The moderating role of biomass availability in biopower cofiring d A sensitivity analysis

Journal of Cleaner Production 135 (2016) 523e532

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Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro

The moderating role of biomass availability in biopower co-firing d A
sensitivity analysis
Zuoming Liu a, *, Thomas G. Johnson b, Ira Altman c
a

Lynchburg College, Department of Management, School of Business and Economics, 1501 Lakeside Drive, Lynchburg, VA 24501-3113, USA
University of Missouri-Columbia, Department of Agricultural Economics, 215 Middlebush Hall, Columbia, MO 65211, USA
c
Southern Illinois University-Carbondale, Department of Agribusiness Economics, Mail Code 4411, 1205 Lincoln Drive, Carbondale, IL 62901, USA
b

a r t i c l e i n f o

a b s t r a c t

Article history:
Received 16 October 2015
Received in revised form
21 May 2016
Accepted 17 June 2016
Available online 19 June 2016

Of the various types of renewable energy technologies being promoted in response to concerns about
climate change and energy security, co-firing biomass for electricity is one that is potentially feasible in

many states and regions of the USA. This study contributes to our understanding of the factors that
influence the economic feasibility of this technology. Using a recently developed spatial evaluation tool
we perform sensitivity analyses to investigate how the cost of co-firing biomass is affected by power
plant scale, level of biomass used as feedstock, local feedstock availability, transportation costs, and
resource and harvesting costs. Specifically, we demonstrate the use of this tool by exploring the cost of
co-firing biomass in existing qualified coal-fired power plants in Missouri.
We find that the cost of electricity generated is higher when biomass is cofired under all assumption.
However, it finds significant and interesting interaction among the cost-related features. We are able to
conclude that abundant and reasonably-priced biomass feedstocks can dramatically increase the feasibility of biopower by reducing transportation costs. Also, the scale of the technology must be rightdlarge
enough to exploit economies of scale but small enough to avoid high transportation costs incurred to
procure large volumes of feedstocks.
© 2016 Elsevier Ltd. All rights reserved.

Keywords:
Biomass
Co-firing
Biopower
Linear programming
Sensitivity analysis

1. Introduction
Two days before the United Nations summit on climate change
on September 21st 2014, one of the largest ever climate-change
demonstrations, estimated to involve more than 300,000 people,
took place in the streets of New York City (USA Today, 2014). Large
protests were held in other locations as well. These demonstrations sent a strong message that more and more people are concerned about climate change. On the other hand, given the world's
overwhelming dependence on low-cost fossil fuels, there are also
concerns about the possible damage to the economy that switching from fossil fuels to renewable energy could cause. In early
September 2014, a report entitled “Better Growth, Better Climate:
The New Climate Economy Report”, was released by the Global

Commission on the Economy and Climate. The Commission
included more than 100 politicians, leaders, economists and other

* Corresponding author.
E-mail addresses: (Z. Liu),
(T.G. Johnson), (I. Altman).
/>0959-6526/© 2016 Elsevier Ltd. All rights reserved.

scientists from seven countries. The report argued that it is
possible to reduce the risk of climate change while achieving
economic growth (GCEC, 2014).
Despite recent dramatic increases in the production of domestic
oil and natural gas, concerns about energy sustainability and security continue to be raised (WEC, 2007; EIA, 2013). In June 2014,
the U.S. Environmental Protection Agency (EPA) proposed guidelines designed to reduce the national level of CO2 emissions from
power plants by 30% from 2005 levels by 2030. Strategies to reach
this goal will be developed and executed at the state level, and each
state is required to submit CO2-reduction plan by 2016 (EPA, 2014).
A study by the University of Massachusetts Political Economy
Research Institute (PERI) and Center for American Progress in
September 2014 declared that 40% of 2005 levels of carbon pollution could be eliminated, and 2.7 million jobs related to clean energy could be created at the same time (Pollin et al., 2014).
In response to these findings, more and more research is being
undertaken to find clean, safe and renewable energy sources to
complement or even replace fossil fuels. Biomass-based energy
(bioenergy) has significant appeal as a partial replacement for fossil
fuels because it is renewable, emits less carbon into the


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Z. Liu et al. / Journal of Cleaner Production 135 (2016) 523e532

atmosphere, is potentially more environmentally benign, is easier
to procure and store, and is almost ubiquitous. Biopower is one
popular use of biomass with better energy utilization than biofuels
(Mizsey and Racz, 2010). Biopower technology offers local benefits
as a way of disposing residues and wastes, and global benefits by
reducing greenhouse emissions (Yusoff, 2006). A great deal of
research has focused on the technical aspects of biopower production such as optimum oxygen factors, air temperature, air-fuel
ratio, operating pressure, biomass particle size, pressure, etc. Bioenergy research and practitioners have confirmed that co-firing
biomass in existing plants, especially coal-powered plants, is a
technically feasible option (Ponton, 2009). While biomass residues
can replace more than 50% of coal in coal-fired plants with large
capital investments (English et al., 1981), up to 20% biomass can be
co-fired with coal without significant modification to current
equipment (Grabowski, 2004; Haq, 2002). Biomass use must to be
managed very carefully to avoid decreased boiler efficiency
(English et al., 2007; English, 2010) and boiler corrosion. In this
article, we focus on 10% and 15% biomass co-firing levels and
analyze the impacts of non-technical factors such as fuel availability
and transportation costs on the feasibility of biopower generation
in the Midwestern U.S. state of Missouri. Specifically, we conduct
sensitivity analyses of varying levels of biomass availability, transportation costs and biomass resource and harvesting costs on the
economic feasibility of co-firing in existing coal-powered plants in
Missouri.
In Missouri, about 90% of the total electricity supply comes from
investor-owned plants. Based on data from the U.S. Energy Information Administration (EIA, 2014), in 2013, 83% of Missouri's
electricity generation came from coal compared to the national
average of about 45%. Another 9% of electricity was supplied by
nuclear power, mainly from the Callaway Nuclear Generating Station, and about 3% of electricity generation came from renewable
energy resources, with about 95% of that from conventional hydroelectric power and wind. Only a small portion of electricity was

generated from biomass, mainly at two low-capacity biopower
plants, the University of Missouri (18 Megawatts or MW) and
Anheuser Busch St. Louis (26 MW) (EIA, 2014). However, given
Missouri's abundant biomass resources from agriculture and
forestry sectors, there is significant potential for more biopower
production. As a major agricultural state, with large quantities of
crop residues and promising prospects for energy crops, as well as
large areas of productive forests, Missouri produces vast amounts of
biomass each year, some of which could be used for biopower
generation. The Missouri Department of Natural Resources has
estimated that 172,550,603 megawatt hours (MWh) could be produced annually. This is almost twice the total electricity produced
in Missouri in 2009 (Fink and Ross, 2006). Although biomass
feedstocks can only be partially collected and used, they nevertheless offer great potential for increased renewable energy generation and reductions in carbon emissions within the state.
In 2008, Missouri adopted a renewable portfolio standard (RPS),
requiring investor owned electric utilities to increase their use of
renewable energy sources to 15% by 2021. With proposed guidelines from the U.S. EPA in 2014 to reduce the national level of CO2
emissions from power plants 30% by 2030, it is imperative for the
power plants in the state to diversify their fuel mix by including
more renewable energy resources. Co-firing biomass in existing
coal-powered plants can help the owners meet the RPS requirements and can be an incremental way of reducing the emission of greenhouse gas and other pollutants. It is in this context that
this study investigates the role of several factors in shaping the
economic feasibility of biomass co-firing in Missouri, with the aim
of identifying the most critical factors determining the ideal locations, scales, and feedstocks for power generation in Missouri. The

tool and method employed in this analysis can be adapted to any
state or region contemplating an increase in biopower capacity.
2. Literature review
Compared with traditional fossil fuels, the supply fluctuations
and low energy density features of biomass feedstocks are major
deterrents for large-scale biopower generation (Akhtari et al.,

2014). Biopower plants usually have small capacities, typically
one-tenth the size of coal-fired plants, due to the limited availability of local feedstocks (IEA , 2007). Due to region-specific variations in feedstock, transportation costs and many other economic
parameters in biopower generation are not known with certainty,
and the cost of this process varies across regions (Schneider and
McCarl, 2003). So conducting a sensitivity analysis over a wide
range of cost assumptions has important practical implications.
Detailed information regarding the forces that impact the
feasibility of biopower production is useful for industry strategists,
policy makers, and bioenergy entrepreneurs. As a result, many
national and regional level studies have been conducted to assess
the economic feasibility and/or environmental consequences
involved in using bioenergy. Given the inevitable uncertainty
involved in locating a new facility, sensitivity analysis is a useful
tool for identifying the most critical factors to consider.
Sensitivity analysis has been widely employed in environmental
and biomass related fields. Mathieu and Dubuisson (2002) simulated the process of wood gasification in the ASPEN PLUS process
simulator based on the Gibbs free energy minimization, and conducted a sensitivity analysis on various factors regarding their effects on process efficiency, such as oxygen factors, air temperature,
oxygen content in air, operating pressure and the injection of
steam. Bettagli et al. (1995) calculated the gas composition under
alternative operating conditions using a model to simulate the
chemical kinetics of gasification and combustion processes. In their
study, they performed a sensitivity analysis to evaluate the influence of the major parameters involved, such as temperature,
pressure, and air-fuel ratio on the composition of the exit gas.
Schuster et al. (2001) used thermodynamic equilibrium calculations to simulate a dual fluidized-bed steam gasifier with a
decentralized system that combined heat and power. They conducted a sensitivity analysis of the process for a wide range of fuel
composition levels and various operating parameters, and found
that the most significant factors that determine the chemical efficiency of the gasification are gasification temperature and fuel
oxygen content. Another study regarding biomass gasification in a
fluidized bed by Lv et al. (2004) involved a sensitivity analysis to
investigate how the gas quality is influenced by many technical

factors including temperature, steam to biomass ratio, biomass
particle size, gas yield, steam decomposition, heating value, etc.
Their results indicate that a tradeoff exists between hydrogen
production and gas heating value as temperature changes, and that
optimal steam level and small size of particles can improve gas
quality. Sadaka et al. (2002) built a two-phase biomass gasification
model and conducted sensitivity analysis to test the model's
response to alternative operating parameters (fluidization velocity,
steam flow rate and biomass to steam ratio). The analysis showed
that all operating parameters impact the model performance, and
that the steam flow rate has a larger impact on the reactor's temperatures than the other two parameters.
Although there are many biomass-related sensitivity analyses,
most focused on the impacts of various technical factors, such as air
temperature, oxygen content, operating pressure, etc. There are
very few studies that investigate how the performance of biopower
is related to non-technical, economic factors, such as input costs
and electricity prices involved in biopower generation. Dornburg


Z. Liu et al. / Journal of Cleaner Production 135 (2016) 523e532

and Faaij (2001) analyzed the energetic and economic performance
of various bioenergy systems with respect to energy savings
compared with fossil energy. The performance was studied for a
number of capacity scales using parameters such as cost of investment and prices of heat and electricity. They varied these parameters to determine how and to what extent the performance
results are influenced. Another sensitivity study was performed by
Monge et al. (2014) to study the impact of capital and operating
expenses on the feasibility of using three types of biofuel technologydHydrolysis, Pyrolysis, and Gasification. They also performed a sensitivity analysis of biofuel conversion yields on
feasibility, and discount rate on net present value of returns. Bazmi
et al. (2015) built a decentralized energy generation optimization

model to study an energy generation system using palm oil which
considered various costs such as biomass acquisition, operation,
capital, transportation, as well as electricity transmission costs.
Sensitivity analyses have also been conducted to determine the
role of cost and location related factors. Most of these have been
focused on biofuels (see for example, Jain et al., 2010; Wright et al.,
2010). Other sensitivity analyses have studied the factors affecting
the feasibility of direct incineration of biomass for electrical production with most of these focusing on forest residues, and dedicated biomass crops in smaller scale plants (see for example
Cucchiella et al., 2015; Hacatoglu et al., 2011; Thakur et al., 2014).
However, under the pressure and guidelines of RPS and EPA
regarding the increase of renewable energy use and reduction of
CO2 emission, converting coal-fired power plants to co-fire plants
for most states would be a good starting attempt without large
capital commitment.
3. Motivation of the study
The basic motivation of this study is to identify the impact of key
economic factors in biopower generation, and provide useful information to guide investors as they make decisions regarding the
location and size of biopower investments. Many factors involved
in biopower production affect the competitiveness of biopower
evis conventional coal-fired generation. Technological advisea
vances in biopower production could significantly change the cost
structure of producing biopower in the long run, but in the short
term factors such as biomass availability, transportation costs,
capital costs, economies of scale, etc. determine the competitiveness of biopower. In this article, we conduct several sensitivity
analyses to test how changes in key economic factors impact the
production costs of co-firing biomass in existing coal-powered
plants. Specifically, the influence of biomass feedstock availability,
transportation costs, and biomass resource and harvesting costs
will be investigated.
Although bioenergy is one of the largest sources of renewable

energy, most biomass resources are widely distributed. At present,
biomass is relatively costly to collect, store and transport, especially
in view of its low energy density (Akhtari et al., 2014). Traditional
fossil fuel suppliers have developed cost-effective supply chains
and logistics processes while most biomass markets have yet to
fully develop. Moreover, the highly seasonal nature of many types
of biomass requires extra effort and expenditures to maintain the
continuity of feedstock supply. Solutions to the supply continuity
issue include such strategies as diversifying the portfolio of biomass
feedstocks or increased storage capacity to even out the supply
fluctuations. Transportation costs are another critical factor in
biopower generation. The bulky nature and low-energy density of
biomass feedstocks make transportation costs one of the major
obstacles in biopower generation (Gold and Seuring, 2011). High
transportation costs resulting from long hauling distances often
prevent biomass from becoming a feasible feedstock. Generally

525

speaking, it is not economically viable to haul biomass fuels over
100 miles (Bechen, 2011). As Shakya (2007) noted, the locations of
most existing biomass power plants are in places where abundant
cheap biomass feedstocks exist or where biomass residues may
otherwise incur disposal costs, such as in sugar milling, wood factories and paper mills. The third key factor included in our sensitivity analysis is the cost of the biomass itself. The cost of the
biomass feedstocks to the power plant includes the in situ opportunity cost of the feedstocks plus the cost of harvesting.
This study analyzes biomass co-firing in Missouri. The analysis
in this study contributes to the literature by exploring the interacting impacts of biomass feedstock availability and key cost factors
on the total cost of producing electricity in conventional coal-fired
power plants. It is complementary to most previous studies which
focus on the feasibility of technology-related factors and dedicated

biomass power plants. This study also takes an explicitly placebased approach in which the local conditions ultimately determine the feasibility of co-firing biomass in any particular location.
The method developed for this article will allow policy makers,
bioenergy industry developers and entrepreneurs to determine the
feasibility of co-firing biomass given any location's agronomic, climatic, geographic and transportation infrastructure characteristics.
4. Methodology and data
This study employs a linear programming (LP) model developed
by Liu et al. (2014) to conduct the sensitivity analyses regarding the
impacts of variations in the availability of biomass feedstocks,
transportation costs and resource and harvesting costs (R&H). Six
general scenarios were developed in Liu et al. (2014), two biomass
co-firing levels (10% and 15%),1 and three assumptions regarding
feedstocks availability (10%, 20% and 30%). The objective function of
their model is to minimize total costs involved in the co-firing
process, including both fixed and variable costs. Typical costs
include costs of operation and maintenance, transportation,
handling and processing, and storage. Annual depreciation and
revenue of electricity sale are also incorporated in the objective
function. The decision variable in this LP model is the quantity of
biomass feedstock procured from each location in the region.
Five major constraints are specified in the model. The first
constraint is that the total supply must be no less than the total
demand. The second constraint is that the installed capacity must
exceed the actual demand by a certain percentage. This extra or
excess capacity is called Peak Reserve Factor which is designed to
safeguard against possible electricity shortfalls due to unpredicted
events. The third constraint limits the power plants' emissions, of
environmental pollutants to levels below some upper bound. The
fourth is feedstock constraint ensuring that the amount of feedstock used does not exceed the available amount. The last
constraint is the energy requirement constraint, which ensures that
the energy used to produce a certain amount of electricity does not

exceed the total energy contained in the biomass feedstocks used.2
The LP model of Liu et al. (2014) is summarized in Appendix A.
Intensive data were used in the linear programming model of
Liu et al. (2014). The first type of data includes the characteristics of
power plants and co-firing technologies, obtained from the State
Energy Data System (SEDS) in the U.S. Energy Information
Administration (EIA). The second type of data is related to the
various types of biomass feedstock available and their

1
Give the technologies assumed by Liu et al. (2014), there is little or no efficiency
loss when up to 15% of energy is provided by biomass (NREL, 2000; Tillman, 2000).
2
The energy constraint reflects the lower energy density when biomass is cofired with coal.


526

Z. Liu et al. / Journal of Cleaner Production 135 (2016) 523e532

Table 1
Summary of scenarios with specifications.
Scenario Co-firing level (% of energy supplied) Biomass availability (% of annual regional production) Transportion costs (TC) R&H costs
Baseline Liu et al. (2014)

TC ± 10%

R&H ± 10%

TC ± 10%

R&H ± 10%

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
10
21
22
23
24
25
26
27

28
29
30

10
10
10
15
15
15
10
10
10
10
10
10
10
10
10
10
10
10
15
15
15
15
15
15
15
15

15
15
15
15

10
20
30
10
20
30
10
10
20
20
30
30
10
10
20
20
30
30
10
10
20
20
30
30
10

10
20
20
30
30

characteristics, availability, prices and transportation costs etc.
These data were collected from the Missouri Department of Natural
Resources as well as the Biomass Site Assessment Tools in BioSAT
(www.biosat.net). The third type of data is the electricity demand
and environmental emission restrictions, which were obtained
from the U.S. Environmental Protection Agency (EPA), EIA and
Missouri Public Service Commission. The fourth type of data is
related to various costs involved in co-firing, which were collected
from several sources including Oak Ridge National Laboratory
(ORNL) and Energy Technology Systems Analysis Program (ETSAP)
of International Energy Agency (IEA).
In this study, we mainly focus on the second type of data to carry
out the sensitivity analyses. As indicated earlier, we are interested
in the impacts of several key economic factors on the total costs of
co-firing. Specifically, the influence of biomass feedstock availability, transportation costs, and biomass resource and harvesting
costs will be analyzed in the sensitivity analyses, using the model
developed by Liu et al. (2014). We use the six scenarios developed
in Liu et al. (2014) as baseline, and varied transportation costs (TC)
and resource & handling (R&H) costs 10% above and below those of
the baseline scenarios. Overall, 30 scenarios were conducted to
complete the sensitivity analyses: 6 (baseline) scenarios, and 24 in
which we varied the transportation costs and R&H costs. These
scenarios are summarized in Table 1.

5. Results and discussion
The LP models in this study were solved using AMPL3. The value
of the cost-minimizing objective function and optimal levels of all

3
A Mathematical Programming Language for describing data optimization with
variables, objectives, and constraints (ampl.com).

TC
TC
TC
TC
TC
TC
TC
TC
TC
TC
TC
TC
TC
TC
TC
TC
TC
TC
TC
TC
TC
TC

TC
TC
TC
TC
TC
TC
TC
TC

À
þ
À
þ
À
þ

À
þ
À
þ
À
þ

10%
10%
10%
10%
10%
10%

10%
10%
10%
10%
10%
10%

R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H
R&H

R&H
R&H
R&H
R&H
R&H
R&H
R&H

À
þ
À
þ
À
þ

10%
10%
10%
10%
10%
10%

À
þ
À
þ
À
þ

10%

10%
10%
10%
10%
10%

decision variables were calculated by the model for each scenario.
The detailed results are reported in the Appendix B.

5.1. Analysis of sensitivity to biomass availability
Consistent with previous studies, such as English et al. (2007)
and Liu et al. (2014), the simulations show that it costs more to
use biomass fuel for electrical generation than coal, even though
the average cost of the biomass feedstock is lower than coal.
Transportation costs play a major role in contributing to higher
total costs. Not surprisingly, the results also indicate that the total
cost of co-firing biomass decreases continuously as the available
supply of biomass feedstocks increases from 10% to 30% of total
local resources. This relationship is true for both 10% and 15% levels
of biomass in total fuel consumption. This pattern is shown in Fig. 1.
The lower cost of production achieved when larger proportions of
local biomass resources are supplied results from savings in
transportation costs due to the shorter hauling distances needed to
procure sufficient feedstocks.
Meanwhile, although co-firing biomass does increase production costs at both 10% and 15% biomass levels, the extra costs
associated with biomass use decrease as the availability of biomass
increases (Fig. 2). As explained above, this negative relationship
between cost and availability of biomass is caused by the lower
transportation costs realized when more feedstocks are accessed at
shorter distances.

Furthermore, the difference in extra costs between 10% and 15%
co-firing levels declines (Fig. 2) with increases in biomass availability. In other words, the cost moderating effect of higher rates of
biomass availability is greater at higher biomass mix rates. This
finding is perhaps not surprising, but it is also not tautological. It is
quite possible that rising co-firing levels could mitigate the advantages of higher rates of resource availability. This interesting


Z. Liu et al. / Journal of Cleaner Production 135 (2016) 523e532

527

Fig. 1. Total costs at 10% & 15% biomass co-firing levels.

Fig. 2. Extra Costs & Different for 10% & 15% biomass co-firing levels.

relationship between co-firing levels and rates of biomass availability results from the interactions among three types of costs.
First, transportation costs rise with co-firing rates because more
biomass feedstocks must be transported longer distances. The
second factor is the lower cost of biomass feedstocks compared
with coal. In these scenarios the price of coal is assumed to be
$5.43/MWh based on data from EIA (2009), compared to $4/MWh
for biomass feedstock calculated based on the LP model. Therefore,
co-firing 15% biomass, results in more fuel cost savings than 10%
because more low cost biomass fuels are used. When biomass
availability is low, i.e.10% of local resources, the savings in fuel costs
will be offset by the high transportation costs because of longer
hauling distance. But when more abundant biomass feedstocks are
available nearby, i.e. 20% or 30% availability, the saving in feedstock
costs will play a larger role in overall production costs. Higher rates
of biomass use increase the arithmetic weight on biomass price in

the calculation of total production costs. The third cost factor
leading to this finding is capital costs. Using larger amounts of lowpriced biomass feedstocks attenuates the capital investment costs
involved in co-firing biomass and further reduces overall production costs. These economies of scales are most effective when both
the rate of biomass use is high and the local availability is high.
Thus it will be very important to the ultimate feasibility of biopower that active and dependable markets for biomass be established. Depending on the relative bargaining power of the buyers
and sellers, the economic rent associated with proximity to the
plant will be split in some way between the power plants and the
biomass producers. The power plants will benefit most from higher
availability rates close to the plant and would therefore have an
incentive to bid up the price of biomass close to their plants.

5.2. Analysis of sensitivity to transportation and R&H costs
As discussed earlier, transportation costs are a critical factor in
biopower generation due to the bulkiness and low energy density
of biomass feedstocks. Another major cost component is the cost of
buying the biomass resources, which includes the price of the
feedstocks and the cost of harvesting. We conducted sensitivity
analyses on these two types of variable costs to determine how the
total costs and extra costs are affected if transportation costs and
R&H costs vary 10% above or below the baseline assumptions. The
results of these sensitivity analyses are summarized in Table 2.
As in the baseline, the total generation costs for all scenarios are
higher when co-firing biomass than coal firing only. Also as before,
costs fall as the availability of biomass increases and rises as the rate
of co-firing increases. These relationships are shown in Figs. 3 and
4.
As expected, the total generation costs increase as the transportation costs and R&H costs increase. The goal of this analysis
was not to determine the direction of the impacts but rather the
relative magnitudes. We find that for both the 10% and 15% co-firing
levels, the cost differences among the three transportation cost

assumptions (baseline, 10% below and 10% above) decline as the
biomass availability increases. In Fig. 3 the steeper lines associated
with higher transportation costs indicates that transportation costs
are disproportionately moderated by higher biomass availability.
Increasing the level of biomass availability thus reduces the significance of variability in transportation costs. We are able to
conclude that total cost of adopting biomass is more sensitive to
transportation costs when biomass availability is low. Thus the
feasibility of co-firing will be relatively more responsive to


528

Z. Liu et al. / Journal of Cleaner Production 135 (2016) 523e532

Table 2
Total co-firing cost and extra cost under alternative scenarios.
Cost change

Total Cost ($)

Extra Cost ($)

Total Cost ($)

Extra Cost ($)

TC-10%
TC (Baseline)
TCþ10%
TC-10%

TC (Baseline)
TCþ10%
R&H-10%
R&H (Baseline)
R&Hþ10%
R&H-10%
R&H (Baseline)
R&Hþ10%

10% Biomass co-firing level

15% Biomass co-firing level

10% available

20% available

30% available

10% available

20% available

30% available

5,354,406
5,586,569
5,842,085
1,489,022
1,731,185

1,986,701
5,279,736
5,586,569
5,893,403
1,424,352
1,731,185
2,038,018

5,148,775
5,258,005
5,347,585
1,293,391
1,402,620
1,492,201
4,966,483
5,258,005
5,539,075
1,111,098
1,402,620
1,683,690

5,064,742
5,155,804
5,231,939
1,209,358
1,300,419
1,376,554
4,957,809
5,155,804
5,426,681

1,102,425
1,300,419
1,571,297

8,192,598
8,395,226
8,597,669
2,409,521
2,612,149
2,814,593
7,944,276
8,395,226
8,846,175
2,161,200
2,612,149
3,063,099

7,767,340
7,934,935
8,085,081
1,984,264
2,151,859
2,302,004
7,495,172
7,934,935
8,355,036
1,712,096
2,151,859
2,571,960

7,562,424
7,711,423
7,860,452
1,779,348
1,928,347
2,077,376
7,275,602
7,711,423
8,145,978
1,492,526
1,928,347
2,362,902

Fig. 3. Total costs under different transportation costs for 10% and 15% co-firing levels.

increasing the local availability of biomass than to reducing unit
transportation costs.
However, the converging (moderating) pattern is much less
obvious for the changes in R&H costs (see Fig. 4). This is because the
R&H costs do not interact as much with biomass availability as do
transportation costs.
As to the extra costs of co-firing biomass under different
transportation costs (or R&H costs), they generate similar patterns
to those in the base scenarios. In Figs. 5 and 6 the lines associated
with 15% co-firing level are all steeper than those associated with
10% co-firing. That is, as the availability of biomass increases, the
difference in extra costs between the 10% and 15% co-firing levels
declines because of the decrease in transportation costs at shorter
hauling distances and the savings in fuel costs by using lower-

priced biomass feedstocks. As in the baseline scenarios, high
biomass availability reduces the negative impact of high transportation costs, and benefits from the lower-priced biomass feedstocks especially when biomass is used at higher mix rates. For low
biomass co-firing levels, benefits of using lower-priced biomass are
positive but less significant.
6. Conclusion
Growing concerns regarding the adverse effects of using fossil
fuels have drawn attention to biomass-based energy because it is
renewable, ubiquitous, generally environmentally friendly, easily
handled and stored, and because it leads to reductions in carbon
emissions. The use of biomass feedstocks as a substitute for coal in

Fig. 4. Total costs under different R&H costs for 10% and 15% co-firing levels.


Z. Liu et al. / Journal of Cleaner Production 135 (2016) 523e532

529

Fig. 5. Extra costs for 10% and 15% biomass co-firing under TC ± 10%.

Fig. 6. Extra costs for 10% and 15% biomass co-firing under R&H ± 10%.

electricity generation can significantly reduce emissions of greenhouse gases such as carbon dioxide (CO2), sulfur dioxide (SO2),
nitrogen oxides (NOx), and methane. In Missouri, where more than
80% of electricity comes from coal-fired power plants, it is inevitable that the state will diversify its electricity generation capacity,
given the EPA's guidelines on carbon reduction.
In contrast to most previous research which has investigated
technological factors in biopower generation, this research studied
the impacts of biomass availability and variations in transportation, and resource and hauling costs on the total power
generation costs when co-firing biopower. The research reported

here does not contradict previous research but rather complements it. This research shows that given knowledge of regionspecific transportation, agricultural, forestry, climatic, and
geographic characteristics, investors and policy makers can make
better decisions about the optimal location and scale of future
biopower capacity. It also indicates the sensitivity of these decisions to variability in the key factors determining the feasibility
of co-firing biomass.
Overall, given the current price of coal, current technology,
transportation costs, availability of biomass, and policy, co-firing
biomass for electricity in Missouri is not yet economically feasible
with subsidies. Total generation costs are higher when co-firing
biomass for all locations and all scenarios analyzed. Until technological innovations or changes in the basic costs of coal, biomass,
and transportation change, policy intervention will be necessary to
significantly increase biomass-fueled electricity generation.
Possible policy options include capital or operating subsidies, tax
credits, cap and trade programs, carbon taxes, and promotion of
green tag programs. Government funding for R&D related to
advanced biopower may also help to improve biopower technology

and reduce the associated costs in the long run but economic incentives will be necessary to achieve increased adoption of biopower adoption in the short term.
This research also suggests where technical research may yield
the greatest returns. Because of the low-energy density and bulkiness of biomass feedstocks, the feasibility of biopower is highly
sensitive to transportation costs. Based on the sensitivity analyses
in this study, costs associated with biomass co-firing decrease as
the nearby availability of biomass feedstocks increase, due to lower
transportation costs. Biomass availability moderates the impacts of
transportation costs on feasibility of biopower. With low levels of
biomass availability, the total cost of co-firing biomass is more
sensitive to transportation costs. Feasibility is less sensitive to fixed
capital costs, resource costs, and operating costs. Thus, research
that leads to reduced transportation costs will potentially have
larger impacts on economic feasibility of biopower than other types

of technical research.
In addition, more concentrated availability of biomass is likely
to have a significant impact on economic feasibility. When biomass
availability is low, transportation costs offset the price advantage
that biomass feedstocks have over coal. More concentrated availability of biomass in areas close to the power plant disproportionately reduces transportation costs. This effect is more
significant when biomass is utilized at higher levels for two reasons. On the one hand, more cost savings are possible when using
larger amounts of the lower-priced biomass. On the other hand,
greater reliance on biomass attenuates the biopower-related capital investment. Therefore, biomass must be used at just the right
leveldhigh enough to take advantage of the low-priced feedstock
and to exploit economy of scale, but low enough that transportation costs do not increase total costs too much. This ‘sweet


530

Z. Liu et al. / Journal of Cleaner Production 135 (2016) 523e532

spot’ will differ from place to place depending on all of the factors
cited above.

Appendix A. LP model adapted from Liu et al. (2014)
Objective function: 2

Min
7. Limitation and future research
This study considers the costs of biomass co-firing technology in
the coal-powered plants in Missouri where biopower is most likely
to become feasible. However, co-firing biomass in current coal-fired
power plants is probably only a short-run response to our goals of
reducing carbon emissions and increasing sustainability. In the long
run, biomass-dedicated power plants will be more effective means

of achieving these goals. Furthermore, biopower production itself is
only one tool in the climate change mitigation toolkit. A complete
solution will require many other changes in energy demand and
supply.
Converting today's coal-fired power plants into co-firing facilities is only a transitional strategy. As we invest in additional generation capacity, and as older plants become obsolete, new, biomass
only plants will be needed. Other research is considering the
technical and economic feasibility of new biopower plants but was
not the focus of this study.
An important extension of the current research would be to
conduct more dynamic analysis of biopower. The model employed
in this study is static with annual-based analysis which assumes
that the parameters included in the model, such as prices, costs,
etc., are stable across the whole year. In practice, the values of
those parameters are very unlikely to stay constant. A dynamic
model could simulate the entire electricity generation system in a
state or region given a time horizon allowing changes in demand
and price of electricity due to the growth in population. Again, the
purpose of the current study was demonstrate the importance of
local characteristics and the interaction between local biomass
availability, generator scale, and planned level of biomass
use when considering conversion to co-firing biomass. The impacts and interactions studies here may still exist in the dynamic
model.
Ignored in this sensitivity analysis are the impacts of co-firing
biomass on the local economy and community. As Altman et al.
(2007) have shown, bioenergy development can generate significant benefits in the local economy by creating new jobs and markets, and adding extra incomes to the local community, especially
in the long run when a mature biopower industry forms in the
economy. If the demand for biomass is sufficiently large and stable,
a market for biomass may develop. When a local biomass market is
established, additional economic activities will be stimulateddspecialized trucking and service providers, financial services, etc.
However, while considering the benefits of biopower to the local

communities, negative impacts are also possible. For example,
traffic congestion and accidents could increase due to the additional trucks moving the low density biomass feedstock. Other
possible drawbacks may include unpleasant odors and appearance,
diminished property values, health and safety concerns, etc. (Gold,
2011). Research into these issues is necessary to weigh the benefits
and costs, and to identify strategies that will enhance the benefits
while limiting the costs.

Acknowledgments
We thank the subject editor and two anonymous reviewers for
their constructive comments regarding the contents and format,
which helped us to greatly improve the manuscript.

X

CðnÞ ¼

n

X
n

Â

4DepðnÞ þ OMðnÞ þ

(
X
f

X
½Delðn; f ; lÞ*Q ðn; f ; lފ
l

þ HPðn; f Þ*

X

Q ðn; f ; lÞ þ Strðn; f Þ*Q s ðn; f Þ

l

9
=
;

X
þ
fTaxðn; eÞ*EMðn; eÞg
e

3

À ACTðnÞ*CAPðnÞ*365*24*P 5
Constraints:
Electricity Supply constraint (electricity supply ! demand):

X
X
½ACTðnÞ*Capðnފ*365*24 !

DðnÞ
n

n

Capacity constraint (activity

½RESERVEðnފ*ACTðnÞ

capacity):

CAPðnÞ

Emission constraint (pollutants within limit):

ENVðn; eÞ

ENV Limitðn; eÞ

Feedstock supply constraint (feedstock supply ! demand):

X
½Q ðn; f ; lÞ þ Q s ðn; t; f ފ

Supplyðf ; lÞ

n

Energy content constraint (energy supplied ! energy used):

#
"
X
X
Q ðn; f ; lÞ ! CAPðnÞ*ActðnÞ*365*24
ENGðn; f Þ*
f

l

Variables and parameters:
ACT(n): activity of biopower plant n, i.e., operation percentage;
CðnÞ: total cost associated with biopower generation;
CAP(n): installed capacity of biopower plant n;
Del(n,f,l): feedstock delivery cost, including transportation cost,
Tran(n,f,l), and R&H cost, R&H(f,l);
Dep(n): annual depreciation of total investment for biopower
plant n;
EM(n, e): emissions of pollutant e in biopower plant n;
Ems(f,n,e): emission coefficients of pollutant e for fuel f at plant
n;
ENG(n,f): energy content of fuel f.
ENV(n,e):
environmental
pollutants,
P
P
i:e:; ½Emsðf ; n; eÞ* Q ðn; f ; lފ;
f cost of handling
l

HPðnÞ:
and processing in biopower plant n;
OM(n): cost of operation and maintenance in biopower plant n;
P: price of electricity;
Q ðn; f ; lÞ : decision variable, quantity of fuel f used in biopower
plant n, from location l;
Q s ðn; f Þ: quantity of fuel f stored in biopower plant n;
Strðn; f Þ : cost of storage of fuel f in biopower plant n;
Supply(f,l): supply of feedstock f at location l;
Tax(n, e): tax or incentives on emission of pollutant e in biopower plant n.


Z. Liu et al. / Journal of Cleaner Production 135 (2016) 523e532

531

Appendix B. Results of 30 scenarios with various
transportation and R&H costs

Baseline (Liu, et al., 2014)

TC þ 10%

TC À 10%

R&H þ 10%

R&H À 10%

Biomass Co-firing level

10% biomass co-firing level

Biomass availability

10%

20%

30%

10%

20%

30%

Biomass-fired capacity (MW)

85.42

85.42

85.42

128.13

128.13

128.13

8111
147,508
11,979
1528

38,183
193,023
17,821
2112

2251
163,921
1244
2484

3578
243,081
5435
2751

1958
163,916
535
3533

3377
245,881
1867
3726

1,171,303
3,068,333
321,893
1,025,040
5,586,569
3,855,384
1,731,185

995,860
2,915,212
321,893
1,025,040
5,258,005
3,855,384
1,402,620

909,584
2,899,286
321,893
1,025,040
5,155,804
3,855,384
1,300,419

1,678,171
4,397,311
321,893
1,537,560
7,934,935

5,783,076
2,151,859

2,026,277
4,509,496
321,893
1,537,560
8,395,226
5,783,076
2,612,149

1,493,789
4,358,181
321,893
1,537,560
7,711,423
5,783,076
1,928,347

8111
152,002
12,043
1562

38,183
192,956
17,821
2179

2550

163,342
1445
2521

3698
240,626
4083
2822

3267
161,964
994
3533

3825
245,029
2168
3782

1,331,938
3,163,214
321,893
1,025,040
5,842,085
3,855,384
1,986,701

1,092,229
2,908,423
321,893

1,025,040
5,347,585
3,855,384
1,492,201

986,460
2,898,545
321,893
1,025,040
5,231,939
3,855,384
1,376,554

1,828,435
4,397,192
321,893
1,537,560
8,085,081
5,783,076
2,302,004

2,228,720
4,509,496
321,893
1,537,560
8,597,669
5,783,076
2,814,593

1,638,355

4,362,644
321,893
1,537,560
7,860,452
5,783,076
2,077,376

8111
152,135
12,043
1429

38,183
193,023
17,821
2112

2178
165,082
533
2118

2871
245,812
3489
2751

1679
164,430
338

3533

3267
247,668
849
3081

1,054,336
3,163,136
321,893
1,025,040
5,354,406
3,855,384
1,709,022

902,267
2,899,574
321,893
1,025,040
5,148,775
3,855,384
1,293,391

821,277
2,896,531
321,893
1,025,040
5,064,742
3,855,384
1,209,357

1,517,130
4,390,757
321,893
1,537,560
7,767,340
5,783,076
1,984,264

1,823,649
4,509,496
321,893
1,537,560
8,192,598
5,783,076
2,409,521

1,351,976
4,350,996
321,893
1,537,560
7,562,424
5,783,076
1,779,348

8111
147,653
11,979
1384

38,183
193,023
17,821
2112

2178
165,048
566
2118

2871
245,662
3005
2766

1679
164,430
338
3533

3267
247,428
994
3177

1,171,303
3,375,167
321,893
1,025,040
5,893,403

3,855,384
2,038,018

1,001,411
3,190,730
321,893
1,025,040
5,539,075
3,855,384
1,683,690

912,531
3,167,218
321,893
1,025,040
5,426,681
3,855,384
1,571,297

1,693,223
4,802,360
321,893
1,537,560
8,355,036
5,783,076
2,571,960

2,026,277
4,960,445
321,893

1,537,560
8,846,175
5,783,076
3,063,099

1,501,149
4,785,376
321,893
1,537,560
8,145,978
5,783,076
2,362,902

8111
147,442
11,979
1595

38,183
193,023
17,821
2112

2251
163,884
1244
2521

3698
240,522

7800
2807

3267
161,776
1182
3533

3377
245,660
1867
3947

1,171,303
2,761,500
321,893
1,025,040
5,279,736
3,855,384
1,424,352

995,850
2,623,700
321,893
1,025,040
4,966,483
3,855,384
1,111,098

898,418

2,712,458
321,893
1,025,040
4,957,809
3,855,384
1,102,425

1,676,825
3,958,894
321,893
1,537,560
7,495,172
5,783,076
1,712,096

2,026,277
4,058,546
321,893
1,537,560
7,944,276
5,783,076
2,161,200

1,493,626
3,922,523
321,893
1,537,560
7,275,602
5,783,076
1,492,526

Biomass Feedstock Required
Mill Residue (tons)
Corn Stover (tons)
Wheat Straw (tons)
Sorghum Straw (tons)
Costs
Transportation Cost ($)
Harvesting/Resource Cost ($)
Handling & Processing Cost ($)
O&M cost ($)
Total Cost ($)
Saved Cost of buying Coal ($)
Extra Cost of Using Biomass ($)
Biomass Stock Required
Mill Residue (tons)
Corn Stover (tons)
Wheat Straw (tons)
Sorghum Straw (tons)
Costs
Transportation Cost ($)
Harvesting/Resource Cost ($)
Handling & Processing Cost ($)
O&M cost ($)
Total Cost ($)
Saved Cost of buying Coal ($)
Extra Cost of Using Biomass ($)
Biomass Stock Required
Mill Residue (tons)
Corn Stover (tons)

Wheat Straw (tons)
Sorghum Straw (tons)
Costs
Transportation Cost ($)
Harvesting/Resource Cost ($)
Handling & Processing Cost ($)
O&M cost ($)
Total Cost ($)
Saved Cost of buying Coal ($)
Extra Cost of Using Biomass ($)
Biomass Stock Required
Mill Residue (tons)
Corn Stover (tons)
Wheat Straw (tons)
Sorghum Straw (tons)
Costs
Transportation Cost ($)
Harvesting/Resource Cost ($)
Handling & Processing Cost ($)
O&M cost ($)
Total Cost ($)
Saved Cost of buying Coal ($)
Extra Cost of Using Biomass ($)
Biomass Stock Required
Mill Residue (tons)
Corn Stover (tons)
Wheat Straw (tons)
Sorghum Straw (tons)
Costs
Transportation Cost ($)

Harvesting/Resource Cost ($)
Handling & Processing Cost ($)
O&M cost ($)
Total Cost ($)
Saved Cost of buying Coal ($)
Extra Cost of Using Biomass ($)

15% biomass co-firing level


532

Z. Liu et al. / Journal of Cleaner Production 135 (2016) 523e532

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