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Computersand Chemical Engineering 87 (2016) 68–81
Contents lists available at ScienceDirect
Computers and Chemical Engineering
journal homepage: www.elsevier .com/ locate /compchemeng
Strategic optimisation of biomass-based energy supply chains forsustainable mobility
Federico d’Amore, Fabrizio Bezzo∗
CAPE-Lab – Computer-Aided Process EngineeringLaboratory, Department of Industrial Engineering, Universityof Padova, viaMarzolo 9, I-35131 Padova,
Italy
a r t i c l e i n f o
Article history:
Received 9 November 2015Received in revised form
17December 2015
Accepted 5 January 2016
Available online 13 January 2016
Keywords:
Alternative fuel vehicle
Bioethanol and bioelectricity supply chain
First and second generation
Indirect land use change
Multi-objective optimisation
a b s t r a c t
The identification of alternative and sustainable energy sources hasbeenoneof the fundamental research
goals of the last two decades, and the transport sector plays a key role in this challenge. Electric cars and
biofuel fed vehiclesmay contribute to tackle this formidable issue. According to this perspective, a multi-
echelon supply chain is here investigated considering biomass cultivation, transport, conversion into
bioethanol or bioelectricity, distribution, and final usage in alternative bifuel (ethanol and petrol) and
electric vehicles. Multiperiod and spatially explicit features are introduced in a Mixed Integer Linear
Programming (MILP) modelling framework where economic (in terms of Net Present Value) and envi-
ronmental (in terms of Greenhouse Gases emissions) objectives are simultaneously taken into account.
The first and second generation bioethanol production supply chain is matchedwith a biopower produc-
tion supply chain assessing multiple technologies. Both corn grain and stover are considered as biomass
sources. In the environmental analysis, the impact on emissions caused by indirect Land Use Change
(iLUC) effects is also assessed. Results will show the efficacy of the methodology at providing stake-
holders with a quantitative tool to optimise the economic and environmental performance of different
supply chain configurations.
© 2016 Elsevier Ltd. All rights reserved.
1. Introduction
The global energy consumption by transport has grown by 2%
per year since 2000 and accounted for 28% of the overall energy
consumption in 2012 (IEA, 2015). Considering that the road trans-
port almost totally relies on petroleum derived fuels, diminishing
the mobility dependency on fossil fuels may represent not only a
strategic decision, but also an environmental necessity. One pos-
sibility to reach that goal is the establishment of the production
of biofuels and bioelectricity for alternative fuel vehicles (AFVs).
On the one hand, biofuels have played a highly significant role in
the search for alternatives as they have seemed tomany the only
feasible approach to replace petroleum-based traditional fuels in
the transport sector. On the other hand, the recent introduction of
the electric vehicles (EVs) in the private fleet market offers a new
possibility to reduce the petroleumdependency.
No parts of this paper may be reproduced or elsewhere used without theprior
written permission of the authors.∗ Corresponding author. Tel.: +390498275468; fax: +39049 8275461.
E-mail address: [email protected] (F. Bezzo).
In regards to both biofuel and bioenergy, many Process Sys-
tems Engineering (PSE) approaches focusing on the Supply Chain
(SC) design and optimisation through mathematical program-
ming (typically Mixed Integer Linear Programming – MILP) have
been recently proposed. With concern to biofuels (in fact, mainly
bioethanol), contributionshavedealteitherwiththemaximisation
of the economic performance (e.g. Dunnett et al., 2008; Zamboni
et al., 2009a), also by considering uncertainty effects (e.g. Dal-
Mas et al., 2011; Kim et al., 2011), or the interaction between
different players (Bai et al., 2012; Yue and You, 2014a,b) or with
the minimisation of the environmental impact (Garcia and You,
2015), typically through a multi-objective optimisation approach
(e.g. Zamboni et al., 2009b; You andWang, 2011). For a more com-
prehensive review, see also Yue et al. (2014c). The design of SCs
for bioenergyproduction has also been optimised in a similar way.
Many mathematical models for biomass production centres and
conversion facilities location have been carried out (e.g. Fiorese
et al., 2005; Freppaz et al., 2004; Voivontas et al., 2001), also com-
bining a detailedenergy conversion optimisationwithenergy/heat
transportationcosts(SödermanandPettersson,2006). For instance,
BruglieriandLiberti (2008)proposedamathematical programming
approach for planning and running an energy production system
process based on burning different biomasses. Contributions have
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F. d’Amore, F. Bezzo / Computers andChemical Engineering 87 (2016) 68–81 69
List of symbols
Sets
g ∈G grid squares, G={1,. . .,60} g’ ∈G set of square regions different than g
i∈ I biomass types, I ={corn, stover }
j∈ J product types, J = {ethanol, DDGS, power }
k∈K production technologies,K = {1,2,3,4,11,22,33}
l∈ L transportmeans, L= {truck, rail, barge, ship, tship} p∈P discretisation intervals for plant size linearisation,
P ={1,. . .,6}
s∈ S lifecyclestages,S = {bg ,bpt ,bt , fp, epow, fd, fdist , ebat ,
ebifuel, ec }
t ∈ T timeperiods, T ={1,. . .,5}
Subsets
elec (k) ⊂ K subset of pure power production technologies,
fratio (k) = {11, 22, 33}
Scalars
ı conversion factor specific for DDGS, 0.954tonDDGS/tonEtOH
fixed costs % over incomes, 0.15 emission inbattery production,3046.924kg ofCO2-
eq/EV
emission in bifuel car driving, 0.005515kg of CO2-eq/kmbifuel
LHV e ethanol lower heating value, 26.952 GJ/tonEtOH ethanol density, 0.7891 tonne/l MWh to tonne ethanol conversion, 0.133570792
tonethanol/MWh
MWh/year to number of EVs conversion,
1.896918157 MWh/EV/year
charg domestic electric charger 1.4kW cost, 59.055
D /newEV
inc differential EVs purchasing cost, 5000D /newEV
KMcost differential EVs driving cost, 0.03 D /kmEVkmCAR average daily trip in Italy, 45km/day
2 conversion of tee to km driven,64825.42357km/tonne
Parameters
̊g averageethanol-petrol distribution diameter, km
AD g arable land density (km2arable/km2grid surface)
BCDmax g maximum cultivation density in region g ,
km2cultivation/km2arable land
dfTCI t discount factor for investments at time t
dfCF t discount factor for cash flow at time t
CFdfCARt discount factor for cash flows at time t for EVs
etperc t ethanol blendingpercentage at time t
t differential EVs purchasing cost reduction at time t
gasolTOT t total number of traditional petrol fleet at time t renewCAR1t relative number of old EVs to be substituted
with new ones at t =4
renewCAR2t relative number of old EVs to be substituted
with new ones at t =5
ωk exceeding electricity production specific for eachconversion technology k, kWhel/lEtOH
ER p ethanol production rate for each plant size p,
tonEtOH/month
PR p electricity production rate for each plant size p,
MWh/month
i,k conversion factor specific for each biomass type i,
tonEtOH/tonbiomass
GS g grid surface, km2
MP j market price for product j
BA g,i biomass i availability for ethanol production in
region g , tonne/time period
ˇi,k fractionof ethanol rate from biomass type i foreachtechnology k
burni,k fractionof biomass i fed to CHP for each technology
k
BY i biomass yield of product i in region g ,tonbiomass/timeperiod/km
2
CI p,k capital investment at each linearisation interval p
and for technology k, MD
c k,cc coefficients for the linear regression of production
costs for each technology k, slope [D /tonEtOH] and
intercept [D ]
fbg i,g emission factor for biomass i growth in grid g and
biomass, kg CO2-eq/tonbiomass fbpt i emissionfactor forbiomass ipre-treatment,kg CO2-
eq/tonbiomass fbt l emission factor for biomass supply via mode l, kg
CO2-eq/tonbiomass km2
ffpi emission factor for ethanol production from
biomass i, kg CO2-eq/tonEtOH fppi,k emission factor forpower production from biomass
i, kg CO2-eq/MWh
ffdl emission factor for ethanol distribution viamode l,
kgCO2-eq/tonEtOH km2
fec k emission credits for each technology k, kg CO2-
eq/tonEtOHLD g,g local delivery distance between grids g and g ’ ,km
PC p,t production costs linearisedfor size pandconversion
technology k, D /timeperiod
PCap p plant capacity of size p used for cost linearisation,
tonne/time period
r k power factor for capital cost estimation for conver-
sion technology k
g,l,g tortuosity factor of transportmode l between g and g ’
UPC i,g unit production costs for biomass type i in grid g ,
D /tonbiomass z i,k biomassconversion intoelectricity,MWh/tonbiomass
Continuous variables
bifuelCARS t number of bifuel vehicles at time t
bifuelKM t total distance travelledbybifuelvehicles at time t ,
km/month
CapElec i,k,g,t supply of biomass i to plant of technology k in
region g at time t , tonne/month
BPC t biomass production constant time t , D /time period
CCF discounted Cumulative Cash Flow,D
CF t Cash Flowat time t , D /time period
Dt Depreciation at time t , D /timeperiodEPC t ethanol production cost at time t , D /timeperiod
ELtot k,g,t energy produced at time t by plant k in region g ,
MWh/month
Etot g,t ethanol produced at time t , tonne/month
EVmt EVs market share at time t
exCOt extra costs for EVs fleet, D /time period
FCC discounted facilities capital costs, D
FCC t facilities capital costs at time t , D /timeperiod
FixC t fixed cost at time t , D /time period
Impact s,t impact forlifecycle stage s at timet ,kgCO2-eq/time
period
Inc t gross earnings at time t , D /time period
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p,k, g ,t linearisation variables for TCI at interval p and fortechnology k, in region g at time t
plan p,k,g,t
linearisation variables for TCI at interval p and for
technology k, in region g at time t
NPV net present value,D
NPV chain net present value for SC profit, D
NPV car net present value for EVs fleet, D
nCARS t number of EVs at time t
newCARS t number of new EVs at time t objo objectivefunctionexpressedas thenegative of NPV ,
D , or as overall impact, tonne CO2-eq
PBT t profit before taxes for production technology k at
time t , D /timeperiod
Pbi,g,t production rate of biomass i in region g at time t ,
tonne/time period
Ppi,k,g,t powerproduction ratefrombiomass i throughtech-
nology k in region g at time t , tee/month
PPC t power production cost at time t , D /timeperiod
P TOT j,k,g,t total production rate forproduct j through technol-
ogy k in region g at time t , tonne/time period or
MWh/timeperiod
Potot g,t energyproduced in region g at time t , tonne/month
powerKM t total distance travelled by EVs at time t ,km/month
RISP t money saved driving EVs rather than petrol ones,
D /time period
TAX t tax amount at time t , D /timeperiod
TCbt biomass transport cost at time t , D /timeperiodTCpt products transport cost at time t , D /timeperiod
TCf ,t ethanol transport cost at time t , D /timeperiod
TCI t total capital investment at time t , D
TDt total ethanol and power demand at time t ,
tonne/month
TDetht total ethanol demandat time t , tonne/month
TDpowt total power demandat time t , tee/month
TGHG total GHG impact, kg of CO2-eq
TI t total impact at time t , kg CO2-eq/time periodTP t total ethanol and power production at time t ,
tonne/month
TPetht total ethanol production at time t , tonne/month
TPpowt total power production at time t , tee/month
VarC t variable costs at time t , D /time period
objective objective selection variable
Binary variables
y p,k,g,t 1ifaproductionfacilitykofsize p is tobeestablished
in region g at time t , 0 otherwiseY k,g,t 1 if a production facility k is already established in
region g at time t , 0 otherwise
Y plank,g,t
1 if theestablishment of a newconversion facility k
is to be planned in region g during time period t , 0otherwise
Y start k,g
1 if the establishment of a new conversion facility
k is to be planned in region g at the beginning, 0
otherwise
Acronyms
AFV Alternative Fuel Vehicle
CHP Combined Heat andPower
C +R Combustion andRankine Cycle
DDGS Distiller’s DriedGrainswith SolublesDGP Dry-Grind Process
EU EuropeanUnion
EV Electric Vehicle
GHG GreenhouseGas
G+MCI Gasification and Internal Combustion Engine
G+ TG Gasification andTurboGas cycleIGSP Integrated Grain-Stover Process
LCA Life Cycle Analysis
LCEP Ligno-Cellulosic Ethanol Process
MILP Mixed Integer Linear Programming
NPV Net Present ValueSC Supply Chain
dealt either with the maximisation of the economic performance
(e.g. Patel et al., 2011; Moonet al., 2011; Kumar et al., 2010; Wang
et al., 2009) orwith theminimisationof the environmental impact
(Muench, 2014; Rickeard et al., 2004). ˇ Cuˇ cek et al. (2010)proposed
an MILP model for the economic optimisation of a bioenergy net-
work in a regional context; the generation of different types of
energyproducts, heat, electricity, biofuels and food was taken into
account. Later on, ˇ Cuˇ cek et al. (2012) also proposed a framework
where the design of regional biomass SCs is obtained under the
simultaneousmaximisation of the economic performance and theminimisation of the environmental and social footprints.
Other studies havemainly focused on a well towheel life cycle
analysis (LCA) of alternative vehicles. Most contributions have
focused on EVs, usually demonstrating the potential benefits in
terms of GHGemissionsreduction (e.g. Sandy, 2012), although the
effective impact is shown to be highly dependent on the technol-
ogyused forelectricityproduction (Onatet al., 2015; Tessumet al.,
2014). A study by Campbell et al. (2009) suggested that bioelec-
tricity is more energetically and environmentally sustainable than
ethanol, especially if first generation technologies are taken into
account.
In general, as discussed above, available studies focused on the
optimisation of upstream SCs for ethanol or electricity production
or analysed the environmental effects of the technologies. How-ever, to our knowledge no study have been presented where both
theproduction SC andvehicle utilisation aresimultaneously taken
into account and optimised for a strategic assessment of biomass
exploitation. This work aims at bridging this gap by introduc-
ing a modelling framework where the whole production SC for
ethanol and/or electricity and the final customer needs are opti-
mised according to both economic and environmental objectives.
Bothcorngrainand stoverwillbe consideredasbiomass choices
and several technological options will be taken into account to
produce either ethanol and/or electricity. Site location and scale,
logistic infrastructure definition (biomass or bioethanol transport)
and end user demand evolution for AFVs (bifuel or EVs) will
be simultaneously incorporated within the optimisation model
according tothespatiallyexplicitNorthernItalyframeworkalreadypresentedby Zamboniet al. (2009a,2009b)andGiarolaetal.(2011).
The economic performance of the entire network will be assessed
in terms of the SC Net Present Value (NPV) and of the end user
potential savings inpurchasinganddriving anAFVinstead of a tra-
ditional one. Theenvironmental performanceof thesystemwill be
evaluated in terms of GHG emissions, by considering the impact
of each single life cycle stage and also incorporating the potential
consequences related to indirect Land UseChange (iLUC).
This study is organised as follows. First, the general modelling
frameworkof theSC ispresented; thesubsequentsectiondescribes
themathematical formulation of the model. Themain case studies
are then introduced and, eventually, the results of the bi-objective
optimisation arepresentedanddiscussed.Somefinalremarks con-
clude thework.
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2. Assumptions andproblem statement
This paper deals with the strategic design and planning of an
industrial SC for the production of bioethanol and bioelectricity in
North Italy over a 15-years’ time horizon. The design process is
conceived as a multi-objective optimisationproblem aiming at: (i)
the maximisation of the financial performance of the business (in
terms of global NPV), and (ii) the minimisation of the impact on
global warming (in terms of overall GHGemissions). The problem
is formulatedas a spatiallyexplicitmulti-periodandmulti-echelon
modelling framework devised for the strategic design and invest-
ment planningof biofuels and biopower supply networks.
Theentirenetworkcanbe divided into twomain substructures:
(i) the upstream network, dealing with biomass growth, biomass
pre-treatment and transport to the conversion facilities, and (ii)
the downstream network, dealing with products production, dis-
tribution and final usage by end user. This study integrates the
multi-objectiveMILPmodellingframeworkproposedbyGiarola et
al. (2011), representing the dynamic evolution of a bioethanol SC
localised in North Italy, with biopower production and the imple-
mentationof enduser-related stages (in terms of AFVs fleet).
As depicted in Fig. 1, the set of LCA stages s considered in
the evaluation are given by biomass growth (bg ), biomass pre-
treatment (bpt ), biomass transport (bt ), bioethanol production ( fp),biopower production (epow), bioethanol transport ( fd), fuel dis-
tribution ( fdist ), bifuel vehicles usage (ebifuel), EVs usage (ecars),
batteries production (ebat ) and, finally, emission credits (ec ) in
terms of GHG saving (as a result of goods or energy displacement
by process by-products end-use).
The environmental performance of the system, including issues
such as potential differences in vehicle conversion efficiency, as
well as vehicle technology for petrol (gasoline) progressive sub-
stitution, is based on two main assumptions: (i) carbon dioxide
emissionsresultingfrom thecombustionof biofuels (inbifuel vehi-
cles)areassumedtooffsetthe carbondioxidecapturedduring crop
growth; and (ii) carbondioxide emissions resulting from the com-
bustionof biomassorsyngas(inpowerplants)are assumedtooffset
thecarbondioxidecapturedduringcropgrowth.However,CH4 andN2O emissions are taken into account.
The GHG overall impact is evaluated in terms of carbon diox-
ide equivalent emission (CO2-eq), using the same assumptions as
Giarola et al. (2011) and considering the contribution of the same
gases (CO2, CH4, N2O). In regards toAFVs, their environmental per-
formance is calculated with respect to traditional petrol vehicles.
On the one hand, by assuming that bifuel vehicles have the same
engineandidentical energy efficiency as traditionalpetrol cars, the
emissionsof bifuelvehiclesonly dependon thebiofuel quota com-
bustion while driving (ebifuel in Fig. 1). On the other hand, EVs
(ecars) manufacturing and usage is assumed not to produce any
extraCO2-eqemissions,apart fromthoserelatedtobatteryproduc-
tion(ebat ),whichshould beadjustedbyremovingthecontribution
related to theconstruction of the combustion engine (which is not
installed in EVs). However, several studies in the literature (Faria
et al., 2013) show that: (i)the battery contribution in termsofGHG
emission is almost comparable with the impact deriving by the
wholevehicle production; and (ii) there is no significant difference
in the environmental impact between EV and traditional vehicle
manufactures. Thus, the impact of the combustion engineproduc-
tion can bereasonably neglected.With concernto the finalproduct
distribution, it is assumed that the already existing infrastructure
for both electricity and liquid fuels may be exploited; accordingly
no environmental impact is assigned to the infrastructure distri-
bution stages. However, we incorporated in the environmental
balance thebioethanol CO2-eqemissionquota ( fdist ) related to the
fuel transportationby truck (fromblendingterminals toendusers).
Conversely, the costs related to the bioethanol distribution do not
need to be accounted for; this depends on the way the overall NPV
is represented. As will be detailed in Section 3, the NPV related to
theproduction SC (fromcorn cultivationdownto bioethanol distri-
butionto thedepots) ishandled separately fromtheNPV concernedwith theAFVs.In particular, thelatter is formulatedasa differential
cost between an EV anda bifuel vehicle. Since ethanol distribution
costs are comprised in the fuel liquid price (as much as electricity
distribution costs are within the electricity market price) and the
distribution configuration from depots to final gas station is not
included in the optimisation problem, there is no need to have an
explicit representation.
On the basis of the above mentioned assumptions, the design
problem canbe formulated as follow. Given the following inputs:
• geographical distribution of ethanol demand centres;• bioethanol and bioelectricity demand over the entire time hori-
zon;• biomass geographical availability;• biomass production costs as function of geographical region;• technical and economic parameters as function of biomass type,
conversion technology and plant scale;• environmental burdens of biomass production as a function of
biomass type and geographical region;• environmental burdens of bioethanol and bioelectricity produc-
tion as a function of biomass type andconversion technology;
Fig. 1. Global SC network.
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72 F. d’Amore, F. Bezzo / Computers andChemical Engineering 87 (2016) 68–81
• transport logistics (e.g. typology, costs, emissions) and allowed
links;• fuel distribution from terminal to enduser;• electricity distribution network efficiency;• ethanolmarket price;• electricitymarket price;• AFVs features (e.g. efficiency, costs, consumptions, average dis-
tances, emissions);
the objective is to study, both economically (in terms of global
NPV maximisation) andenvironmentally (in terms of carbon foot-
printminimisation)the entire SC of biorefineriesandpower plants.
Therefore, the keyvariables tobeoptimisedover theplanningtime
horizon are:
• geographical location of biomass production sites;• biomass production rate and feedstock mix to the plant;• bioethanol facilities technology selection, location and scale;• biopower facilities technology selection, location and scale;• characterisation of transport logistics;• financial performance of the industrial SC over the time horizon;• financial performance of end user economy over the time hori-
zon;• demands quota evolutionover the time horizon (Instance B);• system impact on globalwarming.
The overall time horizon has been divided into five time inter-
vals (each three-years long), starting from 2015.
3. Mathematical formulation
The general modelling framework was formulated as a MILP
problemaccording tothemathematical featuresoutlinedin Giarola
et al. (2011). In particular, we retained themathematical formula-
tion for:
• objective functions general definition;• bioethanol SC economics;• cost linearisation;• logical constraints andmass balances;• environmental issues related to bioethanol SC.
On the other hand, the following new features were imple-
mented:
• biopower SC economics;• end user AFVs-related economics;• environmental issues related to biopower SC;• environmental issues related to AFVsutilisation.
New formulations and adjustments are discussed in the fol-
lowing. Further details can be found in Section 4 and in the
Supplementarydata.
3.1. Objective functions
The first objective function is the maximisation of the NPV [D ]
of theentirebusiness,whichis here expressedas theminimisation
of its opposite value:
Objeco = −NPV (1)
One important difference with respect to Giarola et al. (2011)
is that here the NPV is calculated by summing the industrial SC
profit (NPV chain [D ]) and the end user savings or costs (NPV car [D ])
in driving EVs instead of bifuel vehicles1:
NPV = NPV chain +NPV car (2)
In other words, on the one side we separate the economics of
theproductionSC from the economicinterest of thefinalcustomer.
On the other side, the impact on final customer is represented as
a cost difference with respect to a bifuel car. Note that the over-
all optimisation results in terms of theSC configuration would notchange if NPV car were described as the actual cost of buying and
using bifuel or EVs. Also note thatNPV car is not an actual and tan-
gible profit; it represents an economic advantage or burdenwhen
moving from traditional cars to EVs and can be interpreted as a
market assessingmetric to forecast AFVs penetration. TheNPV chainis calculated by summing up thediscounted cumulative cash flows
(CCF [D ]) minus the capital investment required to establish both
biofuels andbiopower production facilities (FCC [D ]). Accordingly:
NPV chain = CCF − FCC (3)
while NPV car is calculated by summing up the potential saving
indriving EVsinsteadof bifuelones(RISP [D ])minus essentialextra
costs occurred to buy EVs (exCO [D ]):
NPV car = RISP − exCO (4)
The second objective function Objenv aims at minimising the
total GHG impact (TGHG [kg of CO2-eq]) which results from the
operationof thebioethanol and the biopower SC over the 15-years
time horizon.Accordingly:
Objenv = TGHG (5)
Similarly to what was proposed by Zamboni et al. (2009b), the
value of TGHG is estimatedby summing up the total impacts TI t [kg
ofCO2-eq/timeperiod],whichin thiscaseresultfromtheoperation
of theproductionchainand theAFVsutilisation foreachtimeperiod
t :
TGHG =
t
TI t (6)
3.2. Economics
Thefollowingsub-sectionswillmainlydiscussthedifferences in
themathematical formulation withrespect to Giarola et al. (2011),
concerning thecharacterisationof thebiopowerSC andof theAFVs
fleet.
3.2.1. Modelling the biopower SC
The terms CCF and FCC for the NPV chain calculationof Eq. (3) are
evaluated as in Giarola et al. (2011):
CCF =
t
CF t · dfCF t (7)
FCC =
t
TCI t · dfTCI t (8)
where CF t [D ] is the cashflow for eachtime period t , TCI t [D ] isthe
total capital investmentanddfCF t anddfTCI t arethetimedependent
discount factors. Both CF t and TCI t are discounted through the fac-
tors whichare collectedin thetwo differentarrays dfCF t and dfTCI t .
The futureinterest rate has been assumed tobe constantandequal
1 Since in this study we are comparing AFVs end user-related economics with
respect to bifuel vehicles in terms of costs per km driven (through the differential
variable NPV car ) there is no need to describe bifuel vehicles economics through a
specific formulation.
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to 10% as resulting from the application of the CAPM (Capital Asset
PricingModel) rule.
The value of TCI t inEq. (8) is calculated as inGiarola et al. (2011)
by summing up the expenditures needed to establish the produc-
tionfacilitiesplannedat eachtimeperiod t according totheir capital
investment CI p,k (see Section 4):
TCI t =
p,k,g
plan p,k,g,t
· CI p,k (9)
where plan p,k,g,t is a continuousplanningvariablewhich isassigned
a non-zerovalue only forthe time period t inwhich the investment
decision occurs.
The value of CF t in Eq. (7) is given by the following relation:
CF t = PBT t + Dt − TAX t (10)
where Dt [D /time period] and TAX t [D /time period], which are
respectively the depreciation charge and the tax amount for each
time period t , are unchanged with respect to Giarola et al. (2011);PBT t [D /timeperiod] represents the profitbeforetaxesand iscalcu-
lated by summing up the business incomes (Inc t [D /time period])
minus the overall operatingcosts, both fixed (FixC t [D /timeperiod],
evaluated as in Giarola et al., 2011) and variable (VarC t [D /time
period]), andminus thedepreciationcharge foreach time period t :
PBT t = Inc t − VarC t − FixC t − Dt (11)
The business incomes for each time period t (Inc t ) come from
the sumof the total annual revenues earnedthroughthesale of the
product j (i.e. ethanol, electricityor DDGS)obtainedfroma conver-
sion facility of the technology k at the time period t . Accordingly:
Inc t =
j,k,g
P TOT j,k,g,t
· MP j (12)
where P TOT j,k,g,t [tonne/timeperiodorMWh/timeperiod] is thepro-
duction rate of theproduct j obtained from a conversion facility of
the technology k in the region g at the timeperiod t , andMP j is the
marketpriceof theproduct j [D /tonneorD /MWh].Theproduct j setincludesethanol,powerandDDGSwhoseamountsandproportions
dependonprocessing technologyk. Thedetailsontechnologiesand
their formal representation for the economicanalysiswill be given
in Section 4. Conversely, the modelling approach to describe the
electricity production is discussed in Section 3.2.3.
The term VarC t o f Eq. (11) is calculated by summing up the
main costs involved in the operation of the SC and, differently
fromGiarola et al. (2011), alsoaccounts forpower generation costs
through the variable PPC t [D /tee]. Accordingly:
VarC t = EPC t + BPC t + PPC t + TCbt + TCf t (13)
where EPC t [D /tonne] represents ethanol production costs, BPC t [D /tonne] represents biomass production costs, TCbt [D /tonne]
represents biomass transport costs and TCf t represents ethanoltransportcost [D /tonne].Powerproduction costs (PPC t ) aredefined
as a linear function of the total production rate of electricity from
powerplants(ELtot k,g,t [MWh]) anda fixed quotadependingon the
production technology k adopted:
PPC t =
k,g
coef k,"slope" · ELtot k,g,t + coef k,"intercept " · Y k,g,t
∀k ∈ elec k (14)
Further details about electricity production ELtot k,g,t can be
found in Section 3.2.3. In Eq. (14), coef k,“slope [D /MWh] and
coef k,“intercept [D /time period] are the arrays of linear coefficients
specific for each technology k, and Y k,g,t is the binary variable
accounting for whether a facility is operatingwith the conversion
technology k in the region g at the time period t .
3.2.2. Modelling AFVs economics
AsregardsNPV car inEq. (4), thetermRISP isevaluatedas follows:
RISP =
t
RISP t · CFdfCARt (15)
where RISP t [D /time period] represents the potential savings byend users in driving EVs instead of bifuel cars for each time period
t , which is discounted through the CFdfCARt factors for each time
period t . RISP t is evaluated by multiplying the global average
distance covered by EVs ( powerKM t [km/time period]) for the dif-
ferential travelling cost with respect to a bifuel vehicle (KMcost
[D /km]):
RISP t = powerKM t · KMcost (16)
Thevalue of KMcost is assumed tobe0.03D fortravelling1 km
(Renault, 2015; Van Vliet et al., 2010; Kay et al., 2013), consider-
ing an average consumers electricity market price of 170 D /MWh
(Autorità per l’energia elettrica e il gas, 2015). On the other hand,
thediscount factorCFdfCARt is calculated as follows:
CFdfCARt =1
(1+ i)t (17)
where i represents the timeperiod interest rate (for 3 years) and is
evaluated from the yearly interest rate i0, which is set equal to 5%
as results from the application of the CAPM rule:
i = (1+ i0)3− 1 (18)
With concern to theNPV car calculation of Eq. (4), the term exCO
is calculated by summing up the time dependent variable exCOt [D /timeperiod] foreach timeperiodt , discountedthroughthesame
CFdfCARt factors utilised for Eq. (15):
exCO =
t
exCOt · CFdfCARt (19)
where exCOt represents the additional investment for end user to
buy an EVwith respect to a bifuel one:
exCOt = newCARSt · (charg + inc ·t ) (20)
In Eq. (20), newCARS t [new EVs/time period] represents the
globalamountofnewEVspurchasedforeachtimeperiod t , depend-
ingon the electricitymarket demandevolution. Theconstantcharg
(set equal to 59 D /new EV according to Peterson and Michalek,
2013) represents the average cost of a domestic electric charger,
while the constant inc [D /new EV] evaluates the differential pur-
chasing cost of an EVwith respect to a bifuel one. This parameter
mostlydependsonhigh production costsof batteries forelectricity
storage, set nowadays just below 1000 D /kWh by Van Vliet et al.
(2010, 2011). This result is quite far from the U.S. Advanced Bat-teryConsortium longterm commercialisationcost, set equalto 150
$/kWh (USABC, 2007). By taking into account also the Renault EVs
catalogue (2015) and Perujo and Ciuffo (2010), constant inc is set
equal to5000D pernewEVpurchased bythe enduser.The conver-
sion of kWh of energy into number of EVs is operated through the
constant , which will be described in the following sub-sections.Theconstantinc isdecreasedforeach timeperiodt , accordingto the
valueof thet parameter. Heinet al. (2012) suggesta 74%decrease
forbatteriesproductioncostby 2030(from12,000D /kWhat t =1to
3000 D /kWh at t =5). An evenmore optimistic forecast is given by
Weiss et al. (2012), where an identical purchasing cost is foreseen
for EVs and bifuel cars in the Europeanmarket in 2030. Averaging
out those results, the t value was set equal to 0.125, in order to
express a lineardecreaseof EVsdifferential purchasingcost at2030
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until 12.5%of thecurrent value. Thenumber of newEVspurchased
(newCARS t ) for each time period t , is calculated as follow:
newCARSt = (nCARSt − nCARSt −1)+ renewCARSt =4 + renewCARSt =5
(21)
where renewCARS t represents the substitution of obsolete EVs for
new ones. Battery lifetime is supposed to be 10 years (Hein et al.,
2012).Onthe otherhand,thevariablenCARS t representsthe cumu-lative amount of EVs for each time period t , depending on market
dynamics.
3.2.3. Modelling the economics of electricity generation
Details about biomass cultivation (bg ), biomass pre-treatment
(bpt ), biomass transport (bt ) and ethanol production ( fp) can be
found in Giarola et al. (2011). In this work, however, biomass
can be directly converted into electricity rather than ethanol. The
subset elec k of pure power generation is described inside the facil-
ity typology set k. Similarly to what was proposed for ethanol in
Giarola et al. (2011), power production ELtot k,g,t [MWh/month] by
facility k in region g at time t is set by means of the following
inequality:
ELtot k,g,t ≤
p
PR p · p,k,g,t ∀k ∈ elec k (22)
where PR p [MWh/month] represents output rates according to the
plant scale p and p,k,g,t is a binary variable representing the exist-
ence of the facility k of the scale p in the region g at the time t . Eq.
(22) definedas an inequality allowsfor partial loadworkingplants.
Facilities output variability from t to t +1 is limited by:
ELtot k,g,t ≥ 0.8 ·
p
PR p · p,k,g,t ∀k ∈ elec k (23)
In other words, a facilities output maximum variability from
t to t +1 is set equal to 20% (for power generation), according tothe coefficient 0.8 imposed in Eq. (23). Biomass feedstock2 is eval-
uated through the variable CapElec i,k,g,t [tonbiomass/month], which
represents the biomass i input to facility k in region g at time t .
Accordingly, the power generation Ppi,k,g,t [MWh/month] through
biomass i by plant k in region g at time t is set as follows:
Ppi,k,g,t = z i,k · CapElec i,k,g,t (24)
where z i,k [MWh/tonbiomass] evaluatesthe biomass i conversioninto
electricity by the power plants elec k. By using Ppi,k,g,t , it is possible
to evaluate variable ELtot k,g,t used in Eqs. (22) and (23) through
parameterbetaei,k, describing thebiomass i fractioninput to facilityk:
Ppi,k,g,t = ELtot k,g,t · betaei,k (25)
Power production technologies k, assumptions and related
parameters can be found in Section 4 and in the Supplementary
data.
It is important to note that some biorefinery technologies for
ethanol production also result in electricity generation through
the exploitationof DDGS for CHPproduction (see Section 4). Thus,
the overall power generation Ptot power k,g,t from a generic plant k in
2 Inthe presentstudy, thestoveris theonly suitable feedstockforpurepowergen-
eration technologies. Nevertheless, a general feedstock-dependent formulation is
herepresented, in order to permit the future implementationof additionalbiomass
typologies.
region g at time t is evaluated by summing up the contribution of
the biorefineries and of the power plants. Accordingly:
Ptot power k,g,t
= ωk ·Ptot ethanol
k,g,t
+ ELtot k,g,t (26)
where ELtot k,g,t represents, as described in Eq. (22), power plants
electricity output, while the biorefineries contribution to the
power generation is taken into account through the parameter
ωk [kWh/lethanol], representing the electric energy in excess withrespect to ethanol production, according to conversion technology
k. Constant [tonne/l] stands for the ethanol density.
3.2.4. Demand evolution
In this study, market mathematical formulation is based on
two main assumptions: (i) the total transport energy demand is
assumed to be constant during the period of interest; and (ii) the
renewabletransport energy isassumedtogrowaswillbe described
in Section 4. Assuming to not store production, it is imposed that
the global ethanol (TPetht [tonethanol/month]) and power (TPpowt [teepower/month]) production rates match exactly the ethanol
(TDetht [tonethanol/month]) and power (TDpowt [teepower/month])
demands:
TPetht = TDetht (27)
TPpowt = TDpowt (28)
Productions TPetht and TPpowt are given by:
TPetht =
k,g
Ptot ethanolk,g,t (29)
TPpowt =
k,g
Ptot power k,g,t
· (30)
where constant in Eq. (30) allows the conversion of electricity
production Ptot power k,g,t from MWh to ethanol equivalent tonnes
(tee)so astoallowsummingupthecontributionsofdifferentgoods.Withconcernstoelectricity,an immediate andregion-independent
distribution is assumed. Therefore, there is no need for describing
the regional biopower demand, according to its spatially implicit
usability.
AFVs penetration in traditional carfleet depends on bioethanol
and biopower production evolutions. Bifuel vehicles bifuelCARS t (i.e. fuelled by an ethanol and petrol blending) market penetration
is calculated by summing up traditional vehicles fleet ( gasolTOT t [traditional vehicles/time period]) minus EVs (nCARS t [EVs/time
period]) for each time period t :
bifuelCARSt = gasolTOT t − nCARSt (31)
Assuming an average trip distance of 45km per day per vehicle
(ISFORT,2015; JRC,2015), it ispossibleto evaluatethe totaldistancecovered by bifuel vehicles (bifuelKM t [km/time period]) for each
time period t :
bifuelKM t = bifuelCARSt · 45 (32)
On the other hand, the number of EVs (nCARS t ) for each time
period t is calculated as follow:
nCARSt =TDpowt
(33)
where parameter =1.897 MWh/EV/year represents the elec-tric energy required to fuel an EV for 1 year. Its value was derived
fromPerujo andCiuffo (2010), describingtheEVspenetration inthe
province of Milan (Italy). Assuming again an average trip distance
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of 45km per day per vehicle, it is possible to evaluate the total dis-
tance covered by EVs ( powerKM t [km/time period]) for each time
period t . Accordingly:
powerKM t = nCARSt · 45 (34)
3.3. Environmental impact
Thedefinition of TI t (Eq. (6)) is as follows:
TI t =
s
Impact s,t (35)
where Impact s,t [kg of CO2-eq/time period] is the GHG emission
rate resulting from the operation of each single stage s at time t .
TheGHGemission rate is generally definedas follows:
Impact s,t = f s · F s,t (36)
where the reference flow F s,t [units/time period], specific for each
lifecyclestage sandtime t ismultipliedbyaglobalemissionfactor f s[kg ofCO2-eq/unit],which representsthe carbondioxideemissions
equivalent at the stage s per unit of reference flow.
Detailsabout emissionsrelated tobiomassgrowth(bg ), biomass
pre-treatment (bpt ) andbioethanol production ( fp) can befoundinGiarola et al. (2011).
3.3.1. Transport system
The resulting GHG emission of each transport option depends
onboth the distance run by the specific means and the freight load
delivered. As a consequence, the emission factor represents the
total carbondioxide emission equivalent released by thetransport
unit l perkmdrivenandtonne carried. Theglobalwarming impact
related to both the biomass supply (bt ) and the ethanol distribu-
tion to the blending terminals ( fd) is mathematically defined as in
Giarola et al. (2011).
On the other hand,emissionrelated to the final biofuel distribu-
tion to the end users ( fdist ) is a new feature of this study.The liquid
fuel (i.e. ethanol plus petrol) is assumed to be delivered by truckto petrol stations. Therefore, the total amount of ethanol Etot g,t [tonne/time period] blended in terminal g at the time t is deliv-
eredto thefinal usersaccordingtoanaveragedistributiondiameter
̊g [km]. The resulting GHG emission is obtained by multiplying
the transportation emission factor for road transport ( ffdtruck [kg of
CO2-eq/time period]) and the ethanol distribution flow occurring
from each terminal g . Accordingly:
Impact fdist ,t = ffdtruck ·
g
̊g · Etot g,t (37)
Distribution diameters ̊g are evaluated for each blending ter-
minal g according to the following equation:
̊g =
g (LD g,g · Dterm g )
2 · N (38)
where LD g,g [km]represents thedistancebetweena terminal g and
agenericregion g ’,whileDterm g isabinaryvariablewhichidentifies
thepresenceofaterminalintheregion g considered.By multiplying
up LD g,g andDterm g , it ispossibletodefinethedistance betweenan
established terminal g and a generic demand region g ’ . In Eq. (38),
N represents thenumber of distribution regions g ’. By summing up
thedistances for eachdistribution region g’ anddividingby 2N , it is
possible to calculate the average distribution diameter ̊g for each
terminal g . By adding up the circles of diameter ̊g , the resulting
area is equal to 1.3 times the distribution surface (which turns up
to be identical to the average value of the regional tortuosity g,l,g
used in Zamboni et al., 2009a).
3.3.2. Impact in electricity generation
TheGHGemissionsresulting from the bioelectricity generation
(epow) are estimated according to the methodology proposed by
IPCC (2006, 2013), whose results were compared also with those
obtained by Corti (2004), Carpentieri et al. (2005) and Mann et al.
(1996). Considering an average conversion efficiency for each pro-
duction technology k, the emission factors for power production
fppi,k [kg of CO2-eq/MWh] were implemented in the model for-
mulation (the emission factors canbe found in theSupplementary
data). Those factors are multiplied by electricity outputs for the
calculationof this stage impact for each time period t :
Impact epow,t =
i,g,k
fppi,k · Ppi,k,g,t (39)
where Ppi,k,g,t is the power rate from the biomass i at the facility k
in the region g at the time t .
3.3.3. Impact related to EV battery production
Each new EV purchased by the end users (newCARS t [new
EVs/time period]) was selected as the functional unit for this stage
emission evaluation. Accordingly:
Impact ebat ,t = · newCARSt (40)
The final emission factor value [kg of CO2-eq/new EV] was
fixed equal to 3046.9kg of CO2-eq/new EV, by averaging out some
studies in the literature (Zackrisson et al., 2010; Faria et al., 2013;
Li et al., 2014; Majeau-Bettez et al., 2011; Notter et al., 2010). In
this study, is thedifferential emission factor between traditional
petrol andEVsmanufacturing,thusboththebatteryproductionand
the lack of the internal combustion engine are taken into account
through this parameter.
3.3.4. Impact on AFVs usage
With concern to EVs, it is assumed that there are no emissions
(apart frombatteriesproduction stage ebat ). Therefore,EVsutilisa-
tion impact (ecars) is null for each timeperiod t :
Impact ecars,t = 0 (41)
On the other hand, referring to bifuel vehicles-related emis-
sions (ebifuel), a proper study is needed. Just like power plants
emission stage formulation (epow), bifuel vehicles emission factor
has been estimated following the methodology proposed by IPCC
(2006, 2013), under the following assumptions:
• vehicle running with hot engine;• exclusion of “low emission vehicles” (IPCC, 2006);• average vehicle in the USA car fleet.
The resulting emission factor [kg of CO2-eq/kmbifuel car] wasthereforeset equalto 0.005515andthe functionalunitfor theemis-
sion stage calculationwas set equal to 1km (distance travelled by
bifuel vehicles). The GHG emission is obtained bymultiplying the
bifuel vehicle emission factor with the total distance travelledbifuelKM t for each time period t . Accordingly:
Impact ebifuel,t = ς · bifuelKM t (42)
3.3.5. iLUC impact
iLUC occurs when pressure on agriculture due to the displace-
ment of previous activity or use of the biomass induces land-use
changes on other lands, in order to maintain the previous level
of production. Therefore, it has consequences in the GHG balance
of the proposed production systems. According to the European
Commission (2009, 2012), all the facilities (i.e. biofuels plants) that
use land will get an iLUC factor if [kg of CO2-eq/GJethanol]; how-
ever, feedstock that does not require any land for its production
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(i.e. waste, residues, algae) is exempt from iLUC effects. Thus, in
this studybioelectricity production does not produce iLUC-related
emissions, because the conversion facilities input is considered a
residual feedstock (corn stover). Theestimated iLUC emissionsare
introduced as follows into the modelling framework:
Impact iLUC ,t =
k,g
if · Pf k,g,t (43)
where the iLUC factor for the feedstock group (if ), in the case of this work the corn utilised as a biomass to produce ethanol, corre-
sponds to a value of 12kg of CO2-eq/GJethanol produced (European
Commission, 2009, 2012).
3.3.6. Emission credits
Following Zamboni et al. (2009b), the substitution procedure
has beenchosenin order todealwith theeffect ofbyproductalloca-
tionon emissiondiscount.Accordingto thisapproach, theemission
credits earnedby thedisplacementof alternativegoods alongwith
byproducts are subtracted from the primary product total GHG
emissions.
4. Case study
Northern Italy is chosen as a demonstrative case study. The
bioenergy production system is optimised over a 15-year horizon
by setting the transport energy demand so as to find the best mix
of power and ethanol (and accordingly of bifuel and EVs) meet-
ing the economic and environmental objectives. Market prices for
ethanol, DDGSandpowerwere fixed equal to710D /tonethanol, 300
D /tonDDGS and90D /MWh(greencreditswerenot considered).Fur-
ther details can be found in the following subsections and in the
Supplementarydata.
4.1. Spatially explicit features
Northern Italy was discretised according to the grid approachdescribedbyZamboni et al.(2009a), consisting of 59homogeneous
squares of equal size (50kmof length). One additional cell ( g =60)
isused to allow for importingbiomass from foreign suppliers. As in
Zamboni et al. (2009a), bioethanol is sent to the blendingterminals
existing at given locations.
Two main instances were formulated: in Instance A both
bioethanol and bioelectricity demand variations are set, whereas
in Instance B only the global demand is set, allowing the solver to
reach the optimum quota agreement.
4.1.1. Instance A
In this design configuration, both bioethanol and bioelectricity
demands are assumed to be fixed a priori so that their respective
productions,whichare increasing along the time horizon, arepre-set for each time period t .
On the one hand, dealing with EVs penetration, Shepherd et al.
(2012) suggest a market share of 3% by t =5 (2030), which is sim-
ilar to that indicated in a forecast by the Boston Consulting Group
(2009) (3.015%) and ina study bytheBERR(2008) (3.5%).Consider-
ing anaverage value of those results, itwas hereassumed amarket
share for EVs of 3.26% for the entire car fleet by2030. Thus, a linear
growth-rate (starting from zero) was implemented in the study to
describe the bioelectricitymarket evolution. The actual number of
EVswasconvertedinto electricity demandaccording to theparam-
eter [MWh/EV/year] described in the previous Section 3. Thecirculating traditional car fleet in the Northern Italy was assumed
to be constant along the time horizon, consistently to the statistics
of the last years (Automobile Club d’Italia, 2015).
Table 1
Ethanol (TDetht ) and power (TDpowt ) demands, ethanol blending (etperc t ) and EVs
market share (EVmt ) for each time period t (Instance A) and global demand TDt (InstanceB).
t Instance A Instance B
TDetht TDpowt etperc t EVmt TDt [kton/year] [ktee/year] [%vol] [%] [kton/year]
1 557 36 10.20 0.65 593
2 659 73 12.10 1.30 732
3 761 109 14.00 1.95 870
4 857 146 15.80 2.61 1003
5 953 182 17.60 3.26 1135
On the other hand, dealing with the bifuel vehicles penetration
(i.e. petrolplus bioethanol), theirmarketsharedecreasesalong the
15-years time horizon (because of the progressive substitution of
the traditional vehicles with EVs). However, the ethanol blending
(represented by the etperc t parameter) grows during the 5 time
periods, assuming as mandatory the future EU targets on biofuels
already described in Giarola et al. (2011).
The consequence of this design configuration is a forced drop
of petrol utilisation, which is due to: (i) the progressive substitu-
tion of the traditional vehicles with the EVs, and (ii) the increasing
ethanol blending in the bifuelvehicles. Theglobal ethanol demand(TDetht [tonne/month]) and the global electricitydemand (TDpowt [tee/month]),as well as the ethanolblending(etperc t [%
vol]) and the
EVs market share (EVmt [%]) evolutions, are reported in Table 1 f or
each time period t .
4.1.2. Instance B
In this design configuration, the global demand (TDt [tonne/month]) is set, allowing the solver to find the optimal
ethanol/electricity quotas. The global demand TDt is evaluated
by summing up the ethanol demand TDetht and the electricity
demand TDpowt as they resulted in InstanceA for each timeperiodt . The global demand TDt is reported in the last columnof Table 1.
Clearly in this case the AFVs market evolution is a result of the
optimisation.
4.2. Biomass growth, biomass pre-treatment and transport
systems
Withconcerns tocorncultivation, thespatiallyspecificdatasets
(i.e. BCDmax g , AD g , BY i,g and UPC i,g ) were taken fromZamboni et al.
(2009a),whilethe stoveryields (BY “stover , g ) and costs (UPC “stover ,g ),
aswell as thedatatoestimate the impacton globalwarming( fbg i,g )
of biomass production, were derived from Giarola et al. (2011).
Biomass pre-treatment dealswith thedryingand storage oper-
ations after the biomass harvesting and collection. Costs are not
considered because already included in the biomass production
costs, whereas the environmental impact deriving this stage is
taken from Zamboni et al. (2009a).The distribution infrastructure includes trucks, rail, barges and
ships as possible deliverymeans.Trans-shippingwas included as a
viable transport option for biomass importation.All the transport-
relatedparametershavebeenbasedon actualgeographicdistances
between regions g and g’ according to the procedure described in
Zamboni et al. (2009a).
4.3. Bioethanol production
Following the results of Giarola et al. (2011), three main
processing technologies were identified: (i) theDry Grind Process
(DGP), where corn is converted into ethanol through a biologi-
cal process; (ii) the Ligno-Cellulosic Ethanol Process (LCEP), where
stover only is converted into ethanol; and (iii) the Integrated
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Table 2
Technological option for ethanol production, identification and products
description.
k Process Input Output
DGP LCEP IGSP Grain Stover Ethanol CHP DDGS
1 X X X X
2 X X X X
3 X X X X X
4 X X X X X
Grain-Stover Process (IGSP), where both corn grain and stover are
processed to obtain ethanol. Table 2 summarises the main fea-
tures of the four technological options, in particular the feasible
input/output combinations are pointed out. Among IGSP possibili-
ties,twodifferenttechnologies (k=3,4)areimplementedaccording
to different biomasses mixtures in terms of corn grain and stover
as described in Giarola et al. (2011).
4.4. Bioelectricity generation
In order to analyse the potential technological options for
the conversion of biomass into electricity, it was decided to
exclude those technologies that currently do no exhibit eithera proven maturity or a sound economic viability (e.g. Stirling
engines, Organic Rankine Cycles, Fuel cells) (Lora and Andrade,
2009). Accordingly, three technological options were considered
in this study: (i) biomass direct combustion for Rankine steam
cycle (C+R, k=11); (ii) biomass gasification for Turbo Gas cycle
(G+TG, k=22); and (iii) biomass gasification for Internal Combus-
tion Engine (G+MCI, k=33). It was assumed to consider only pure
power generation, excluding from the calculations the combined
heat and power potential production. Thus, the electricity genera-
tion (PR) was assumed to be proportional to the biomass (i.e. corn
stover only) input (Capelec ), according to the following equation:
Capelec =PR · f c
k · LHV fuel(44)
where f c represents the load factor (assumed to be of 8000h/year
independently from the technology k), while k is the conversionefficiency foreachtechnology k and LHV fuel is a constant represent-
ing the lower heating value of the feedstock introduced (which for
corn stoveris 15.9MJ/kg, accordingto the Jenkinscatalogue,1998).
Electricity generationratesPR p are discretised according to the set
p (Table 3). The capital investment CI and the production costs
PC of the technologies elec k are evaluated through the following
equations:
CI = a · PR−b (45)
PC = c · PR−d (46)
where thecoefficients a, b, c and d (Table 4) dependon theconver-
sion technology k.With concern to thebiomass direct combustion within a Rank-
inecycle, their capital investment and theproductioncosts, aswell
as the conversion efficiency, were evaluated from the literature
Table 3
Production capacity, nominal values foreach plant size p, ER p and PR p.
p ER p PR p[kton/year] [MW]
1 96 1
2 110 5
3 150 10
4 200 15
5 250 30
6 276 60
Table 4
Facilities coefficients a, b, c and d for thecalculation of costs,CI and PC.
k a b c d
11 137,720 0.407 0.263 0.280
22 76,007 0.433 0.202 0.240
33 26,414 0.377 4.768 0.730
(Caputo et al., 2005; Patel et al., 2011; Weiss et al., 2012; Fulmer,1991). According to theliterature, theplant scale for theconversion
technologywas limited between 3MWand 60MW.
For the gasificationplants, both the technical and the economic
parameters were also derived from the literature (Arena et al.,
2010; Kinoshita et al., 1997; Craig and Mann, 1996; Wu et al.,
2002, 2008). The results were also compared with the Biomass
CHP catalogue (2007). According to the literature, the plant scale
for the Turbo Gas cycles was limited between 100kWand 60MW,
while for the Internal Combustion Engines the possible range was
set between 100kW and 10MW (the latter is the largest existing
size for stationary generation according to General Electric, 2015).
Similarly to the mathematical formulation adopted for biore-
fineries, linear equations were obtained by the regression of the
capital investment CI p,k (Table 5) and the production costs PR p,k(Table 6) values, related to several capacity intervals p per each
technology k.
The biomass conversion factors (i.e. corn stover converted
into electricity which is then distributed to end user), z i,k[MWhdist/tonstover], are set equal to 1.0890 for k=11, 1.6326 fork=22 and 1.5306 for k=33. According to statistics made by Terna
(2014), the average efficiency of the Italian National grid is equal
to 0.935 (defined as the electricity distributed to end users with
respect to the electricity produced at the facility). The parameter z i,k wasevaluatedbymultiplyingthatvalue (0.935)andtheaverage
conversion efficiencyk for each technology k.With regard to the environmental aspects, the GHG emission
from the power production stage was assumed to be proportional
to the total annual amount of biomass i processed by the facility k,accordingto themethodologyproposed by IPCC (2006). The global
emission factors assigned to elec k stover-basedprocesses (i.e. fppi,k[kg ofCO2-eq/MWh]), setequal to1.97 fork=11, 1.47for k=22 and
1.53 for k=33, were calculated through a spreadsheet tool (IPCC,
2013) accounting only for CH4 and N2O contributions.
Table 5
Capital Investment, valuesof the linearisation parametersCI p,k [MD ].
p k 1 2 3 4 11 22 33
1 62 396 187 81 – 4 2
2 70 434 204 90 22 10 5
3 91 535 252 117 32 14 8
4 115 648 305 149 49 21 –5 139 753 354 179 62 26 –
6 151 804 379 195 94 39 –
Table 6
ProductionCosts, valuesof the linearisation parameters c k,c .
k slope intercept
[D /tonne or D /MWh] [D /month]
1 140.83 169,906
2 202.88 891,755
3 143.36 507,404
4 17.746 388,525
11 10.71 64,377
22 13.71 45,894
33 2.91 19,805
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78 F. d’Amore, F. Bezzo / Computers andChemical Engineering 87 (2016) 68–81
B2
B3
B1
iLUC
iLUC
A2
A3
A1
iLUC
iLUC
-8
-6
-4
-2
0
2
4
6
8
0 10 20 30 40 50 60 70 80 90 100 110 120
N P V - N e t P r e s e n t
V a l u e [ € / G J
o u t p u t ]
TGHG -
Total GHG Emission [kg of CO2-eq/GJoutput]
Instance B Instance A
Fig. 2. Paretocurves under bi-objectiveoptimisation forInstanceA andInstanceB.
5. Results and discussion
Thedesign variables (about 300k continuous variables and 15kdiscrete ones per iteration) were optimised in about 60hours by
using the CPLEX solver in the GAMS® modelling tool on a 3.40GHz
processor.
As expected, the Pareto set of sub-optimal solutions (see Fig. 2)
resulting from the bi-objective problem solution reveals the con-
flict between environmental and economic performance. First of
all, theresultswill bediscussedwithoutconsidering theiLUCeffect
on environmental performance. In Instance A, the optimal config-
uration in terms of economic performance (case A1 as reported in
Fig. 2) entails a marginal NPV of 2.08 D /GJoutput against a global
environmental impactof 94.8kg ofCO2-eq/GJoutput, which infactis
equivalent to a GHG increase of about 10% compared topetrol (the
GHGemissionfactorforpetrolwasassumedequalto85.8kgofCO2-
eq/GJ, according to HGCA, 2005). In such a configuration theusageof biomass (at least, the biomass considered in this study) does not
lead to any environmental advantage. The environmental impact
is almost identical in Instance B, where the optimal configuration
in terms of economic performance (case B1 as reported in Fig. 2)
entails a marginalNPV of 5.55D /GJoutput (significantly higher than
in InstanceA)anda globalenvironmental impact of 95.2kgof CO2-
eq/GJoutput, equivalent toa GHG increaseof about 11% compared to
petrol. In both Instance A and InstanceB, the systemdesignwould
involve the establishment of standardDGPbiorefineries (k=1) and
a significant corn importation fromabroad.With regardto theelec-
tricity production, G+TG (k=22) and G+MCI (k=33) facilities are
establishedandthefeedstockfor theelectricity conversion (stover)
is completely produced in Northern Italy.
Fig. 3a illustrates the optimal economic configuration in thecase of Instance B. Fig. 4a shows the contributions of NPV chain and
NPV car to the global NPV under the economic optimisation. With
regards to NPV chain, both instances perform similarly (440MD vs.
407).The good SC economicperformance ismainlyrelated to tech-
nology choicesandbiomasssupply costs.On theotherhand,NPV car is quite different in the two cases. In InstanceA it isworth 198MD ,
while it reaches 1294MD inInstance B. This is due tothe larger EV
penetration in Instance B (about 12% by t =5). In fact, that design
configuration can produce a NPV car of about 1.3GD in 15 years,
against of about 200MD in Instance A. Thus, it would seem that
EVs would acquire a higher than expected market share in an
unconstrained condition. However, Fig. 5 suggests a more com-
plexsituation. AlthoughthefinalNPV is largely positive, InstanceB
requires that the final users accept a period of negative economic
performance,which isneeded topaybackthe investment forestab-
lishing the production facilities. In other words, due to the initial
high EV cost, initially EV buyers would not get any return from
their investment (note from Fig. 5 that the initial payback time is
about 10years,whichis also the average battery lifetime)and sim-
plywould be instrumental to set inmotion the production SC. This
is unlikely to occur unless some incentives are promoted through
dedicatedpolicies. Inview of the above, Instance A, despite a lower
value of NPV car , exhibits a payback time of 7 years and probablyrepresents a more sensible designoption.
The best configurations in terms of global warming mitigation
potential are achieved through the establishment of different con-
version technologies for ethanol production. In Instance A, the
optimalconfigurationin termsof environmentalperformance(case
A2 as reported in Fig. 2) entails a global environmental impact
of 34.6kg of CO2-eq/GJoutput corresponding to a GHG decrease
of about 60% compared to petrol (compliant with long terms EU
environmental targets). The environmental optimum (case B2 as
reported in Fig. 2) is even better in Instance B, in which the global
environmental impact is of 16.0kg of CO2-eq/GJoutput, correspond-
ing to a GHG decrease of about 81% compared to petrol. However,
theoperationof suchasystemwouldbefeasibleonlyundera strong
support policy (governmental subsidy should account for about
3.85 D /GJoutput in Instance A, corresponding to about 1.2GD par-
celled out over the 15 years horizon, and for about 5.92 D /GJoutputin Instance B, corresponding to about 1.8GD over 15 years).
In Instance A, the strategic design involves the establishment
of the expensive LCEP technology for the ethanol production
(k=2) and, similarly to the economic optimum, of gasification
technologies (k=22, 33) for electricity generation. On the other
hand, in Instance B the SC configuration results do not suggest
the establishment of pure power generation facilities to reach
the environmental goals (see Fig. 3b). It appears that adopting
LCEPtechnology toproducebothethanolandelectricity represents
the best environmental option. Fig. 4b shows the contributions
of NPV chain and NPV car to the global NPV under the environ-
mental optimisation. In Instance A NPV chain is worth −1377MD
against −1799MD in Instance B, whereas in Instance A the NPV car is worth 198MD against −15MD in Instance B. The Instance
B negative value of NPV car is related to the fact that second
generation ethanol technologies are preferred and the payback
time is not reached within the time horizon considered in this
study.
An intermediate situation is representedby thecasesA3and B3
reportedinFig. 2,whichrepresentthe thresholdbetweenprofitable
andunprofitableproduction SC (NPV chain≈0).RegardingInstanceA,
theoptimisationentailsamarginalNPV of0.50D /GJoutput (thanksto
the contribution of the AFVs penetration in the market) against a
global environmental impact of 40.6kg of CO2-eq/GJoutput equiv-
alent to a GHG decrease of about 53% compared to petrol. The
economic result is better in Instance B,with amarginalNPV of 2.93
D /GJoutput (again given only by NPV car ) against an environmentalperformance of 41.9kg of CO2-eq/GJoutput corresponding to a GHG
decrease of about 51% compared to petrol. In both Instance A and
Instance B, those results are achieved through the installation of
IGSP biorefineries (k=3, 4), which could represent an interesting
halfway technology between the high economic performance of
DGP process and the low environmental impact of LCEP process.
Again, the power generation depends on gasification technologies
(k=22, 33) in both Instance A and Instance B.
Theeffectsof iLUC aremerely environmental,as showninFig.2.
As a consequence, in cases A1 and B1 the environmental results
are worsened significantly after the incorporation of iLUC effects:
theoverall GHGemissions increase in Instance A to about 106.8kg
of CO2-eq/GJoutput (+25% with respect to petrol) and in Instance B
to about 103.5kg of CO2-eq/GJoutput (+21% with respect to petrol).
8/18/2019 Strategic Optimisation of Biomass
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F. d’Amore, F. Bezzo / Computers andChemical Engineering 87 (2016) 68–81 79
Fig. 3. SC configuration at the end of timehorizon (t =5) in Instance B under (a)economic and (b) environmental optimisation.
There isno iLUCeffect in cases A2and B2 since in bothcases stover
is theonly feedstock.
5.1. Taxation effects
In our analysis, NPV car was calculated considering the actual
pricea consumerwouldpayforfuelandelectricityin Italy. Toassess
the effect of taxation on fuel and electricity, duties were excluded
in the calculation of the parameter KMcost . In other words, the
market prices are now assumed to be equal to the selling prices
at the facility plus the distributioncosts. Theprice for electricity is
set equal to 110 D /MWh and the price for the bioethanol is 0.709
D /l. The latter was calculated by summing up an average market
price for the ethanol (0.56 D /l according to Giarola et al., 2011)
and by assuming an average distribution cost, evaluated as for the
petrol in 0.15D
/l (Unione Petrolifera, 2015). As a consequence,
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80 F. d’Amore, F. Bezzo / Computers andChemical Engineering 87 (2016) 68–81
-2000
-1500
-1000
-500
0
500
1000
1500
2000
NPVchain NPVcar NPV
N e
t P r e s e n t V a l u e [ M € ]
(a) Instance A Instance B
-2000
-1500
-1000
-500
0
500
1000
1500
2000
NPVchain NPVcar NPV
N e t P r e s e n t V a l u e [ M € ]
(b) Instance A Instance B
Fig. 4. NPV chain, NPV car and NPV [MD ] under: (a) economic or (b) environmental
optimisation.
parameter KMcost is now worth 0.05 D /km and the effect isto make EVs more advantageous with respect to bifuel ones. In
Instance A the NPV increases up to 6.00 D /GJoutput (+191%) while
in Instance B it becomes 35.35 D /GJoutput (+543%). Clearly the
elimination of taxation would have positive consequences on the
economics of EVs and it shows how current Italian taxation donot
favour EVspenetration in themarket.
5.2. A sensitivity analysis on price decrease in EVs
Thepotential savings fora final customermainly dependon the
dynamicsconcerning thedifferential purchasing costbetween EVs
and bifuel cars over the timehorizon. Thus, for InstanceB weveri-
fiedtheeffect ontheeconomicoptimumby consideringdifferential
pricevariationdynamics,assuming thefinal differentialpurchasing
costby2030 froma null toa maximumvalue of2500D /newEV.On
the one hand, the results show that the differential cost of EVs has
hardly any consequence in the NPV chain (i.e. theSC configuration is
notaffectedby thisparameter).Ontheother hand,notsurprisingly,
the consequences onNPV car are remarkable (see Fig. 6).
-1500
-1000
-500
0
500
1000
1500
2000
2018
2021
2024
2027
2030
N e t P r e s e n t V a l u e f o r E V s - N P V c a r
[ M € ]
Years
Instance A_NPVcar Instance B_NPVcar
Fig. 5. Actualisation of NPV car through the years [MD ] for Instance A and Instance
B.
-1000
0
1000
2000
3000
0 500 1000 1500 2000 2500 N e t P r e s e n t
V a l u e - N P V [ M € ]
EVs differential cost by 2030
Instance A_NPVchain Instance A_NPVcar
Instance B_NPVchain Instance B_NPVcar
Fig. 6. EVs differential purchasing cost by 2030: economic consequences.
In Instance A, there is no convenience for the end users in pur-
chasing an EV if the differential purchasing cost is still of 1200
D /new EV by t =5 (2030). Nevertheless, this value seems quite
pessimistic with respect to the one that was implemented in the
formulation according to the literature (625 D /EV by t =5). In
Instance B, the initially not affordable economic situation for end
users, due to a high differential purchasing cost, generates a drop
in the EVs penetration during the first years.
6. Final remarks
A multi-objective MILP modelling framework for the strate-
gic optimisation ofmulti-echelonbioethanol andbiopower supply
chains, intended to support the alternative fuel vehicles penetra-
tionin theNorthern Italymarket, hasbeenpresentedanddiscussed.
All simulation studies show that both bifuel and electric vehicles
are needed for complying with themarket demand.
Froman economicstandpointof thesupply chain infrastructure,
the results show that a correct mix of first generation biorefineries
for ethanol production and of gasification facilities for electricity
generation represents a viable industrial optionandpermits a fea-
sible penetration of the alternative fuel vehicles in the traditional
market. Nevertheless, this network configuration does not repre-
sent a suitable answer tomatch the EU targets on global warming
mitigation, due to the high environmental impact resulting from
the handling of the first generation technologies, especially after
incorporating iLUC effects. On the other hand, second generation
biorefineries are still rather expensive and requiring some sort of
incentives. However, coupled with same gasification technologies
for power generation, they represent the best option for a signifi-
cant reductionof global GHGemissions.
Appendix A. Supplementary data
Supplementary data associated with this article can be found,
in theonline version,athttp://dx.doi.org/10.1016/j.compchemeng.
2016.01.003.
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