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

Johnsonb, Ira Altmanc a Lynchburg College, Department of Management, School of Business and Economics, 1501 Lakeside Drive, Lynchburg, VA 24501-3113, USA b University of Missouri-Columbi

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The moderating role of biomass availability in biopower co- ﬁring d A

sensitivity analysis

Zuoming Liua,*, Thomas G Johnsonb, Ira Altmanc

a Lynchburg College, Department of Management, School of Business and Economics, 1501 Lakeside Drive, Lynchburg, VA 24501-3113, USA

b 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

a r t i c l e i n f o

Article history:

Received 16 October 2015

Received in revised form

21 May 2016

Accepted 17 June 2016

Available online 19 June 2016

Keywords:

Biomass

Co-ﬁring

Biopower

Linear programming

Sensitivity analysis

a b s t r a c t

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

Wefind that the cost of electricity generated is higher when biomass is cofired under all assumption However, itfinds 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 feasi-bility 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

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

demonstra-tions sent a strong message that more and more people are

con-cerned 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

switch-ing 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

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 se-curity continue to be raised (WEC, 2007; EIA, 2013) In June 2014, the U.S Environmental Protection Agency (EPA) proposed guide-lines designed to reduce the national level of CO2emissions 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 pollu-tion could be eliminated, and 2.7 million jobs related to clean en-ergy could be created at the same time (Pollin et al., 2014)

In response to thesefindings, 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

* Corresponding author.

E-mail addresses: lzuoming@gmail.com (Z Liu), JohnsonTG@missouri.edu

(T.G Johnson), ialtman@siu.edu (I Altman).

Contents lists available atScienceDirect Journal of Cleaner Production

j o u r n a l h o me p a g e :w w w e l se v i e r co m/ lo ca t e / jc le p r o

http://dx.doi.org/10.1016/j.jclepro.2016.06.101

Journal of Cleaner Production 135 (2016) 523e532

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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

pro-duction such as optimum oxygen factors, air temperature, air-fuel

ratio, operating pressure, biomass particle size, pressure, etc

Bio-energy 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,

trans-portation 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

Infor-mation 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

Sta-tion, and about 3% of electricity generation came from renewable

energy resources, with about 95% of that from conventional

hy-droelectric 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

pro-duced annually This is almost twice the total electricity propro-duced

in Missouri in 2009 (Fink and Ross, 2006) Although biomass

feedstocks can only be partially collected and used, they

never-theless offer great potential for increased renewable energy

gen-eration 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

guide-lines 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

re-quirements and can be an incremental way of reducing the

emis-sion 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

loca-tions, 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 supplyﬂuctuations 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 avail-ability of local feedstocks (IEA , 2007) Due to region-speciﬁc vari-ations 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 relatedfields.Mathieu and Dubuisson (2002) simu-lated the process of wood gasification in the ASPEN PLUS process simulator based on the Gibbs free energy minimization, and con-ducted a sensitivity analysis on various factors regarding their ef-fects 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 in flu-ence 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 calcula-tions to simulate a dual fluidized-bed steam gasifier with a decentralized system that combined heat and power They con-ducted 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 effi-ciency of the gasification are gasification temperature and fuel oxygen content Another study regarding biomass gasification in a fluidized bed byLv 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, steamflow rate and biomass to steam ratio) The analysis showed that all operating parameters impact the model performance, and that the steamflow rate has a larger impact on the reactor's tem-peratures 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

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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

in-vestment and prices of heat and electricity They varied these

pa-rameters 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

tech-nologydHydrolysis, Pyrolysis, and Gasiﬁcation They also

per-formed 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

pro-duction with most of these focusing on forest residues, and

dedi-cated 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

CO2emission, 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

in-formation 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

viseaevis conventional coal-ﬁred generation Technological

ad-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

competitive-ness 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

speaking, it is not economically viable to haul biomass fuels over

100 miles (Bechen, 2011) AsShakya (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 fac-tories and paper mills The third key factor included in our sensi-tivity analysis is the cost of the biomass itself The cost of the biomass feedstocks to the power plant includes the in situ oppor-tunity 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 inter-acting 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 place-based approach in which the local conditions ultimately deter-mine 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, cli-matic, geographic and transportation infrastructure characteristics

4 Methodology and data This study employs a linear programming (LP) model developed

byLiu 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 inLiu et al (2014), two biomass co-firing levels (10% and 15%),1and 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 feed-stock 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 ofLiu et al (2014)is summarized inAppendix A Intensive data were used in the linear programming model of

Liu et al (2014) Theﬁrst 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 co-ﬁred with coal.

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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

avail-ability, transportation costs, and biomass resource and harvesting

costs will be analyzed in the sensitivity analyses, using the model

developed byLiu et al (2014) We use the six scenarios developed

inLiu 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 inTable 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

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

5.1 Analysis of sensitivity to biomass availability Consistent with previous studies, such asEnglish et al (2007)

andLiu 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 inFig 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 produc-tion 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 avail-ability 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 ad-vantages of higher rates of resource availability This interesting

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

3 A Mathematical Programming Language for describing data optimization with

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relationship between co-ﬁring levels and rates of biomass

avail-ability 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 fromEIA (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 thisﬁnding is capital costs Using larger amounts of

low-priced biomass feedstocks attenuates the capital investment costs

involved in co-ﬁring biomass and further reduces overall

produc-tion 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

bio-power that active and dependable markets for biomass be

estab-lished 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 inTable 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 inFigs 3 and

4

As expected, the total generation costs increase as the trans-portation 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 Weﬁnd 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 InFig 3the 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

sig-niﬁcance 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

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

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

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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 (seeFig 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 InFigs 5 and 6the 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 trans-portation costs, and benefits from the lower-priced biomass feed-stocks 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

Table 2

Total co-ﬁring cost and extra cost under alternative scenarios.

Cost change 10% Biomass co-ﬁring level 15% Biomass co-ﬁring level

10% available 20% available 30% available 10% available 20% available 30% available Total Cost ($) TC-10% 5,354,406 5,148,775 5,064,742 8,192,598 7,767,340 7,562,424

TC (Baseline) 5,586,569 5,258,005 5,155,804 8,395,226 7,934,935 7,711,423

Extra Cost ($) TC-10% 1,489,022 1,293,391 1,209,358 2,409,521 1,984,264 1,779,348

TC (Baseline) 1,731,185 1,402,620 1,300,419 2,612,149 2,151,859 1,928,347

Total Cost ($) R&H-10% 5,279,736 4,966,483 4,957,809 7,944,276 7,495,172 7,275,602

R&H (Baseline) 5,586,569 5,258,005 5,155,804 8,395,226 7,934,935 7,711,423 R&Hþ10% 5,893,403 5,539,075 5,426,681 8,846,175 8,355,036 8,145,978 Extra Cost ($) R&H-10% 1,424,352 1,111,098 1,102,425 2,161,200 1,712,096 1,492,526

R&H (Baseline) 1,731,185 1,402,620 1,300,419 2,612,149 2,151,859 1,928,347 R&Hþ10% 2,038,018 1,683,690 1,571,297 3,063,099 2,571,960 2,362,902

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

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

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electricity generation can signiﬁcantly reduce emissions of

green-house 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

inevi-table 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

trans-portation, 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

comple-ments it This research shows that given knowledge of

region-speciﬁc 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

de-cisions 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

techno-logical 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 in-centives will be necessary to achieve increased adoption of bio-power adoption in the short term

This research also suggests where technical research may yield the greatest returns Because of the low-energy density and bulk-iness 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 tofixed 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 avail-ability of biomass in areas close to the power plant dispropor-tionately reduces transportation costs This effect is more signiﬁcant when biomass is utilized at higher levels for two rea-sons 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 cap-ital 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 trans-portation costs do not increase total costs too much This‘sweet

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%.

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

cited above

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

facil-ities is only a transitional strategy As we invest in additional

gen-eration 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

im-pacts 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 AsAltman et al

(2007)have shown, bioenergy development can generate signi

ﬁ-cant beneﬁts in the local economy by creating new jobs and

mar-kets, 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

stim-ulateddspecialized trucking and service providers, ﬁnancial

ser-vices, 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

addi-tional 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

Appendix A LP model adapted fromLiu et al (2014)

Objective function:

n

CðnÞ ¼X

n

2 4DepðnÞ þ OMðnÞ þX

f

(

l

½Delðn; f ; lÞ*Qðn; f ; lÞ

þ HPðn; f Þ*X

l

Qðn; f ; lÞ þ Strðn; f Þ*Qsðn; f Þ

9

=

;

e

fTaxðn; eÞ*EMðn; eÞg

ACTðnÞ*CAPðnÞ*365*24*P

3 5

Constraints:

Electricity Supply constraint (electricity supply demand):

X

n

½ACTðnÞ*CapðnÞ*365*24 X

n

DðnÞ

Capacity constraint (activity capacity):

RESERVEðnÞ*ACTðnÞ CAPðnÞ

½

Emission constraint (pollutants within limit):

ENVðn; eÞ ENV Limitðn; eÞ

Feedstock supply constraint (feedstock supply demand):

X

n

½Qðn; f ; lÞ þ Qsðn; t; f Þ Supplyðf ; lÞ

Energy content constraint (energy supplied energy used):

X

f

"

ENGðn; f Þ*X

l

Qðn; f ; lÞ

#

CAPðnÞ*ActðnÞ*365*24

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,

i:e:;P

f ½Emsðf ; n; eÞ*P

l

Qðn; f ; lÞ;

HPðnÞ: cost of handling and processing in biopower plant n; OM(n): cost of operation and maintenance in biopower plant n; P: price of electricity;

Qðn; f ; lÞ : decision variable, quantity of fuel f used in biopower plant n, from location l;

Qsðn; 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 bio-power plant n

Trang 9

Appendix B Results of 30 scenarios with various

transportation and R&H costs

Biomass Co-firing level 10% biomass co-firing level 15% biomass co-firing level

Biomass-ﬁred capacity (MW) 85.42 85.42 85.42 128.13 128.13 128.13 Baseline ( Liu, et al., 2014 ) Biomass Feedstock Required

Corn Stover (tons) 147,508 193,023 163,921 243,081 163,916 245,881

Costs Transportation Cost ($) 1,171,303 995,860 909,584 1,678,171 2,026,277 1,493,789 Harvesting/Resource Cost ($) 3,068,333 2,915,212 2,899,286 4,397,311 4,509,496 4,358,181 Handling & Processing Cost ($) 321,893 321,893 321,893 321,893 321,893 321,893 O&M cost ($) 1,025,040 1,025,040 1,025,040 1,537,560 1,537,560 1,537,560 Total Cost ($) 5,586,569 5,258,005 5,155,804 7,934,935 8,395,226 7,711,423 Saved Cost of buying Coal ($) 3,855,384 3,855,384 3,855,384 5,783,076 5,783,076 5,783,076 Extra Cost of Using Biomass ($) 1,731,185 1,402,620 1,300,419 2,151,859 2,612,149 1,928,347

TC þ 10% Biomass Stock Required

Corn Stover (tons) 152,002 192,956 163,342 240,626 161,964 245,029

Costs Transportation Cost ($) 1,331,938 1,092,229 986,460 1,828,435 2,228,720 1,638,355 Harvesting/Resource Cost ($) 3,163,214 2,908,423 2,898,545 4,397,192 4,509,496 4,362,644 Handling & Processing Cost ($) 321,893 321,893 321,893 321,893 321,893 321,893 O&M cost ($) 1,025,040 1,025,040 1,025,040 1,537,560 1,537,560 1,537,560 Total Cost ($) 5,842,085 5,347,585 5,231,939 8,085,081 8,597,669 7,860,452 Saved Cost of buying Coal ($) 3,855,384 3,855,384 3,855,384 5,783,076 5,783,076 5,783,076 Extra Cost of Using Biomass ($) 1,986,701 1,492,201 1,376,554 2,302,004 2,814,593 2,077,376

TC 10% Biomass Stock Required

Corn Stover (tons) 152,135 193,023 165,082 245,812 164,430 247,668

Costs Transportation Cost ($) 1,054,336 902,267 821,277 1,517,130 1,823,649 1,351,976 Harvesting/Resource Cost ($) 3,163,136 2,899,574 2,896,531 4,390,757 4,509,496 4,350,996 Handling & Processing Cost ($) 321,893 321,893 321,893 321,893 321,893 321,893 O&M cost ($) 1,025,040 1,025,040 1,025,040 1,537,560 1,537,560 1,537,560 Total Cost ($) 5,354,406 5,148,775 5,064,742 7,767,340 8,192,598 7,562,424 Saved Cost of buying Coal ($) 3,855,384 3,855,384 3,855,384 5,783,076 5,783,076 5,783,076 Extra Cost of Using Biomass ($) 1,709,022 1,293,391 1,209,357 1,984,264 2,409,521 1,779,348 R&H þ 10% Biomass Stock Required

Corn Stover (tons) 147,653 193,023 165,048 245,662 164,430 247,428

Costs Transportation Cost ($) 1,171,303 1,001,411 912,531 1,693,223 2,026,277 1,501,149 Harvesting/Resource Cost ($) 3,375,167 3,190,730 3,167,218 4,802,360 4,960,445 4,785,376 Handling & Processing Cost ($) 321,893 321,893 321,893 321,893 321,893 321,893 O&M cost ($) 1,025,040 1,025,040 1,025,040 1,537,560 1,537,560 1,537,560 Total Cost ($) 5,893,403 5,539,075 5,426,681 8,355,036 8,846,175 8,145,978 Saved Cost of buying Coal ($) 3,855,384 3,855,384 3,855,384 5,783,076 5,783,076 5,783,076 Extra Cost of Using Biomass ($) 2,038,018 1,683,690 1,571,297 2,571,960 3,063,099 2,362,902 R&H 10% Biomass Stock Required

Corn Stover (tons) 147,442 193,023 163,884 240,522 161,776 245,660

Costs Transportation Cost ($) 1,171,303 995,850 898,418 1,676,825 2,026,277 1,493,626 Harvesting/Resource Cost ($) 2,761,500 2,623,700 2,712,458 3,958,894 4,058,546 3,922,523 Handling & Processing Cost ($) 321,893 321,893 321,893 321,893 321,893 321,893 O&M cost ($) 1,025,040 1,025,040 1,025,040 1,537,560 1,537,560 1,537,560 Total Cost ($) 5,279,736 4,966,483 4,957,809 7,495,172 7,944,276 7,275,602 Saved Cost of buying Coal ($) 3,855,384 3,855,384 3,855,384 5,783,076 5,783,076 5,783,076 Extra Cost of Using Biomass ($) 1,424,352 1,111,098 1,102,425 1,712,096 2,161,200 1,492,526

Trang 10

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