7), where the contractors operate their business. The results of per unit profit ($ODt⁻¹) for each FMU that supplies biomass to the four power plants in the BASE and seven other scenarios are shown in Table 7. The profit for any FMU depends on the price of biomass being offered by the power plants. In the BASE scenario, most of the FMUs make positive profit.

Table 7: Per unit profit ($·ODt⁻¹) for each FMU from supplying biomass in different scenarios of profit maximization model.

Scenario
Per unit profit ($·ODt⁻¹) for each FMU

BS
CR
DM
DR
ER
KE
LS
LN
LH
SA
SR
WA
WJ

BASE
0.73
0.21
0.44
3.67
1.98
−1.01
3.61
−0.40
0.80
4.47
0.10
3.94
3.05

INTC
−0.74
−0.94
0.28
2.66
0.56
−2.00
2.17
−2.71
−0.58
−0.21
−1.85
−0.30
−1.32

IRTB1
7.23
0.21
6.33
3.67
2.03
−1.01
3.61
6.10
7.31
4.47
6.60
3.94
3.05

IRTB2
13.73
0.21
12.22
3.67
2.10
−1.01
3.61
12.60
13.81
4.47
13.10
3.94
3.05

IRFF1
0.73
5.63
0.45
3.67
1.98
4.15
3.61
−0.40
0.80
4.48
0.10
4.19
3.87

IRFF2
0.73
11.05
0.45
3.67
1.98
9.30
3.61
−0.40
0.80
4.50
0.10
4.44
4.68

IRTF1
7.23
5.63
6.33
3.67
2.03
4.15
3.61
6.10
7.30
4.48
6.60
4.19
3.87

IRTF2
13.73
11.05
12.23
3.67
2.09
9.30
3.61
12.60
13.80
4.50
13.10
4.44
4.68

Note: BS: Black Sturgeon Forest; CR: Crossroute Forest; DM: Dog River-Matawin Forest; DR: Dryden Forest; ER: English River Forest; KE: Kenora Forest; LS: Lac Seul Forest; LN: Lake Nipigon Forest; LH: Lakehead Forest; SA: Sapawe Forest; SR: Spruce River Forest; WA: Wabigoon Forest; WJ: Whiskey Jack Forest.

Although 19,315 depletion cells used in this study are from 18 FMUs, the optimal depletion cells selected by the model fall only in 13 FMUs for all the scenarios. In the BASE scenario, only the Kenora and Lake Nipigon FMUs show negative gross margins (Table 7) as the optimal depletion cells falling in these FMUs are relatively farther away from the power plants (Figure 2). While testing the sensitivity of increasing truck charge rate by 20% (INTC scenario), only four FMUs (Dog River-Matawin, Dryden, English River, and Lac Seul FMUs) make positive profit. These results suggest that, in future, contractors operating in most of the FMUs will face negative gross margins, thereby halting the biomass supply hindering smooth operation of the power plants. We, therefore, tested the sensitivity of increased per unit price on part of the larger power plants to the resultant gross margin structures of biomass supplying FMUs. For this purpose, a combination of price increase scenarios at the two larger power plants (Fort Frances and Thunder Bay) is investigated. For an increase in price by 10% (IRTB1) and 20% (IRTB2) at the Thunder Bay CHP plant, the per unit profit increases by 148% and 296% on average for all FMUs, respectively. The FMUs, which are located near the Thunder Bay CHP plant (Black-Sturgeon Forest, Dog River-Matawin Forest, English River, Lakehead Forest, Spruce River Forest, and Lake Nipigon Forest), make more per unit profit from an increase in price at the Thunder Bay CHP plant (ranging from 3.82 to 7.32 $ODt⁻¹ in IRTB1 and from 10.32 to 13.82 $ODt⁻¹ in IRTB2) because the transportation costs from these FMUs to this plant are low. Similarly, for an increase in price by 10% (IRFF1) and 20% (IRFF2) at the Fort Frances CHP plant, the average per unit profit of FMUs increases by 54% and 108% on average for all FMUs, respectively. Finally, if there is an increase in price of forest biomass at both the Thunder Bay and Fort Frances CHP plants by 10% (IRTF1) and 20% (IRTF2), the average per unit profit of FMUs increases by 202% and 404%, respectively. Therefore, an increase in price of forest biomass at a power plant greatly helps to increase the profits of close by FMUs but only marginally helps to increase the profits of faraway FMUs. It is, therefore, advisable to put smaller power-plants in a wider range of area in order to make biomass collection a profitable business for FMUs. This will also help in reducing the distribution cost of power-to-small communities and increasing the rural employment through bioenergy production systems in the region.

4. Conclusions

This study investigates an optimal biomass supply chain for four large-scale biomass-based power plants in NWO, which has been a priority research area recently due to greater emphasis put on green energy sources in Canada and around the globe. Two optimization models have been developed and applied to analyze the impacts of biomass competition, likely to be created due to increasing demand for biomass feedstock on part of the four power plants in this region, on cost and gross margin structures for biomass based power plants. Twenty-two model scenarios (14 for cost minimization and 8 for profit maximization model) are used to study the impacts of changes relevant to economic and technological parameters of biomass procurement for each power plant and FMUs. The results show that per unit procurement costs are directly proportional to the size of the power plants in all scenarios. The highest percent increase in per unit biomass procurement costs from the base scenario is found in the increased truck charge rate by 20% scenario, followed by the scenario where only forest harvest residue biomass is used by all power plants.

The results from increased truck charge rate and FHR only scenarios point to some of the important policy implications for biomass supply chain management in NWO. The policy should focus more on controlling transportation costs by investing in transport infrastructure and logistics and rethinking of current emphasis on collecting only FHR biomass for bioenergy purpose in order to reduce the biomass procurement costs. Further, harvesting standing underutilized tree species reduces per unit procurement cost. Results from other scenarios are also important and interesting to evaluate the impacts of changing economic and technological parameters on cost structures for designing the improved decision-making strategies for power plants.

On the other hand, the results from profit maximization model are more useful to the biomass suppliers (contractors), who are interested in making maximum profits in supplying forest biomass feedstock to power plants from given FMUs. They can maximize their profits from FMUs that are closer to the power plants. However, their profits significantly increase if the power plants offer higher prices. This is possible only if the suppliers meet the quality standards and lead time requirements of the buyers. The variations in costs and gross margin structures under various model scenarios are explained by location of depletion cells relative to power plants, availability of each type of biomass in depletion cells, biomass demands, and differential processing costs for two types of biomass. This modeling framework can be applied elsewhere to study the similar problem of biomass supply chain. The results of such modeling can help decision makers make improved decisions relating to biomass supply chains for bioenergy production.

Acknowledgments

The authors are grateful that the Ontario Ministry of Energy and Infrastructure through the Ontario Centres of Excellence, NSERC Strategic Network on VCO, Abitibi-Bowater Inc., and Thunder Bay Community Economic Development Commission funded this research. They are thankful to Lakehead University, Confederation College, FPInnovations FERIC Division, Lac Des Mille Lac First Nation, Buchanan Group, and Ontario Ministry of Natural Resources for further support.

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