concawe symposium 2019, antwerp · source: ricardo energy & environment sultan modelling and...

Impact Analysis of Mass EV

Adoption and Low Carbon

Intensity Fuels Scenarios

Nikolas Hill

13th Concawe Symposium 2019, Antwerp

18 March 2019

2© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019

Ricardo is a Global Engineering & Environmental Consultancy with

over 2900 engineers, scientists and consultants in all major regions

Sectors

Automotive

Commercial

Vehicles

Defence

Energy &

Environment

Motorsport

Motorcycle

Off-Highway

Vehicles

Rail


• Concawe commissioned Ricardo to conduct research and provide analysis of

the wider potential implications of high EV uptake and alternative scenarios

The impact of two scenarios: ‘High EV Adoption’ & ‘Low Carbon Fuels’

on GHG emissions, infrastructure, costs & resources are compared

Note: * The scenarios consider the European light duty vehicle fleet only. L-category vehicles, buses, and medium and heavy duty trucks have not been included in the analysis

# Low Carbon Fuels include biofuels and eFuels generated from renewable energy sources

High EV Scenario Low Carbon Fuels

Scenario

3. What are the cost

implications?

2. What are the implications for

energy supply and electricity

infrastructure?

4. What are the implications on

materials and natural

resources?

1. What are the Greenhouse Gas

(GHG) emissions, including

life cycle emissions?CO2


Quantitative analysis of impacts was conducted using SULTAN

model with defined inputs, and post-processing of the results

Input Data / Pre-processing Output Data / Post-processing

SULTAN

Scenario Database

and Calculation

Engine

Activity by mode

Vehicle Energy Consumption [MJ/km]• By mode, model year, powertrain type *

GHG Emission Factors [CO2e/MJ]• By fuel / energy carrier **

• TTW and WTW

AQP Emission Factors• By mode fuel / powertrain

• Direct emission factors for NOx, SOx, PM

Cost Data• Fuel costs (excl./incl. tax) **

• Capital costs by powertrain

• O&M costs

Vehicle stock• Fleet # projection by mode

• Survival rates

• % share of new vehicles by powertrain *

Results Database and

SULTAN Results Viewer

Notes:

* Key input variable, set by the scenarios

developed for this study

** Input variable to sensitivity scenarios for

this study, e.g. electricity CO2e/kWh, energy

prices, etc.

*** Input data for calculations informed by

Literature Review and Deep Dive analysis

SULTAN Outputs• Fleet numbers / mix by powertrain

• Energy consumption by fuel

• TTW, WTW, AQP emissions

• Energy Security metrics

• Economic outputs (social, end-user)

– TCO: Capital, Fuel and O&M costs

– Net fiscal revenue impact

– External costs of emissions

– Cumulative costs

Additional Post-Processing ***• GHG emissions from vehicle production

and disposal

• Alternative fuel infrastructure requirements

(# by type, share of energy cons., costs)

• Resource requirements

Vehicle numbers by powertrain

Additional Final Results, e.g.• Total life cycle GHG emissions

• Total costs including infrastructure

Scenario Modelling Calculations

Source: Ricardo Energy & Environment. * SULTAN is a Ricardo tool developed for the European Commission as a transport policy modelling tool, with the ability to evaluate the medium- and long-

term (to 2050) impacts of new vehicle technologies

Overview of the SULTAN* modelling analysis


EU Passenger Car Vehicle Parc

0%

20%

40%

60%

80%

100%

2015 2020 2025 2030 2035 2040 2045 2050

Plug-in Share:

91.3 %

Plug-in Share:

46.5 %

0%

20%

40%

60%

80%

100%

Note: New registrations & vehicle parc profiles calibrated to data and projections from European Commission modelling -Impact Analysis of Mass EV Adoption and Low Carbon Intensity Fuels

Scenarios, Ricardo report for CONCAWE August 2018, https://www.concawe.eu/publications

“H

igh

EV

”

Scen

ari

o

“L

ow

Carb

on

Fu

els

” S

cen

ari

o

68% of energy from

Low Carbon Fuels

in 2050

88% of energy from

Electricity in 2050

• Both scenarios were scoped to deliver Tank-to-Wheel (TTW) GHG savings of 90 percent

compared with 1990 figures

Two contrasting scenario options : What if all cars were electric?

What if the share of Low Carbon Fuels was significantly increased?


“H

igh

EV

”

Scen

ari

o

“L

ow

Carb

on

Fu

els

” S

cen

ari

o

0%

20%

40%

60%

80%

100%

0%

20%

40%

60%

80%

100%

2015 2020 2025 2030 2035 2040 2045 2050

Fossil fuel

Bio-fuel

E-Fuel

Electricity

% Fuel Share by energy

• In the High EV scenario 88% of

energy is electrical by 2050

• In the Low Carbon fuels scenario 68%

of energy is from bio-fuels and e-fuels

and 22% from electricity

Two contrasting scenario options : What if all cars were electric?

What if the share of Low Carbon Fuels was significantly increased?


• Net GHG reduction for biofuels is

assumed to reach ~85% by 2050

• After 2020: assumed that the share

of low/no-ILUC biofuel (i.e. from

waste or non-crop feedstocks) will

increase to >95% share by 2050

• For the Low carbon fuels scenario:

– It is assumed that the majority

of biodiesel used post-2025 will

be drop-in fuels (including syn-

diesel, eFuels and HVO) and by

2050 substitution reaches 100%

– Gasoline is also mainly replaced

by advanced biofuels (synthetic

gasoline) and substitution nears

80% by 2050

Both scenarios feature substitution of conventional liquid fuels by

biofuels, with a higher share in the Low Carbon Fuels scenario

European scenarios for biofuel and other low carbon fuel uptake

0%

5%

10%

15%

20%

25%

30%

2015 2020 2025 2030 2035 2040 2045 2050

% s

ha

re

Biodiesel

Total

Biogasoline

Biomethane

BioLPG

Source: Analysis by Ricardo Energy & Environment based on previous work for the EC and other European projects, and the availability (in PJ) of low carbon fuels developed by CONCAWE

Low carbon fuel substitution by energy carrier, High EV scenario

Low carbon fuel substitution by energy carrier, LCFuels scenario

Hig

h E

VL

ow

Ca

rbo

n F

ue

ls

0%

20%

40%

60%

80%

100%

2015 2020 2025 2030 2035 2040 2045 2050

% s

ha

reThe total volume of bio-fuels is within that assumed to be available for LDVs


0

10

20

30

40

0

200

400

600

800

1000

1200

1400

1600

1800

2020 2025 2030 2035 2040 2045 2050

Mto

e/y

ear

PJ/y

ear

Syndiesel(imported eFuel)

Syndiesel (EUeFuel)

Syndiesel(pyrolysis)

Syndiesel(gasification)

HVO

FAME

The energy available from biofuels and eFuels for European light

duty vehicles has been estimated from other research sources

0

10

20

30

40

0

200

400

600

800

1000

1200

1400

1600

1800

2020 2025 2030 2035 2040 2045 2050

Mto

e/y

ea

r

PJ/y

ear

Biogasoline(imported eFuel)

Biogasoline (EUeFuel)

Biogasoline(gasification)

Ethanol (2Glignocellulosic)

Biogasoline(pyrolysis)

Ethanol (1G)

Bio

eth

an

ol a

nd

bio

ga

so

lin

eF

AM

E a

nd

syn

die

se

l

• Availability of Low Carbon Fuel is

intended to reflect scenario where

the whole biomass supply chain is

optimised to maximise use of

bioenergy

• Quantities available to LDVs allow

for similar substitution levels in

other road transport (e.g. HDVs)

• Use in other transport modes is

not considered explicitly

Source: Directorate-General for Research and Innovation (European Commission), “Research and innovation perspective of the mid-and long-term potential for advanced biofuels in Europe,” 2018;

K. Sub Group on Advanced Biofuels Sustainable Transport Forum, Maniatis, I. Landälv, L. Waldheim, E. Van Den Heuvel, and S. Kalligeros, “Final Report, Building Up the Future,” 2017;

dena (German Energy Agency), “«E-FUELS» STUDY - The potential of electricity-based fuels for low-emission transport in the EU - VDA,” 2017;

H. D. C. Hamje et al., “EU renewable energy targets in 2020: Revised analysis of scenarios for transport fuels.”


End-of-Life

Adds assessment of environmental

impact of “end of life” scenario (i.e. -

to-Grave). Can include: re-using or

re-purposing components,

recycling materials, energy

recovery, and disposal to landfill

Vehicle Production

Assessment of ‘Cradle-to-Gate’

environmental impact of producing

the vehicle including extract of raw

materials, processing, component

manufacture, logistics, vehicle

assembly and painting

Vehicle cycle “Embedded”

emissions result from from vehicle

production; fluid, filter and component

replacement during life; and end-of-

life activities. A “cradle-to-gate” LCA

study may only consider vehicle or

component production

Well-to-Wheel (WTW) Analysis -

Life Cycle Assessment of the fuel or

electricity used to power the vehicle

Fuel & Electricity

Production

Assessment of (WTT)

environmental impact of producing

the energy vector(s) from primary

energy source to point of

distribution (e.g. refuelling station)

The analysis considered the whole life of the vehicle, Well-to-Wheel

(WTW) fuel production and use and embedded GHG emissions

Vehicle Life Cycle

Use/Operation

• Environmental impact of driving

(TTW emissions)

• Impact from maintenance and

servicing

Study Boundary:

Analysis of the whole vehicle life

lifecycle included embedded

emissions from vehicle production,

maintenance and servicing, and end-

of-life activities, and WTW

(WTT+TTW) emissions from

production and use of the fuel /

energy in operating the vehicle


BAU Total

0

200

400

600

800

2015 2020 2025 2030 2035 2040 2045 2050

GH

G E

mis

sio

ns

[MtC

O2

e]

EU Light Duty Vehicle Emissions (Well-to-Wheel + Embedded)

Both scenarios result in a similar and significant reduction in GHG

emissions to 2050, with WTW GHG savings reduced 92% vs 1990

Tank-to-Wheels

Well-to-Tank

(Annual) Vehicle

Disposal

(Annual) Vehicle

Production

Embedded emissions from production

and disposal rises to ~25% by 2050

for both the Low Carbon Fuels and the

High-EV scenario

Hig

h E

VL

ow

Ca

rbo

n F

ue

ls

Source: Ricardo Energy & Environment SULTAN modelling and analysis * BAU scenario as used by European Commission as a baseline for quantifying the impact of future policy changes

124

BAU Total

0

200

400

600

800

2015 2020 2025 2030 2035 2040 2045 2050

GH

G E

mis

sio

ns

[MtC

O2

e]

624

624

Both scenarios

result in significant

reduction from

Business As Usual

(BAU) EU reference

Both scenarios

result in significant

reduction from

Business-As-Usual

(BAU) EU reference

135


Sensitivities on electricity GHG intensity affect which scenario

results in lower GHG emissions

Sensitivities on Electricity GHG intensity vs base High EV scenario

Source: Ricardo Energy & Environment SULTAN modelling and analysis

+33 MtCO2e

-8 MtCO2e

“Low” electricity GHG intensity

High EV Scenario produces 8Mt CO2

LESS than the Low Carbon Fuels

Scenario

Electricity Global Warming

Potential (GWP) [kgCO2/kWh]

“High” electricity GHG intensity

High EV Scenario produces 33Mt

CO2 MORE than the Low Carbon

Fuels Scenario

Total GHG emissions

High EV Scenario

Low Carbon Fuels Scenario

→ Sensitivities on low carbon fuel availability show similar

magnitude of variations


Network reinforcement required beyond 15-20% EV penetration to

deliver adequate EV re-charge power will be significant*

Source: The Energy Technologies Institute & *Household Electricity Survey - A study of domestic electrical product usage Intertek Report R66141 ; Impact Analysis of Mass EV Adoption – Ricardo;

Defossilizing the transportation sector - FVV

400 kV / 275 kVTransmission

Network

EHV (132 kV)Extra High Voltage

Network

HV (33 kV – 22 kV)

MV (11 kV – 10

kV)

LV (400 V three phase

- 230 V single phase)

High Voltage (HV)

Network

Grid Supply Point

Bulk Supply Point

Primary Substation

Secondary Substation

Low Voltage (LV)

Distribution Network

Medium Voltage (MV)

Network

Generation

Significant Re-enforcement Required

• Capital costs for re-enforcing EU EV charging

infrastructure & charge facilities for High EV scenario

– €630 billion assuming primarily “home” charging

(€326 billion for Low Carbon Fuels)

– €830 billion assuming “grazing” frequent top-up

• Based on “Smart” network with charge periods

selected to minimise local network loads

Only a small part of total road

transport costs including vehicles

and energy, but who pays for this?

• Electricity for EV charging increases to ~550 TWh

in 2050, ~17.5% EU 2015 electricity generation


The annual parc total costs to the end user are similar for the High EV

and Low Carbon Fuels scenarios if lost fuel tax revenue is considered

Cost of electricity infrastructure upgrades

Operation and maintenance costs

Fuel / energy costs

Capital (vehicle)

Net Fiscal Revenue (NFR) loss: Reduction

in fuel tax receipts to governments

Total BAU 2,280

2,263

0

500

1,000

1,500

2,000

2,500

2015 2020 2025 2030 2035 2040 2045 2050

An

nu

al C

ost [B

illio

n €

]

Total Parc Annual Costs to End-user for All Light Duty Vehicles

Hig

h E

V

Total BAU 2,280

2,254

0

500

1,000

1,500

2,000

2,500

2015 2020 2025 2030 2035 2040 2045 2050An

nu

al C

ost [B

illio

n €

]

Lo

w C

arb

on

Fu

els


Note: *Including infrastructure costs but excluding adjustment for Net Fiscal Revenue **BAU scenario as used by European Commission as a baseline for quantifying the impact of future policy changes

• Total annual parc cost to the end

user is similar for both scenarios in

2050, when adjusting to maintain Net

Fiscal Revenue

Taxes are applied for all energy carriers at their current and projected (BAU) levels

Cumulative cost savings to the

end end-user between ~€1,100 &

€1,600bn (1.3% - 1.8%) vs EC

BAU reference to 2050


• Emission externalities

contribute to significantly

lower social costs for

both scenarios

• Note: Societal costs exclude all taxes

The net societal cumulative costs are lower for High EV scenario

only in later periods

Overall cumulative cost-

effectiveness is best for

the other scenarios up to

2045-2050

Cumulative net societal

costs are significantly

higher for the High EV

scenario in earlier periodsCumulative Net Societal Costs (relative to High EV)


Cu

mu

lati

ve

So

cie

tal C

os

t

-150

-100

-50

0

50

100

150

2015 2020 2025 2030 2035 2040 2045 2050

Cum

ula

tive C

ost

[Bill

ion €

]

High EV

LCFuels

High EV+externalities

LCFuels+externalities

Total Parc Annual Societal Costs (excl. tax), including Externalities

Hig

h E

V

BAU Total

0

200

400

600

800

1,000

1,200

1,400

1,600

2015 2020 2025 2030 2035 2040 2045 2050

Annual C

ost

[Bill

ion €

]

Externalities

Infrastructure

O&M

Fuel

Capital

External costs (or ‘externalities’) are the monetary value attached to GHG and Air Quality emissions


• The High EV scenario

requires almost three times

the total battery capacity

compared to the Low Carbon

Fuels scenario

Under the High EV scenario, ~15 Gigafactories would be needed to

supply batteries to the European EV market by 2050

Resources & Materials – Annual Battery Capacity [GWh]

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2015 2020 2025 2030 2035 2040 2045 2050

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2015 2020 2025 2030 2035 2040 2045 2050Source: Ricardo Energy & Environment SULTAN modelling and analysis;

* Tesla (https://www.tesla.com/en_CA/gigafactory)

Hig

h E

VL

ow

Ca

rbo

n F

ue

ls

Note: Tesla Giga Factory

estimates factor in anticipated

battery energy density

improvements per unit from

2025-2050* This output should

be expected to scale with

increased battery kg/Wh

Estimated number of

battery Giga-factories

required for Europe

Tota

l B

atte

ry C

apacity r

equired [

GW

h]


0

50,000

100,000

150,000

200,000

250,000

300,000

2015 2020 2025 2030 2035 2040 2045 2050

0

50,000

100,000

150,000

200,000

250,000

300,000

2015 2020 2025 2030 2035 2040 2045 2050

The Lithium resource requirements for the Low Carbon Fuels

scenario are less than half of those for the High EV scenario

Resources & Materials – Key Battery Materials [tonnes], annual demand

3

LiLithium

27

CoCobalt

28

NiNickel

Source: U.S Geological Survey (Mineral Commodity Summaries 2017);

Ricardo Energy & Environment Sultan Modelling And Analysis

The use of Cobalt and Nickel in

battery chemistries is expected to be

phased out between 2030 and 2040:

the share after this is uncertain

Hig

h E

VL

ow

Carb

on

Fu

els

• Assuming current chemistry

mixes the resource

requirements for Lithium,

Cobalt and Nickel would

increase very substantially

over the period to 2050,

which would pose a potential

availability risk

• Current global total

production p.a.:

– Li : 35 kt

– Co : 123 kt

Current Co production:

123,000 t

Current Li production:

35,000 t

Current Co production:

123,000 t

Current Li production:

35,000 t

Ma

terial R

equired in E

U [

tonnes]


• A broad range of

solutions should be

considered,

including

electrification and

liquid or gaseous

fuels, to minimise the

risks associated with

a single scenario

• The efficiency and

emissions of ICEVs

will need to continue

to develop, and to

accept future low

carbon fuels

UncertaintiesPositives

● No behaviour change

in refuelling

● Allows greater use of

our existing

manufacturing skills

and assets

● Low carbon fuel supply chain

and processes scale up

● Development to deliver zero

impact on air quality from

tailpipe

● Most efficient use

renewable electricity

● Free up low carbon fuel

supplies for other

transport applications

● Battery costs and improvements

in energy density

● Investment in charging

infrastructure and electricity

distribution network

● Availability of resources for

batteries (e.g. Lithium & Cobalt)

“H

igh

EV

”

Scen

ari

o

“L

ow

Carb

on

Fu

els

” S

cen

ari

o

Both scenarios have significant challenges. A broad range of

solutions, including liquid low carbon fuels, would minimise the

risks in achieving our future targets

Nikolas Hill

Ricardo Energy & Environment

The Gemini Building

Fermi Avenue

Harwell, Didcot

OX11 0QR

United Kingdom

T: +44 (0)1235 75 3522

E: [email protected]


Dr Nick Powell

Ricardo Strategic Consulting

Shoreham Technical Centre

Old Shoreham Rd

Shoreham-by-Sea

BN43 5FG

United Kingdom

T: +44 (0)1273 794524


mailto:[email protected]




Additional slides


Why are we interested? Combination of changes in the regulatory

environment, as well as the uptake of new fuels and powertrains

32.329.0

0.0

42.039.9

14.6

46.344.2

21.7

34.231.6

10.1

0

5

10

15

20

25

30

35

40

45

50

GasolineICE

G-ICE(+10%bio)

BEV GasolineICE

G-ICE(+10%bio)

BEV GasolineICE

G-ICE(+10%bio)

BEV GasolineICE

G-ICE(+10%bio)

BEV

Tailpipe Only Well-To-Wheel Vehicle Lifecycle Vehicle Lifecycle

2015 2030

GH

G E

mis

sio

ns [tC

O2e

]

Vehicle Use Fuel / Electricity Production Vehicle Embedded Emissions Total

Source: Ricardo life cycle analysis (2018) for average EU passenger car. Assumes lifetime 210,000 km, uplift of NEDC to real-world fuel consumption (~35%/40% for ICE/EV). GHG from fuel/electricity

consumption is based on the 2015 average fuel/grid electricity factor; Literature average 15.3 kgCO2e/kg battery. Avoided burden approach – including credits for recycling.

Historical; CO2

Regulations

-10% -100% -5% -65% -4.5% -53% -8% -70%

Biofuels

deployed; RED

Increase in xEV

Deployment

Future

Developments

Av. Car:

ILLUSTRATIVE

Tailpipe

counted as

zero for bio

component


Primary Energy Air Quality Pollutants

pics • How much / what

types of source?

• What is the most

efficient use of

renewables?

– BEV = 1x

– FCEV = ~3x

– eFuel = ~5x

• Emissions impacts

vary by:

– Powertrain

– Lifecycle stage

– Location

• Can influence

conclusions

Resources [ Lifecycle Costs, i.e. TCO ]

• Availability of key

materials for

batteries, motors

• Biomass supply for

bioenergy and

other uses

• Water consumption

• Total Cost of

Ownership

≈ LCA for money

• Environmental

externalities

(costs) added for

societal analysis

Introduction, background and objectives of the workshop [09:30]

It isn’t all about Greenhouse Gases – other impacts and factors also

influence the overall comparisons of impacts…for example:

3

LiLithium

27

CoCobalt

28

NiNickel

0

50,000

100,000

150,000

200,000

250,000

300,000

2015 2020 2025 2030 2035 2040 2045 2050

Mate

rial

Required [

tonnes]


Key Inputs & Assumptions

The price of liquid fuels and electricity has been based on data from

published studies

0.99

0.87

0.83

0.78

0.55

0.65

0.75

0.85

0.95

1.05

1.15

2025 2030 2035 2040 2045 2050

€/litre

ga

so

line

e

qu

iva

len

t

High price: av. low carbon fuel price

Base case: av. syndiesel price

Diesel price

Gasoline price

Base case: av. low carbon fuel price

Base case: av. biogasoline price

Source: S. Frank et al., “EU Reference Scenario 2016 Energy, transport and GHG emissions Trends to 2050.”, 2016

Directorate-General for Research and Innovation (European Commission), “Research and innovation perspective of the mid-and long-term potential for advanced biofuels in Europe,” 2018;

K. Sub Group on Advanced Biofuels Sustainable Transport Forum, Maniatis, I. Landälv, L. Waldheim, E. Van Den Heuvel, and S. Kalligeros, “Final Report, Building Up the Future,” 2017;

dena (German Energy Agency), “«E-FUELS» STUDY - The potential of electricity-based fuels for low-emission transport in the EU - VDA,” 2017;

H. D. C. Hamje et al., “EU renewable energy targets in 2020: Revised analysis of scenarios for transport fuels.”

0.185

0.165

0.207

0.12

0.14

0.16

0.18

0.20

0.22

2015 2020 2025 2030 2035 2040 2045 2050

Ele

ctr

icity P

rice

[€

/kW

h]

Electricity Price [€/kWh, excl. tax]

Low Carbon Fuels Price [€/litre eq., excl. tax]

European Electricity Scenario

Sensitivity “High GHG” Scenario

Sensitivity “Low GHG” Scenario

Assumed Energy Cost Trends Excluding Taxes

Sensitivity studies were also carried out to understand the affect of electricity and fuel price

• Assumptions about energy costs are

based on the references below


169

95

60

183

117

72

142

72

42

429

190

130

100

0

50

100

150

200

250

300

350

400

450

2015 2020 2025 2030 2035 2040 2045 2050

Ba

tte

ry P

ack C

ost [$

/kW

h]

Key Inputs & Assumptions

Battery costs are a key component of EV costs. Cost/kWh is

expected to decline by over 70% by 2030 compared to prices in 2015

Assumed Technology Cost Trends – Battery Pack

Source: Ricardo Energy & Environment analysis

Battery Pack Cost [$/kWh]

Typical / Central Case

Sensitivity “High” Scenario

Sensitivity “Low” Scenario

Sensitivity “Very High” Scenario

Sensitivity studies were also carried out to understand the affect of battery cost on overall costs

• Estimates for future battery pack costs

(including assembly) are based on

learning-based cost analysis developed

as part of work for the European

Commission

• Battery costs are used together with

electric range and State of Charge

assumptions to calculate the costs of

baseline xEV powertrain vehicles relative

to conventional equivalents

• Assembly of the battery pack into the

vehicle is considered in vehicle costs

• Sensitivity studies were carried out for

‘Low’, ‘High’ and ‘Very High’ battery cost

trends

• The average battery pack size for an EV

passenger car in 2050 is assumed to be

82kWh (108kWh for an average Light

Commercial Vehicle)

– Battery pack energy density assumed

to increase to 800Wh/kg by 2050


To investigate the implication for network infrastructure, Ricardo

has considered a series of recharging scenarios for plug-in vehicles

Home charging is where users charge

mainly using off-street home or on-street

residential recharging infrastructure

Same charging type split as “Home

Unmanaged”, but with longer time

periods to simulate managed charging

Recharging Scenarios

Home

Unmanaged

Grazing is where users charge little and

often, mainly using charging points away

from the home

Grazing

Unmanaged

Home

Managed

Same charging type split as “Grazing

Unmanaged”, but with longer time

periods to simulate managed charging

Grazing

Managed

Notes: Current EU housing data shows 28% of households are located in rural environments, and 72% are located in urban and sub-

urban environments. Therefore, Ricardo has assumed an EV electricity demand split of 28% for rural charging and 72% for urban

charging, applied to all four scenarios. Urban includes both urban and sub-urban properties

Source: Eurostat (online data code: ilc_lvho01)

Based on total electrical energy requirements calculated by SULTAN


Twice the electrical energy is required for EVs in the High EV scenario

compared to the Low Carbon fuels scenario (550 TWh vs 289 TWh)

0

100

200

300

400

500

600

2015 2020 2025 2030 2035 2040 2045 2050

En

erg

y C

on

su

mp

tio

n

[TW

h]

Electricity consumption from recharging by location (Home Charging Scenario)

0

100

200

300

400

500

600

2015 2020 2025 2030 2035 2040 2045 2050

En

erg

y C

on

su

mp

tio

n

[TW

h]

Public rapid

Public

convenience

Commercial

depot

Workplace

On-street

home

Off-street

home

Source: Ricardo Energy & Environment SULTAN modelling and analysis Note: * Including EU electricity used to produce EU eFuels decreases the difference to 39% lower for Low Carbon Fuel vs High EV

Hig

h E

VL

ow

Ca

rbo

n F

ue

ls

Unmanaged charging would require

significantly more upgrades to Low

Voltage (LV) networks to support off-

street and on-street charging (and

therefore much higher cost – more than

double the cost cumulatively to 2050)

• EV charging electricity demand in

2050 in the managed home

charging scenario is ~550 TWh

(1980 PJ)

– ~17.5% of the EU’s 2015

electricity generation

• For ‘Home’ charging scenario, most

of this energy (~60%) is expected

from overnight residential charging

• Requirements are ~47%* lower in

the Low Carbon Fuels scenario,

with a higher share of charging

from residential/home

concawe symposium 2019, antwerp · source: ricardo energy & environment sultan modelling and...

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