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Energy 56 (2013) 70e80

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Energy

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Energy supply infrastructure LCA model for electric and hydrogentransportation systems

Alexandre Lucas a,*, Rui Costa Neto b, Carla Alexandra Silva b

aMIT Portugal Program, IST-Technical University of Lisbon, Avenida Prof. Cavaco Silva, Campus IST, TagusPark, Room 16.6, 2780-990 Porto Salvo, Portugalb Institute of Mechanical Engineering, IST-Technical University of Lisbon, Av. Rovisco Pais 1, Pav. Mec. I, 2� andar, 1049-001 Lisboa, Portugal

a r t i c l e i n f o

Article history:Received 11 October 2012Received in revised form22 March 2013Accepted 24 April 2013Available online 27 May 2013

Keywords:HydrogenElectric vehiclesLife cycle analysisInfrastructureUncertainty

* Corresponding author. Tel.: þ351 961741327.E-mail addresses: alexandre.lucas@ist.utl.pt,

(A. Lucas), costaneto@ist.utl.pt (R.C. Neto), carla.silva@

0360-5442/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.energy.2013.04.056

a b s t r a c t

Many transportation environmental life cycle analyses neglect the contribution of the energy supplyinfrastructures. In alternative light duty vehicle technologies, it has been shown through case studiesthat this can be a relevant factor. However, no model that can generalise the evaluation of energy andemissions from construction, maintenance and decommissioning of such infrastructure to analysedifferent scenarios currently exists. A model is proposed, focussing on electricity and on hydrogen supplythrough centralised steam methane reforming (H2(a)) and on-site electrolysis (H2(b)). The model outputsare in gCO2eq/MJ and MJeq/MJ of the final energy. Model main inputs are the region’s electricity mix, theannual distance driven, supply chain losses and the number of vehicles per station or chargers. Theevaluation of the number of vehicles served per each charger/station as a function of annual distancedriven is presented. The uncertainty is estimated by using the pedigree matrix, impact uncertainty andliterature estimates. The model shows consistency in the results and uncertainty range. Charging policiesthat minimise the electricity infrastructure burden should incentivise approximately 37% of normalcharging. H2(a) pipeline lifetime should be extended. Efforts in the electrolyser should be undertaken toapproximate the ratio of vehicles per station with a conventional one.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

In the road transportation sector, the Life Cycle Analysis (LCA)cradle-to-grave (CTG) concept is usually separated into a Well-to-Wheel (WTW) fuel analysis and a materials cycle analysis. How-ever, the infrastructure required to manufacture, transport, anddistribute the actual fuel has been left unattended. Tank-to-Wheel(TTW) reference studies by Weiss et al. (2000, 2006) [1,2] presentthe expected energy use and emissions for 2020 from passenger cartransportation. The studies cover pure combustion (gasoline,diesel) engines (ICE), combustion hybrids, fuel cell hybrid electricvehicles (FCHEV), and battery-powered electric cars (EV). Althoughanalysed over different time horizons, both electricity andhydrogen are regarded as important alternatives to mitigate envi-ronmental concerns. Edwards et al. (2011) [3] presents a compre-hensive report of WTW cycles for several hydrogen, electricity, andconventional fuels pathways, as well as local or centralised pro-duction philosophies. Studies for hydrogen fuelling infrastructure

Alexandre.Lucas@efacec.comist.utl.pt (C.A. Silva).

All rights reserved.

and pathways are also well covered in the literature. Gross et al.(2007) [4] estimates future costs of hydrogen fuel and the scale ofdistribution facilities. They demonstrate that a hydrogen infra-structure that could support large-scale deployment of fuel cellelectric vehicles could be commercially viable. Huang et al. (2006)[5] simulated ten hydrogen pathways using petroleum-basednaphtha, natural gas (NG), electricity and coal as feedstock. Theyconcluded that all pathways have significant reductions in WTWpetroleum use except the H2 pathways from naphtha.

The final report of the Hysociety Project [6] shows an extensiveamount of researchwork related to LCA studies of hydrogen appliedto the transportation sector. No study includes the materials for theenergy supply infrastructure, including final refuelling stations orelectricity chargers. However, as addressed by Frischknecht et al.(2007) [7], depending on the sector, capital goods or infrastructuremay have a relevant impact on the total environmental burden. Inprevious studies, Lucas et al. (2012) [8,9] analysed the energysupply infrastructure component separately and included it in thevehicle LCA. Research findings report that this stage may have atotal LCA contribution as high as 12%.

The environmental impacts of power plants have been studiedin several contexts [10e13], and, although for conventional tech-nologies a consensus has been reached, the uncertainty is still very

A. Lucas et al. / Energy 56 (2013) 70e80 71

high in other technologies (e.g., renewable sources and nuclear).There are several main reasons for this uncertainty: the variety inthe technologies used, location, scale, plant’s lifetime, capacityfactors considered; or simply different allocation methods used inthe LCA [14]. For H2 production, Spath et al. (2001) [15] developedinventories for an H2 plant including the pipeline infrastructure,and it can be seen that the main variables influencing the result perunit of final energy are the same.

Regarding the electricity transmission grid, the range of theenvironmental burden associated with its construction, mainte-nance and decommissioning has been accepted by the scientificcommunity [9,12,16e18]. The variations in the estimates are relatedto different lifetimes considered, the high use of SF6 insulatedsubstations [17], high densities of the transmission grid per squarekilometre and the high usage of underground cables [16].

Regarding electricity chargers and H2 refuelling stations, esti-mates [8,9,19] have shown that, within their own pathways, theyare the main energy and emission contributors and also that thenumber of refuelling/charging stations per vehicle is the mainfactor of uncertainty. However, by establishing scenarios it ispossible to study the impact of each choice and advise futuredecisions.

Because there is no tool to assist in the evaluation of infra-structure deployment in terms of environmental impact caused byconstruction, maintenance and decommissioning activities, anddue to its potential relevance, it is important to derive a generalisedmodel that could allow such analysis.

2. Methodology

System boundaries are shown in Fig. 1 following the authors’previous studies [8,9]. They refer directly to the supply chainsdivided into generation, transportation and end distribution facil-ities. The electricity pathway for EV’s includes the infrastructure forthe feedstock logistics (only an NG pipeline was considered), PowerPlants (Coal, Oil, NG, Nuclear, Hydro, Geothermal, Wind and Solar),the electricity transmission grid and normal, home and fast char-gers. Energy supply infrastructures used by FCHEV include an NGpipeline associated with the steam methane reforming centralisedH2 plant, H2 distribution pipelines and H2 refuelling stations(subsequently referred to as the H2(a) pathway). Another possibleH2 pathway used by FCHEV includes the same set of power plantsand incoming feedstock infrastructure used in the electricitypathway, the electricity transmission grid, and H2 refuelling sta-tions equipped with an electrolyser system for on-site hydrogenproduction (subsequently referred to as H2(b)). Fuel cell plug-inhybrid electric vehicles (FCPHEV) use both pathways.

Fig. 1. LCA scope definition of the three pathways: electricity, hydrogen centralisedproduction H2(a), hydrogen local production H2(b).

As part of the feedstock logistics infrastructure, the NG pipelinewas included in the Natural Gas Combined Cycle (NGCC) plants. Allother types of feedstock (coal, oil, fuel, etc.) were considered to besupplied by mobile transportation (ships and trucks), so theircontributions were already accounted for in the part of the fuelcycle in the Well-to-Tank (WTT) stages or the vehicle’s materials.Maintenance activities were only considered for power plants, H2plant and H2 stations.

Following the boundaries in Fig. 1, the model estimates theemissions and energy use related to the construction, maintenanceand decommissioning of electricity and H2 pathways. Emissions andenergy use are based on Global Warming Potential for 100 years(GWP100y) and Cumulative Energy Demand (CED) methods [20],with output functional units of gCO2eq/MJ and MJeq/MJ, respectively.

The model, as expected, is more abstract than the system itrepresents. From one point of view, abstraction, and the assump-tions made to achieve it, eliminates unnecessary detail and allowsconcentration on elements within the system that are most rele-vant to define. On the other hand, such an abstraction process in-troduces uncertainty in the results.

The nature of uncertainty can be separated into two categories:aleatory uncertainty, generally referred to as variation, that isinherent and naturally unpredictable variation, and epistemic un-certainty, generally referred to as uncertainty, that is related toinaccurate measurements, lack of data, and missing information. Inthis model, only the latter is addressed. Typically, the uncertaintyaddressed in LCA can be divided into three groups: parameteruncertainty (input data), model uncertainty (mathematical rela-tionship) and scenario uncertainty (choices). Because we are pre-senting amodel and the scenario inputs are left to the user, only theparameter uncertainty was considered. There can be two types ofparameter uncertainty: measurement uncertainty, which is relatedto imperfections, assumptions or the inability to take an exactmeasurement when the actual inventory is being developed, anduncertainty related to the data quality of the inventories used. TheEcoinvent [21] Pedigree matrix was used to account for such un-certainty, defining distributions using individual raw data pointsand attributing uncertainty levels according to the nature of theuncertainty.

Another source of uncertainty considered in this study accountsfor the actual emission or energy impact to be considered for acertain material. This is not due to the uncertainty of the identifiedimpact value of the specific material (considered negligible in thisstudy), but due to an under-specification of the material. When acertain type of material is inventoried, it may be difficult to identifyits exact composition or type, for example, plastics or alloys. Forthat reason, there is the need to either use a similar product tocompensate for its absence in the used database (application un-certainty) or to incorporate all possible values for that impact. Forthe inventories developed and compiled in this study (for thechargers and H2 refuelling stations), the best fit distribution wasused to incorporate all different values (quantity of emissions orenergy) found in the Simapro 7.1 database [12] for variations of thesame material. The same approach was used for the NG and H2pipelines but using a uniform distribution. This analysis can befound in Tables 1e4 of the Support Information (SupInf) section.Fig. 1 of the SupInf exemplifies how both parameters (quantity andimpact) are related and addressed in this study.

Regarding power plants, to incorporate the whole range ofpossibilities within a certain generation technology, uniform dis-tributions were considered in order, to include all possible inves-tigated impacts. All uncertainty values can be found in Table 5 ofthe SupInf.

In terms of how uncertainty is considered to influence themodel, it is important to note that the values of a, b, g used to

A. Lucas et al. / Energy 56 (2013) 70e8072

replace the last term in the electricity pathway and F, J, l used toreplace the last term in the H2(a) pathway (see SupInf) are esti-mated assuming average inventory values. In the event of a varia-tion in the inventory, the same percentage of variation should beapplied to each parameter of the quadratic functions.

3. Model formulation

The model is presented in a set of parameterised equations withtwo equations (emissions and energy) per pathway. The structureof each equation is similar, with each parameter referring directlyto a supply chain stage: generation, transmission or end distribu-tion facilities.

In each stage, the value of gCO2eq/MJ or MJeq/MJ is corrected bythe loss factor of the downstream stages so that it can reflect a unitbased on the final energy; i.e., a unit of final energy is equal to theenergy supplied to the vehicle (1 MJ), accounting for the energylosses in all stages in between. Losses are given in percentages byðMJex=MJfÞ=ðMJ Stage InputÞ, which is the energy spent in an in-dividual step of the chain per unit of final energy divided by theenergy that goes into that stage, which is also per unit of finalenergy. An example is shown in Table 6 of the (SupInf) section. Forthe case studies analysed, data from Edwards et al. (2011) [3] wasused for all losses except for the transmission grid, which werebased on Ref. [22]. L%TG refers to the losses in the electric trans-mission grid, L%pline to losses in the H2 distribution pipeline, L%CO tothe losses in the H2 refuelling station compressor, L%H2Plant to thelosses in the H2 SMR plant and L%El&CO to the losses in the elec-trolyser and compressor system at the H2 refuelling station. Lossesconsidered are only assumed to be related to electricity.

The infrastructure LCA of the correspondent pathway is ob-tained by adding up all the terms in the general equations. Toconvert the output of the model to a MJ/km unit, the total should besimply be multiplied by the TTW value of the vehicle; no otheradjustments are needed.

Table 1Percentage of chargers of total service ratio and partial service ratios.

Normal Home Fast

Partial service ratios 0.105 0.975 0.003% of Total service ratioa 11.73% 88.00% 0.3%

a Considering 2.5% of home chargers level on the street.

3.1. Electricity pathway

The electric infrastructure emissions LCA is represented ingCO2eq/MJ in Eq. (1) and is a function of parameters that referdirectly to the supply chain in Fig. 1.

LCACO2eq Electricity ¼ Mc

ð1� L%TGÞþ 0:07þ Nc þ Hc þ Fc (1)

The parameter given by variable Mc refers to the carbon in-tensity from the construction, maintenance and decommissioningof all power plants, which can be calculated by (2) according to theregion’s electric mix. The 0.07 gCO2eq/MJ constant refers to the bestestimate of the electricity transmission grid carbon intensity perunit of final energy. This value has reached some consensus in theliterature [9,12,16e18], even though some variation is reported,hence its inclusion in the uncertainty analysis. Nc, Hc and Fc definethe emissions life cycle analysis of normal, home and fast chargers,respectively, which are further developed using Eqs. (3)e(5). Theenergy losses in the chargers are considered to be negligible.

Mc ¼ 1:5M1 þ 1:2M2 þ 1:1M3 þ 1:67M4 þ 3:1M5 þ 4:2M6

þ 6:2M7 þ 15:1M8 (2)

Sub-parameters Mi in Formula (2) indicate the contribution tothe electric mix of each of the electricity generation sources (Oil,Coal, Natural Gas, Nuclear, Hydro, Geothermal, Wind and Solar,respectively), where

P8i¼1Mi ¼ 100%. The multiplying factors in

gCO2eq/MJ used for each Mi variable are the mean and median

values found in the literature review (Table 5 of the SupInf). Theuncertainty values and capacity factors considered are shown inTable 7 of the SupInf.

Parameters Nc, Hc and Fc, are calculated byEqs. (3)e(5) andreflect the relation of (GWP frommaterials)/(Total energy flowing):

Nc ¼ 187:04Eþ03 � %NchSR � SRTotalAvrcarMJ=year � %Nch � LTimeNch

(3)

Hc ¼ 42:04Eþ03 � %HchSR � SRTotalAvrcarMJ=year � %Hch � LTimeHch

(4)

Fc ¼ 3146:91Eþ03 � %FchSR � SRTotalAvrcarMJ=year � %Fch � LTimeFch

(5)

Nc in Eq. (3) is given by the carbon intensity of the normalcharger (187.04 gCO2/MJ inventoried based on Refs. [8,9] and on theuncertainty developed in Tables 1 and 2 of SupInf), divided by two(the number of sockets in each satellite), multiplied by the per-centage of normal chargers (%NchSR) of the total number ofchargers-per-vehicle and multiplied by the chargers-per-vehicleratio (subsequently referred to as the total service ratio or SRTotal).The numerator is then divided by the total energy that flowsthrough the chargers, which is calculated by multiplying theaverage energy spent by a car per year (AvrcarMJ/year ¼ TTW � km/year) with the percentage of charge actually being performed inthis type of charger (%Nch) during its lifetime period (LTimeNch). Hc(4) and Fc (5) follow exactly the same idea, only with different in-ventory values and ratios.

3.1.1. Evaluation of SRTotal and charging percentagesIf SRTotal and the percentages of chargers are unknown, the

methodology below can be applied. Using a vehicle’s TTW andannual distance driven, one can estimate the amount of energyrequired from each of these chargers. Eq. (6) can be used tocalculate the total service ratio (SRTotal) by inputting the averagenumber of kilometres travelled per year of a vehicle (x) in thescenario under study.

SRTotal ¼ 8:8931E�06xþ 9:7934E�01 (6)

To develop the projection of what the total Service Ratio mightbe in each scenario when the annual mileage changes, we consid-ered the reference study [23] values for France, shown in Table 1.The ratio of each type of charger (home, normal and fast) pervehicle (further referred as partial service ratios) and its corre-spondent percentage can also be estimated, given by Eq. (7)

%NchSR ¼ 7:1826E�06xþ 1:2008E�02 (7)

To make the projection, one can assume an annual mileage of14,500 km per year [23] and that 2.5% of level 1 chargers (typicalhome charger) will be installed on the street (with physical char-acteristics similar to those of level 2 chargers or typical normalchargers). Additionally, the mean amount of charging per type ofcharger considered is 10% with normal charging, 84% at home and

A. Lucas et al. / Energy 56 (2013) 70e80 73

5.5% using fast chargers [23]. Applying Eq. (3), we reach referencevalues of 5.2 gCO2eq/MJ and, with a similar approach, 0.11 MJeq/MJof normal chargers intensity.

Because the functional unit is a unit based on final energy, theLCA values will vary if the amounts of charging performed in eachtype of charger also changes. Although fast chargers are not likelynor desirable to have a higher use than indicated for environ-mental, electric constraints and load diagram management prob-lems [23,24], home and normal charging may change considerablydepending on the region’s number of garages, public or privateparking spaces or population density. For that reason, both themileage and the amount of charging (critical to define the amountof energy that flows through the charger) have to be included.Therefore, by changing the number of kilometres, maintaining theratio of the number of fast chargers and fixing the reference in-tensities values, a new total service ratio is obtained.

In addition to maintaining the fast chargers ratio, the ratio ofhome chargers is also considered to be unchangeable (as it is un-likely that a user will install additional chargers at home dependingon the distance driven); hence, the only variable changing will bethe normal chargers ratio, which is assumed to change linearly. Eq.(7) provides the percentage of normal chargers service ratio as afunction of annual kilometres per vehicle. The percentage of homechargers service ratio is then given by taking the total service ratioand subtracting the normal and fast chargers percentage.

To estimate the carbon and energy intensity of the charginginfrastructure, an example is given considering the values pre-sented in Table 1. The carbon and energy intensity will vary ac-cording to a quadratic function. Fig. 2 shows that an increase of theaverage number of kilometres per year will result in a decrease ofthe carbon intensity of the charging infrastructure per unit of finalenergy (assuming only the normal chargers service ratio changes).The same behaviour is verified in the energy use LCA estimations.

Such behaviour is verified for Table 1 inputs; however, the pa-rameters are not fixed and the annual average distance driven pervehicle is not the only variable. Additionally, the amount of normaland home charge can vary. Hence, by changing the amount ofcharge to be provided per each type of charger, the curve shown inFig. 2 will increase or decrease its positioning in relation to theabscissa axes according to a general quadratic curve. This curve cancompletely replace the Nc, Hc, and Fc parameters of Eq. (1) byinputting the average annual distance driven (x), expressed now inEq. (8).

LCACO2eq Electricity ¼ Mc

ð1� L%TGÞþ 0:07þ acx2 � bcxþ gc (8)

The three parameters of the quadratic function must be foundby inputting the percentage of normal charging given by %Nch. Fig. 3shows how each parameter evolves when the percentage of normalcharging increases (maintaining fast charging and adjusting homecharging accordingly). Overall, the carbon intensity decreases due

Fig. 2. Life cycle emissions variation of the charging system with average annualdriven distance (x).

to the high weight of the third parameter gc (Fig. 3c). Eqs. (4)e(6) ofthe SupInf are extracted from the curves in Fig. 3 and can beinputted in Eq. (8), replacing Nc, Hc and Fc shown in Eq. (1).

To identify the charging policy (percentages of charging amongchargers) that minimises the burden caused by the infrastructureon an MJ-based unit, the derivative of the third parameter shouldbe equal to zero. As seen in Fig. 3c, although unlikely, if the share ofthe total charge performed by normal chargers reaches 37%, themarginal effect of an energy flow increase is zero. In fact, if it be-comes higher than 37%, the intensity values starts to increase. Thisis because, while the normal charging share increased, the share ofhome charging decreased to a point where, with the same fixedpartial home service ratio (e.g., 0.975), the quantity of energy thatflows through the home charger is so low that it is no longerbeneficial to the system to reduce it (or increasing the normalcharging share).

Following the exact same methodology, the infrastructure en-ergy intensity equations were estimated. Eq. (9) contains threetypes of parameters.

LCAMJeq Electricity ¼ Me

ð1� LTGÞþ 1:52E�03 þ Ne þ He þ Fe (9)

First, the parameter given by Me refers to the energy intensityfrom the construction, maintenance and decommissioning of po-wer plants which can be calculated by (10), according to the re-gion’s electric mix. Second, the constant 1.52E�03 MJeq/MJ refers tothe best estimate of the transmission grid energy intensity per unitof final energy. This value has reached some consensus in theliterature [9,12,16e18], even though some variation is reported;hence, it is included in the uncertainty analysis. Third, Ne, He and Fedefine the energy intensity of the chargers, which can be furtherestimated by using Eqs. (11)e(13). Energy losses in the chargers areconsidered to be negligible.

Me ¼ 1:9E�03M1 þ 1:9E�03M2 þ 3:5E�03M3 þ 1:2E�03M4

þ 5:7E�02M5 þ 6:4E�02M6 þ 4:4E�02M7 þ 2:6E�01M8

(10)

Parameters Ne, He, and Fe, are calculated with Eqs. (11)e(13) andreflect the relation of (Energy Use from materials)/(Total energyflowing):

Ne ¼ 3975:894� %NchSR � SRTotalAvrcarMJ=year � %Nch � LTimeNch

(11)

He ¼ 856:03� %HchSR � SRTotalAvrcarMJ=year � %Hch � LTimeHch

(12)

Fe ¼ 60016� %FchSR � SRTotalAvrcarMJ=year � %Fch � LTimeFch

(13)

In case SRTotal and the percentages of chargers are unknown, Ne,He and Fe can be replaced by the general quadratic curve, inMJeq/MJ,presented in Eq. (14), where x is the average distance driven for avehicle in km/year.

LCAMJeq Electricity ¼ Me

ð1� LTGÞþ 1:52E�03 þ aex2 � bexþ ge (14)

Following the same methodology as in the emission analysis,from the resulting curves, the corresponding Eqs. (7)e(9) of theSupInf for energy use can be extracted and inputted into Eq. (14),replacing Ne, He and Fe of Eq. (9).

Fig. 3. Carbon intensity quadratic curves of each parameter ac (a), bc (b), gc (c) of the chargers general equation.

Fig. 4. Life cycle emissions variation of a H2(a) refuelling station with different averageannual driven distance.

A. Lucas et al. / Energy 56 (2013) 70e8074

3.2. Hydrogen (a) pathway

The H2(a) infrastructure emissions LCA in gCO2eq/MJ is repre-sented by Eq. (15) and is given by parameters that refer directly tothe supply chain. The first and third parameters account for the NGpipeline infrastructure that supplies the SMR plant and that dis-tributes H2 to the refuelling stations, respectively, and are adjustedwith their corresponding losses. The 0.354 gCO2eq/MJ constant isthe best estimate for these parameters based on the literature [12],but due to the variety of estimations in the database, a uniformdistribution was used to characterise it. The second parameter isrelated to the SMR hydrogen plant. Because the capacity factorgreatly influences the result, the constant 0.17 gCO2eq of outputenergy, based on Ref. [15], was considered using a 100% capacityfactor, which must be adjusted with the real capacity factor byintroducing the variable CF. The fourth parameter refers to the H2refuelling station. The numerator is the emissions LCA in gCO2/yearbased on Ref. [19] inventory. An uncertainty analysis was conductedon this variable, which is presented in Table 3 of the SupInf. Thisfactor is then divided by the total energy that flows through therefuelling station per year.

LCACO2eqH2ðaÞ ¼0:354�

1� L%H2Plant

���1� L%pline

���1� L%Co

�

þ 0:17=CF�1� L%pline

���1� L%Co

�þ 0:354ð1� L%CoÞ

þ 1:84Eþ07

ðMJsold=yearÞ (15)

Regarding the last parameter, the energy that flows through therefuelling station may change according to the number of kilo-metres driven per year and the number of cars per station. Using areference TTW value of 1.25 MJ/km [8] for a FCHEV and consideringa given ratio of 2000 vehicles per station, the curve in Fig. 4 isobtained. As expected, the figure shows that an increase of theaverage number of kilometres per year will result in a decrease ofthe carbon intensity of the charging infrastructure per unit of finalenergy. The same behaviour is verified in the energy use LCAestimations.

However, the annual distance driven is not the only variable thatmay vary. The amount of energy supplied by the refuelling stationalso depends on the ratio of vehicles per station that can vary

substantially from region to region; hence, this last term has to beanalysed further. The curve shown in Fig. 4 will increase or decreaseits positioning in relation to the abscissa axes, according to a gen-eral quadratic curve. By introducing the average annual distancedriven per vehicle (x), this curve can replace completely the lastparameters of Eq. (15), resulting in Eq. (16).

LCACO2eqH2ðaÞ ¼0:354�

1� L%H2Plant

���1� L%pline

���1� L%Co

�

þ 0:17=CF�1� L%pline

���1� L%Co

�þ 0:354ð1� L%CoÞ

þfcx2�Jcxþ lc

(16)

Simulating several ratios of vehicles per station and extractingall three terms of each quadratic function that was found, thecorresponding curves in Fig. 5 are obtained, from which the cor-responding Eqs. (10)e(12) of the SupInf can be extracted.

Following the exact same methodology, the infrastructureenergy intensity equations were estimated:

LCAMJeq H2ðaÞ ¼2:59E�02

�1� L%H2Plant

���1� L%pline

���1� L%Co

�

þ 3:03E�03= CF�1� L%pline

���1� L%Co

�þ 2:59E�02

ð1� L%CoÞ

þ 3:20Eþ05�MJsold=year

� (17)

Fig. 5. Carbon intensity quadratic curves of each parameter Fc(a), Jc(b), lc(c) of the H2(a) refuelling station by vehicles per station.

A. Lucas et al. / Energy 56 (2013) 70e80 75

where the last term can be replaced by a general quadratic equa-tion, with x representing the average annual distance driven.Eq. (17) can hence be replaced by Eq. (18)

LCAMJeq H2ðaÞ ¼2:59E�02

�1� L%H2Plant

���1� L%pline

���1� L%Co

�

þ 3:03E�03= CF�1� L%pline

���1� L%Co

�þ 2:59E�02

ð1� L%CoÞ

þfex2�Jexþ le

(18)

The terms of the quadratic function refer to energy intensityvalues, whose values can be estimated by Eqs. (13)e(15) of theSupInf in a manner similar to the procedure in the emissionsanalysis.

Fig. 6. Vehicles per refuelling station ratio H2(b) according to annual driven distance.

3.3. Hydrogen (b) pathway

The H2(b) infrastructure emissions LCA in gCO2eq/MJ is repre-sented by Eq. (19). The Mc variable corresponds to the electric mixand should be calculated according to Eq. (2). The second termnumerator is the same constant of the electricity pathway and ac-counts for the transmission grid. The third term refers to the H2refuelling station. The numerator is the station’s carbon intensity ingCO2/year based on Ref. [19] inventory. This value is then divided bythe total energy that flows through the refuelling station per year.

LCACO2eq H2ðbÞ ¼ Mc

ð1� L%TGÞ � ð1� L%El&CoÞþ 0:07ð1� L%El&CoÞ

þ 2:033Eþ07�MJsold=year

� (19)

The energy sold per year is given by the number of vehicles perstation multiplied by the average TTW value of the vehicle and theaverage annual distance driven (x) (Eq. (20)).

MJSupplied ¼ VehiclesStation

� TTW� x (20)

Naturally, the question to know is how many vehicles perstation to consider. Because the production capacity is limited due

to the electrolyser capacity, there is a maximum value that can besupplied. Considering the installed capacity and the associatedcapacity factor, whichmay vary, divided by the energy required by avehicle, Eq. (20) may be rearranged to provide the ratio of vehicles/station (Eq. (21)).

VehiclesStation

¼ EInst:cap: � CFTTW� x

(21)

Fig. 6 shows the ratio of vehicles per stations when the averageannual distance driven in the vehicle changes based on theconsidered capacity and inventory [19]. This is caused by a linearrelation between the number of kilometres and the number ofstations required because the energy output is limited by the pro-duction ratio of the electrolyser.

Considering a 92.88% capacity factor, the ratio of vehicle perH2(b) refuelling station can be estimated using Eq. (22), which in-troduces the annual distance driven x.

VehiclesStation

¼ 2:1969E�06x2 � 9:0998E�02xþ 1:1463Eþ03 (22)

Following the same methodology, the Energy LCA can be esti-mated by (23) in MJeq/MJ, where Me can be calculated using (10).The losses considered are only related to electricity.

LCAMJeq H2ðbÞ ¼ Me

ð1� L%TGÞ � ð1� L%El&CoÞþ 1:52E�03

ð1� L%El&CoÞ

þ 3:55Eþ05�MJsold=year

� (23)

Table 3Input values considered for electricity pathway (chargers).

N Intc Nch ¼ 187.0a TTW ¼ 0.54Inte Nch ¼ 3975.89b LTimeNch ¼ 6 years [8]

Scenario/inputs %NchSR SRTotal AvrcarMJ/year %Nch

France 11.62% 1.108 7830 15%Portugal 10.47% 1.094 6966 15%USA 14.85% 1.148 10,260 15%

H Intc Hch ¼ 42.0c

Inte Hch ¼ 856.03d LTimeHch ¼ 15 years [8]Scenario/inputs %HchSR SRTotal AvrcarMJ/year %Hch

France 88.11% 1.108 7,830 79.5%Portugal 89.26% 1.094 6966 79.5%USA 84.88% 1.148 10,260 79.5%

F Intc Fch ¼ 3146.9e

Inte Fch ¼ 60016f LTimeFch ¼ 12 years [8]Scenario/inputs %FchSR SRTotal AvrcarMJ/year %FchFrance 0.27% 1.108 7830 5.5%Portugal 0.27% 1.094 6966 5.5%USA 0.27% 1.148 10,260 5.5%

a, c, e, b, d, f Refer to carbon and energy intensity of normal, home and fast chargersrespectively. TTW is the energy in MJ/km for an average EV [8].

Table 4Input values considered for H2(a) pathway.

Intplinea 3.54E�01 IntH2Plantc 0.17 IntH2statione 1.84Eþ07 TTW ¼ 1.25b �02 d �03 f þ05 g

A. Lucas et al. / Energy 56 (2013) 70e8076

4. Model verification and validation

There are two steps to evaluate how coherent a model is withrespect to the system. One must ascertain whether the model im-plements the assumptions and is built correctly (model verifica-tion) and whether the assumptions that have been made arereasonable with respect to the real system (model validation) [25].

Regarding verification, the model structure is very simple,which is typical of a LCA and is just a sum of the impacts that havebeen verified for each stage along the chain. The difficulties in thedeveloped model are the treatment of the support data, the correctpresentation of the functional unit, assuring that there is no doublecounting and checking that the most relevant system parametersare addressed. In addition, it is necessary tomake themodel easy touse but also to allow the users to input specific scenario data. TheStructured walk-through/One-step Analysis [25] technique wasused with the support of other researchers. Because a one-stepanalysis can take a long time, it is often applied to such simplifiedmodels. Other researchers’ contributions facilitated the building ofthe required simple and flexible model. In addition to this method,the Continuity Test [25] technique was applied. The model wastested several times for slightly different values of input parameters(charging shares, annual distances driven, electricity mix, andnumber of vehicles per stations). A slight change in the inputgenerally produced only a slight change in the output best estimatevalues; no discontinuities or sudden changes were observed in theoutput values.

Model validation essentially consists of demonstrating that themodel is a reasonable representation of the actual system. It may bedifficult in practice to achieve full validation of the assumptions,input parameters and distributions or output values of the model,especially if the system being modelled does not yet exist. For thisreason, the main assumptions of the service ratios and the numberof H2 (a) stations were assumptions that were based on expertjudgments with no current possibility of being validated by realmeasurements.

However, there are several approaches [25] used to validate amodel and any combination of these approaches may be consid-ered appropriate for a particular model; hence, three techniqueswere used. First, the Parameter Variability technique [25] was used,which consists of applying the model and changing its input valuesand internal parameters to determine the effect upon the model’sbehaviour or output. Three case scenarios with very different reallife inputs were used: Portugal (high renewable energy sourcespenetration in electric mix), France (high vehicle/station ratio) andUSA (high annual distance driven). This technique may be used forboth verification and validation purposes.

Second, the Face Validity or result analysis [25] technique wasused, where other researchers, individuals who were knowledge-able about the system, were asked whether the model and/or itsbehaviour was reasonable and adjustments weremade accordingly.

Finally, a Comparison with other data was performed. No othermodels were identified, so the results were compared with theexisting literature [8] that used Portugal as a case study.

Table 2Input values considered for electricity pathway (electric mix by country).

M1 M2 M3 M4 M5 M6 M7 M8

Electric mix %Coal Oil NGCC Nuclear Hydro Geo Wind Solar km/year2.5% 1.5% 6.5% 77.7% 9.3% 0.0% 2.2% 0.3% France 1450015.2% 1.0% 32.0% 0.0% 28.5% 1.0% 21.5% 0.8% Portugal 1290042.0% 1.0% 25.0% 19.0% 8.0% 1.0% 3.0% 1.0% USA 19100

4.1. Case scenarios

Three countries were chosen to apply the model. Portugal isused as a confirmation scenario because the present study is basedon previous studies [8,9] and due to its developed stage of infra-structure deployment. France has a particular electric mix, highlydependent on nuclear sources, and has been used as a reference forstudies addressing end distribution infrastructure. In the USA,several vehicle technologies are being analysed, and it presents oneof the most developed hydrogen infrastructures in the world,especially in California [26]. The differences between the averagedistance driven in a vehicle per year for each scenario will enhanceany impact the final energy demand has over each pathway.

Table 2 presents the inputs to the electricity pathwaymodel. Theelectric mix by country [27e29], required to calculate M, refers tothe year 2011. The annual distances driven considered are based onaverage real country data [8,23,30].

Table 3 presents all the inputs to calculate N, H and F using Eqs.(3)e(5), (11)e(13) in the electricity pathway. Eq. (6) was used toestimate the SRTotal in the Portugal, France and USA case studies.

Table 4 presents the input values used when applying H2(a) Eqs.(15) and (17). The inventory values are presented in units based onenergy output, not final energy. Because the ratio of vehicles perstation variable is crucial for the estimations, it was assumed thatthey were in the same proportion as conventional fuel stations ineach country. This ratio was obtained by dividing the number of

Intpline 2.59E IntH2Plant 3.03E IntH2station 3.20E CF ¼ 0.9Inputs L%H2plant L%pline L%Co km travelled/

yearMJ sold/station/year

Vehicles/Station

France 21% 2% 16% 14,500 5.65Eþ07 3118Portugal 21% 2% 16% 12,900 3.60Eþ07 2234USA 21% 2% 16% 19,000 4.69Eþ07 1974

a, b Refer to carbon and energy intensity of NG and H2 pipeline.c, d Refer to carbon and energy intensity of SMR plant.e,f Refer to carbon and energy intensity per year of H2 refuelling stations respec-tively. Units are in gCO2eq/MJ and MJeq/MJ respectively.g Refers to the capacity factor of the SMR plant. TTW is the energy in MJ/km for aFCHEV [8].

Table 5Input values considered for H2(b) pathway.

TTW 1.25 CF H2 statione 92.88%Int H2 Stationa gCO2eq/year 2.033Eþ07 Int TGc gCO2eq/MJ 0.07Int H2 Stationb MJeq/year 3.56Eþ05 Int TGd MJeq/MJ 1.52E�03

Inputs L%TG L%ElþCO MJ sold Vehicles/station

France 6% 43% 5.27Eþ06 290.56Portugal 8% 43% 5.27Eþ06 326.59USA 6% 43% 5.27Eþ06 221.74

a, b Refer to carbon and energy intensity per year of H2 refuelling stations withelectrolyser module.c, d Are the carbon and energy intensities of the transmission and distributionelectric grid. Units are in gCO2eq/MJ and MJeq/MJ respectively.e Refers to the capacity factor of the electrolyser. TTW is the energy in MJ/km for aFCHEV [8].

A. Lucas et al. / Energy 56 (2013) 70e80 77

light duty vehicles in each country by the number of existingconventional fuel stations [31e33]. The disparity in the values canbe explained by the different sizes of the station itself (number ofdispensers), among other reasons. However, this question wasconsidered to be negligible in the study, as the goal is to demon-strate the model application.

Regarding the H2(b) pathway, input values and losses are pre-sented in Table 5. Inventory values are presented in a unit based onthe energy output, not the final energy. The hydrogen refuellingstation inventory is based on Ref. [19]; the electrolyser has amaximum capacity rate of 47,250 kgH2/year. The Low HeatingValue was used to convert kgH2 to MJ units.

Table 6 shows the results for the energy supply infrastructureLCA for the pathways under study after applying general Eqs. (1),(9), (15), (17), (19) and (23). The results are presented for each ofthe terms in the equations (left to right). Overall, the Portuguesescenario reports higher values both of emissions and energy usefrom the infrastructure. This can be explained by the high pene-tration of renewable energy sources, in particular hydro and wind.The USA scenario presents very low impact from the infrastructurewith low variability. This is due to its coal- and NG-based electricmix because both technologies are mature and well documented.It also benefits from the high annual distance driven per vehicle

Table 6Results of energy and emissions LCA from energy supply infrastructure in France, Portug

gCO2eq/MJ-minemax (most likely)

France Portugal USA

ElectricityMix 2.0 3.2 2.0Grid 0.1 0.1 0.1N 3.4 3.4 3.5H 0.4 0.5 0.3F 1.8 2.1 1.5

Total 5.6e26.4 (7.8) 6.2e36.6 (9.3) 6.0e17.7 (7.3)

H2(a)NG pipeline 0.5 0.5 0.5H2 SMR plant 0.2 0.2 0.2H2 pipeline 0.4 0.4 0.4H2 station 0.3 0.5 0.4

Total 0.6e2.2 (1.5) 0.7e2.4 (1.7) 0.6e2.3 (1.6)

H2(b)Mix 2.0 3.2 2.0Grid 0.1 0.1 0.1H2 station 3.9 3.9 3.9

Total 4.4e24.3 (6.0) 5.3e34.1 (7.2) 5.0e16.3 (6.0)

value. Total values are presented in gCO2eq and MJeq by unit of finalenergy with the minimum, maximum and most likely values.

4.2. Results and analysis

The model was applied demonstrating appropriate structure,logic as well as correct causal relationships. Extreme real valueswere considered for the electricity mix, the ratio of vehicles tostation and the average annual distance driven per vehicle.

Regarding the scenario analysis, the French scenario reportslower LCA emissions and energy use for both hydrogen pathways.In terms of the potential use of the electric vehicle, the USA scenarioreports lower LCA values and the Portuguese scenario reports thehighest values in all pathways. The comparison of the three sce-narios studies is presented in Fig. 7.

A characteristic that stands out is the high uncertainty in elec-tricity and H2(b) pathways of the Portuguese scenario. This isexplained by a high contribution of hydroelectricity to the electricmix when compared with the other two scenarios. The revisedliterature presents an extreme variability of results for this elec-tricity source, as both the scale and location of the plant are criticalfactors. The reduction of uncertainty in the result by improving thequality of these data or distribution characterisation is recom-mended. In addition to variability, this scenario also presentshigher values for both emissions and energy use. In the electricityand H2(b) pathways, in addition to the generation mix, a high valueof contribution from fast chargers is also verified in comparisonwith the other two scenarios. This can be explained by the inferiordistance driven per year of the average vehicle, which, despiteresulting in a lower total service ratio, is penalised by the non-linear relation with the required energy from the charger, whichis inferior.

The second highest scenario in terms of uncertainty is France.This is due to the 77% nuclear power contribution to the electricmix. Nuclear has also a high variability of results, as there are manytechnologies that can be employed and addressed in the studiesrevised. An improvement of the quality of this input is also advisedto reduce the resultant total uncertainty amplitude. The USA sce-nario benefits from low intensity and low variability of the power

al and USA.

MJeq/MJ-minemax (most likely)

France Portugal USA

9.03E�03 3.24E�02 1.17E�02

1.52E�03 1.52E�03 1.52E�03

7.26E�02 7.26E�02 7.34E�02

8.95E�03 1.01E�02 6.82E�03

3.53E�02 3.91E�02 2.79E�02

0.08e0.20 (0.13) 0.10e0.32 (0. 16) 0.07e0.20 (0.12)

3.99E�02 3.99E�02 3.99E�02

4.11E�03 4.11E�03 4.11E�03

3.08E�02 3.08E�02 3.08E�02

5.66E�03 8.88E�03 6.82E�03

0.02e0.12 (0.08) 0.02e0.13 (0.08) 0.02e0.12 (0.08)

1.54E�02 5.64E�02 2.04E�02

2.64E�03 2.64E�03 2.64E�03

6.74E�02 6.74E�02 6.74E�02

0.06e0.18 (0.08) 0.07e0.36 (0. 13) 0.06e0.18 (0.09)

Fig. 7. Emissions (a) and energy (b) LCA of energy supply infrastructures for France, Portugal an USA scenarios.

Fig. 8. Model results for Portugal case scenario comparison of with literature [8].

A. Lucas et al. / Energy 56 (2013) 70e8078

plants infrastructure LCA, as it bases its generation mix in matureconventional technologies.

Losses are not a significantly distinct factor between the sce-narios, as the transport and end distribution infrastructures alongthe pathways are fairly similar. Regarding the H2(a) pathway, thevariable contributing the most to scenario differentiation is thenumber of vehicles per station considered. The average ratio ofvehicles per conventional fuel station in each country wasconsidered to represent the ratio of vehicles per H2(a) refuellingstation. An high disparity is verified within the three scenariosranging from 3118 vehicles in France to 1974 vehicles in the USA perstation. However, not only is this ratio important, but the energysupplied by each station should also be analysed. On average, in theFrench scenario more energy would be supplied per station in ayear despite having an inferior annual distance driven. This causesboth emissions and energy use to be inferior to the other twoscenarios. The Portuguese scenario is penalised by having a lowerratio of vehicles per station than France and, consequently, bysupplying less energy on each one.

The output behaviour is reasonable in terms of the direction andamplitude of results. A model is considered valid for a set ofexperimental conditions if the model’s accuracy is within itsacceptable range, which is the amount of accuracy required for themodel’s intended purpose. Although high uncertainty is verified inall pathways, it is a trade-off with the simplicity of the model’sstructure.

4.3. Comparison to literature results

Fig. 8 shows a comparison of the model results with the onesfound in the literature [8]. As expected, uncertainty values are

higher in the model than in the case study under comparison.However, differences show consistency as the case study valuesare within the model’s range. Regarding the best estimate value,differences are reported in different directions; however, this is notdue to lack of accuracy.

For the electricity pathway, this fact is explained by the inclu-sion of impact uncertainty in the chargers inventories, which alsoconsiders lower impact values than the ones used in the case study;hence, the average value of both emissions and the energy useinfrastructure LCA is decreased.

Regarding the H2(a) pathway, emissions and energy use impactvary in the same direction. An increase of the best estimate isverified in comparison with the case study values. This change ismostly given by inventory data treatment using the pedigree ma-trix [21], which not only changed the uncertainty range but also thebest estimate value. Concerning the H2(b) differences, the emis-sions in the model show a decrease in comparison with the emis-sions in the case study. On the contrary, the energy use resultsreport an increase. This is given by changes in the pondering factorsof the power plants compiled by all the literature estimates. Thisdifference in direction also occurs in the electricity pathway, but itis concealed by a higher influence caused by the impact uncertaintyinclusion. The accuracy is acceptable and depends on the type ofvariables inputted. Higher accuracy could be obtained through dataquality improvement. The model, however, correctly fulfils thepurpose of the analysis.

5. Conclusions

A model is proposed that focuses on the electricity andhydrogen supply through centralised steam methane reforming

A. Lucas et al. / Energy 56 (2013) 70e80 79

and on-site electrolysis. Verification and validationwere performedmainly by Face Validity, a Scenario Application using real data fromFrance, Portugal and USA and by literature Comparison techniques.The model shows consistency in the results and in the uncertaintyrange. The evaluation of the service ratios of electric chargers andstations as a function of the annual distance driven is alsopresented.

The model is consistent with the values from a previous analysisperformed by the authors [8], which used Portugal as a case study.Uncertainty values, as expected in a generalisation study, haveincreased, mainly due to the inclusion of the estimations found inthe literature of the different power plants, the inclusion of theimpact uncertainty and the application of the pedigree matrix inthe inventories. Improving data quality or the characterisation ofthe statistical distributions and future real measurement validationis advised.

The French scenario reported lower LCA emissions andenergy use for both hydrogen pathways, with 0.6e2.2 gCO2eq/MJand 0.02e0.12 MJeq/MJ for H2(a) and 4.4e24.3 gCO2eq/MJ and0.06e0.18 MJeq/MJ for H2(b). Regarding the potential use of theelectric vehicle, the USA scenario reports lower LCA values with6.0e17.7 gCO2eq/MJ and 0.07e0.20MJeq/MJ. The Portuguese scenarioreported the highest values in all pathways,with 6.2e36.6 gCO2eq/MJand 0.10e0.32 MJeq/MJ for the electricity pathway, 0.7e2.4 gCO2eq/MJ and 0.02e0.13 MJeq/MJ for the H2(a) pathway and 5.3e34.1 gCO2eq/MJ and 0.07e0.36 MJeq/MJ for H2(b). This reveals that acountry with high penetration of renewable sources has a tendencyto have higher contribution to LCA from the infrastructure. Nuclearenergy on the other hand, although having higher impacts from theinfrastructure than conventional technologies, improves the overallburden as it has negligible generation emissions.

The model indicates that, with the considered assumptions, theenvironmental infrastructure burden of the electricity pathway isminimised with a policy which incentivises approximately 37% ofthe charge from normal chargers. Regarding the H2(a) pathway, thepipeline infrastructure is the most emission and energy intensive.Efforts to increase its lifetime should be exerted to reduce the overallimpact. Regarding H2(b), as the ratio of vehicles per station is limitedby the electrolyser production, increasing the installed capacitywould decrease the impact per MJ supplied. Efforts should be madeto approximate this ratio to one of a conventional fuel station.

Acknowledgements

The authors would like to thank the MIT-PP and the support ofall companies that made this study possible, in particular EFACECfor the financial support. Special thanks to Elsevier LanguageEditing Services.

Appendix A. Supplementary material

Supplementary material related to this article can be found athttp://dx.doi.org/10.1016/j.energy.2013.04.056.

Nomenclature

CED Cumulative Energy DemandCF Capacity FactorCTG Cradle-to-GraveEV Full Electric VehicleEInst.cap. energy installed capacityFCHEV Fuel Cell Hybrid Electric VehicleFCPHEV fuel cell plug-in hybrid electric vehiclesFe fast charger energy impactFc fast charger emissions impact

GWP Global Warming PotentialHc home charger emissions impactHe home charger energy impactICEV Internal Combustion Engine VehicleIntpline pipeline carbon/energy intensityIntH2Plant H2 plant carbon/energy intensityIntH2station H2 station carbon/energy intensityIntTG transmission Grid carbon/energy intensityIntc Fch fast charger carbon intensityInte Fch fast charger energy intensityIntc Hch home charger carbon intensityInte Hch home charger energy intensityIntc Nch normal charger carbon intensityInte Nch normal charger energy intensityLCA Life Cycle AnalysisLTimeNch normal charger lifetimeLTimeHch home charger lifetimeLTimeFch fast charger lifetimeL%TG losses in transmission gridL%pline losses in pipelineL%H2Plant losses in H2 plantL%El&CO losses in electrolyser and compressorL%Co losses in the H2 compressorMJex energy expendedMJf final energyMc infrastructure carbon intensity of electric mixMe infrastructure energy intensity of electric mixNc normal charger emissions impactNG Natural GasNe normal charger energy impactNGCC Natural Gas Combined CycleSMR Steam Methane ReformingTTW tank-to-wheelUSA United States of AmericaWTT well-to-tankWTW well-to-wheelx annual average distance driven per vehicle%NchSR normal chargers share of service ratio%HchSR home chargers share of service ratio%FchSR fast chargers share of service ratioSRTotal total service ratio (chargers/vehicle)AvrcarMJ/year vehicle average energy required per year%Nch average share of normal charging%Hch average share of home charging%Fch average share of fast charging

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