electro-mobility modelling

ELECTRO-MOBILITY MODELLING

Christian Thiel and Gillian Harrison

Joint Research Centre, Institute for Energy and Transport

University of Leeds 29/04/15

OUTLINE

• BACKGROUND

• Joint Research Centre

• Electro-mobility

• ELECTRO-MOBILITY MODELLING

• Market Agent Model

• Background

• Description

• Fleet Impact Tool

• Geospatial Allocation

• SUMMARY AND DISCUSSION

2

Commissioner Tibor Navracsics Education, Culture, Youth & Sport

President Jean-Claude Juncker 28 Commission Members

Director Giovanni F. De Santi JRC Institute for Energy and Transport

Director-General Vladimír Šucha Joint Research Centre

7 JRC Institutes

BACKGROUND

European Commission Joint Research Centre

3

• Renewable energy

• Sustainable and safe nuclear energy

• Energy security, system and markets

• Energy technologies modelling and assessment

• Alternative fuels

• Hydrogen and fuel cells

• Sustainable transport

• Energy efficiency

BACKGROUND

JRC IET Key Activities

4

BACKGROUND

Key activities

• Technology assessment (techno-socio-economics)

• Modelling of technologies, sectors and the energy system

• Reference technology databases

• Monitoring of technological innovation and of various sectors

Themes

• Low-carbon technologies

• Energy intensive industry

• Critical materials

• E-mobility

• Innovation capacities

Energy Technology Policy Outlook Unit

Support the development and assess the impact of energy technology innovation on the transition to a low carbon society, in support of the

energy, transport, industrial and innovation policies of the EU.

Policies

• Energy Technology

• Industry

• Transport

• Research & Innovation

• Eco-Innovation

5

BACKGROUND

• Perform integrated electro-mobility

modelling activities in order to assist the

design and implementation of electro-

mobility/ transport related policy initiatives

at European and regional/urban level

• EMM will perform comprehensive

multidisciplinary model-based analyses of

technological, market, behavioural aspects

and policy options for electro-mobility

6

Electro-Mobility Modelling Project (EMM)

BACKGROUND

7

EMM Suite of Models TRANSPORT ENERGY

EU/N

atio

nal

R

egio

nal

Market Agent

Model

Lead Market Model

Fleet Impact Model

EV Technologies

Simulator

Travel Behaviour Model

Spatial EV Charging Model

JRC-EU-TIMES

Dispatch Model

Smart Grid

Model

TECHNOLOGY ACTIVITIES

Activity Data

Fleet average CO2, Energy Demand, Technology Costs

Energy Mix, Cost

Real travel values (V, park)

Act

ivit

y p

ater

ns

Spatial/Time Energy Needs

EV Measured Data

(e-VELA)

0

0.5

1

1.5

2

2.5

2010 2015 2020 2025

Pro

jec

tio

n o

f P

HE

V/ E

V n

ew

sa

les

in

Eu

rop

e (

M u

nit

s/y

ea

r)

ERTRAC roadmap

ACEA 2010

Communication,

San Sebastian*

Total sum of member

state targets (per IEA 2009)

*assumption: total car market in Europe: ~15 million new sales per year

current sales (21 models)

Sales projections from 2009 SET-Plan workshop on Electrification of Road

Transport

BACKGROUND

Electro-mobility

8

Based on the corrected EEA CO2 monitoring DBs: http://www.eea.europa.eu/data-and-maps/data/co2-cars-emission-7

BACKGROUND

9

EU Electric Vehicle Sales

BACKGROUND

Source GeM: Combination of slow and fast chargers, most cars not privately owned.

10

Electric Vehicle Grid Power Requests

Power requested per charge

Time of the day (h)

P (

in K

W)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 5 10 15 20

Average 1.45 KW

Total power requested

Time of the day (h)

P (

in M

W)

01

02

03

04

0

0 5 10 15 20

Average 20 MW

Electro-mobility as a Flexible Load option

Electric vehicle load shift potential, avoiding peaks during peak time

scenario with 10% electric vehicles in the car fleet

without load shifting with load shifting

Source: JRC Report (2013).ISBN 978-92-79-30388-3

BACKGROUND

11

What do the car drivers know? Familiarity

Low level of familiarity


COUNTRY

BACKGROUND

AGE

12

Low familiarity yet overall perception in line with actual EV features


BACKGROUND

Perception of EV Features

8.6

7.8

7.6

9.0

8.9

9.1

8.9

7.9

8.0

6.3

3.8

13

MARKET AGENT MODEL

System dynamics modelling with market agent approach

14

Background Studies the interaction between, and influence of, the

market agents of the automobile sector on possible

powertrain technology transitions within Europe, for

each of the MSs and across the period 1995 to 2050

ADVANTAGES DISADVANTAGES

• Dynamic

• Can include other models

• Captures unexpected non-linear

feedback between elements

• Many scenarios in short time

• Subscription of parameters

• Simplified version of reality

• Dependent on quality/availability

of input data

• For scenario comparison rather

than prediction

Not physically within model, but form decision rules that CLDs are based on

USERS Evaluate, purchase and dispose

vehicles

AUTHORITIES Conceive, implement and monitor

policy and regulation

INFRASTRUCTURE Invest, build, operate and

decommission infrastructure and

maintenance services

MANUFACTURER Produce, price and market

vehicles, and invest in capacity/R&D

15

Overview

MARKET AGENT MODEL

USERS Evaluate, purchase and dispose

vehicles








Fleet

Sales

+

Forecast sales+

Performancemetrics Emissions

-

-

KEY OUTPUT INDICATORS FOR ALL MARKET AGENTS

15

MARKET AGENT MODEL

Overview

Fleet

Willingness toconsider

+

Sales+

+

Choice

Attractiveness ofPowertrain

+

+

Popularity

Forecast sales+

Performancemetrics

+

Emissions

-

Total Cost ofOwnership -

+

-

+

Convenience +

+

+

+

EnvironmentalImpact

-








KEY USER ELEMENTS

15

MARKET AGENT MODEL

Overview

Fleet


+

Sales+

+

Choice


+

+

Popularity

Forecast sales+

Performancemetrics

+

Emissions

-

Total Cost ofOwnership -

Fuel and recharginginfrastructure

+

Forecast fuel andrecharging infastructure

revenue

+

+

Fuel andrecharging price

+

-

+

Convenience +

++

+

Maintenancenetwork

+

-

+

EnvironmentalImpact

-+





KEY INFRASTRUCTURE PROVIDER ELEMENTS

15

MARKET AGENT MODEL

Overview

Fleet


+

Sales+

+

Choice


+

+

Popularity

Revenue

Marketing

+

+

+

Forecast sales+

ComponentR&D

+

Performancemetrics

+

+

Emissions

-

Forecast emissionpenalties

+

+

Profits

+

Production

Productionexperience

+

Costs-

Price

+

Total Cost ofOwnership

+

-Fuel and recharging

infrastructure

+


revenue

+

+


+

-

+

Forecastprofits

+-

+

+

Convenience +

++

+

-

+

Maintenancenetwork

Componentcost

-

+

+

-

++

EnvironmentalImpact

-

-

+

+

Productioncapacity

+

+

+



KEY AUTOMOBILE MANUFACTURER ELEMENTS

15

MARKET AGENT MODEL

Overview

Fleet


+

Sales+

+

Choice


+

+

Popularity

Revenue

Marketing

+

+

+

Forecast sales+

ComponentR&D

+

Performancemetrics

+

+

Emissions

-


+

+

Profits

+

Production


+

Costs-

Price

+


+

-

Authoritysubsidies

-


+

+


revenue

+

+


+

-

+

Forecastprofits

+-

-

+

+

Convenience +

++

+

-

+

Maintenancenetwork

+

Componentcost

-

+

+

Fleet EmissionPenalties

+-

++

EnvironmentalImpact

-

-

+

-

+

Productioncapacity

+

+

+

KEY AUTHORITY ELEMENTS

15

MARKET AGENT MODEL

Overview

Fleet


+

Sales+

+

Choice


+

+

Popularity

Revenue

Marketing

+

+

+

Forecast sales+

ComponentR&D

+

Performancemetrics

+

+

Emissions

-


+

+

Profits

+

Production


+

Costs-

Price

+


+

-

Authoritysubsidies

-


+

+


revenue

+

+


+

-

+

Forecastprofits

+-

-

+

+

Convenience +

++

+

-

+

Maintenancenetwork

+

Componentcost

-

+

+

Fleet EmissionPenalties

+-

++

EnvironmentalImpact

-

-

+

-

+

Productioncapacity

+

+

+

1549 ELEMENTS WITH UP TO 10,000 SUBSCRIPTS

2972 DATA INPUTS

707,720 ELEMENTS OVERALL AT EACH TIME STEP

=

+

63,668 CONSTANT VALUES

641,079 SIMULTANEOUS EQUATIONS

+

Including….

15

48 VENSIM VIEWS

+

MARKET AGENT MODEL

Overview

16

Name Elements

Powertrain

Petrol ICEV; Diesel ICEV;

LPG ICEV; CNG ICEV; Biodiesel ICEV; Bioethanol ICEV;

Petrol HEV; Diesel HEV; Biodiesel HEV; Bioethanol HEV;

Petrol PHEV; Diesel PHEV; Biodiesel PHEV; Bioethanol PHEV;

BEV;

FCV

Vehicle Class Passenger, Light Commercial

Vehicle Size Small, Medium, Large

Vehicle Age Under 2 Years; 2 to 5 Years; 5 to 10 Years; Over 10 Years

Country Each member state

Powertrain Class BEV; FCV; HEV; ICEV

Fuel Biodiesel; Bioethanol; CNG; Diesel; Electric; Hydrogen; LPG; Petrol

Users Private, Fleet, Public

Geography Urban, Non-Urban

Utility Criteria Environment; Performance; Reliability; Safety;

Convenience; Popularity; Choice; Cost

Component Electric drive system; BEV battery; HEV battery; PHEV battery; IC engine;

Hydrogen storage tank; Body materials; Fuel cell system

Primary Energy

Source Renewables, Oil; Gas; Solids; Nuclear

MARKET AGENT MODEL

Subscripts

1. Qualitative representation of the market mechanisms leading to new technology market penetration.

Literature Review

Expert Discussions

Develop Causal Loop

Diagrams

Market Agent Behaviour

MARKET AGENT MODEL

Development

17


2. Development of a quantitative simulation model from the established causal loop diagrams.

Literature Review

Expert Discussions

Develop Causal Loop

Diagrams


Stocks and Flows

Subscripts Underlying Equations

Data Collection

Assumptions and

compromises

Boundaries

MARKET AGENT MODEL

Development

18

19


2. Development of a quantitative simulation model from the established causal loop diagrams.

3. Establishment of a baseline scenario and conduct of scenario analyses.

Literature Review

Expert Discussions

Develop Causal Loop

Diagrams


Stocks and Flows

Subscripts Underlying Equations

Data Collection

Assumptions and

compromises

Reality Checks

Calibration Sensitivity

Testing

Boundaries

MARKET AGENT MODEL

Development

20


Reality Check


Testing

MARKET AGENT MODEL

Development

Basic checks that the model is performing

logically

"For a model to be reasonable when I ___________ it should _______"

Name Test Input Description Outcome

No population no sales

No population If population is zero, then there should be zero vehicle demand

Initially failed as scrappage schemes did not account for population size. Minor technical

adjustment to ensure no sales if no population

No willingness to buy no sales

No willingness to buy

If willingness to buy a powertrain is zero, then there should be no sales

Initially failed as willingness to buy was influencing indicated market share which is then

smoothed to provide an actual market share. This is an error as willingness to purchase should

be an immediate impact. This was rectified by including willingness to buy after the smoothing

function

27


Reality Checks


Testing

Experiments to determine

coefficients that relate variables to historical patterns.

MARKET AGENT MODEL

Development

1. Need historical data and relationship to parameter to be determined 2. Historical time series data sets are inputs in a separate model to

establish a Vensim data set to use as a "payoff definition" 3. These data sets are used in Vensim optimisation routines to

determine unknown values of parameters within the main model by running model many times at different (bounded) values until a best fit is achieved

eg Vehicle Registrations

21

Population

GDP per capita

coeff 1

coeff 2

Initial GDP per

capita

Average

Household Size

coeff 3

Number of

Households

Initial Historic

passenger demand

initial passenger

demand

GDP Ratio

indicated calibrated

passenger new

registrations

initial

households

household ratio

Passenger Vehicle Registrations

4 M

3 M

2 M

1 M

0

22

2 2 2

2 2 2

2 2 2

22

21 1 11 1 1 1 1 1

11

1 1 1 1

1996 1998 2000 2002 2004 2006 2008 2010 2012

Time (Year)

Veh

icle

/Yea

r

calibrated passenger new registrations[United Kingdom] : Baseline to 2012 1 1 1 1 1 1 1Eurostat 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

MARKET AGENT MODEL

Calibration

New Registrations = Initial Demand x coeff 1 x GDP Ratiocoeff 2 x Household Ratiocoeff 3

Coeff 1 = 0.9344 Coeff 2 = 0.5083 Coeff 3 = 0.01

22


Reality Checks


Testing

To understand the impact and

importance of input assumptions

1. Uncertainty of influence of certain parameters 2. Especially important if chosen data input is weak 3. 146 constant inputs were identified to analyse impact of key

outputs (Energy, emissions, EV uptake) – using optimisation routine as in calibration

4. The top 20 of these were included in multivariate sensitivity analyses

eg Emissions

MARKET AGENT MODEL

Development

23

sensitivity

Petroleum Persistence

Policy Push

50% 75% 95% 100%

EU27 average CO2 emissions per km travelled[Tank to wheel]

200

150

100

50

01996 2010 2024 2037 2051

Time (Year)

• Baseline values for chosen constant inputs • Varies between a chosen range • Number of simulations usually 200 • Uses Monte Carlo method • Takes up to a day to run analysis

Parameter Baseline Range

Annual change in passenger importance of criterion

[Environment,Private,Urban]

0% -5% to 5% per annum

Initial power train utility [Petrol HEV, Convenience]

0.95 0.85 to 1

Initial base unit production cost [Petrol ICEV, Small]

€8000 €6000 to €10000

Max economies of scale effect on costs

20% 10% to 30%

Reference maximum price differential for WtC

50% 40% to 60%

MARKET AGENT MODEL

Sensitivity Testing

Development of baseline scenarios based on market conditions (oil

price, GDP growth rate, R&D learning rate) and government policies

(EV purchase subsidies, fleet emission regulation targets)

Policies have greater influence on EV success than market

conditions

25

MARKET AGENT MODEL

Baselines

Scenarios of exploring interaction between fleet emission regulations

targets, EV purchase subsidies and infrastructure provision subsidies

Policies aimed at users can make significant short term impacts

(depending on timing and length)

Policies aimed at manufacturers (fleet emission regulation) have

greater influence on EV market share over the long term

Infrastructure subsidies appeared to have an impact only when

complemented with other policies (subsidies; fleet emission

regulation)

Important interactions between EV types were identified

26

MARKET AGENT MODEL

Policy Analysis

27

2030

BASELINE: CURRENT EMISSION TARGET, NO SUBSIDIES

MARKET AGENT MODEL

Results

2030

SUBSIDIES 1: CURRENT EMISSION TARGET, LOW SUBSIDIES

83%

308%

27

MARKET AGENT MODEL

Results

2030 2030

EXTENDED CO2 1: PROPOSED EMISSION TARGET, LOW SUBSIDIES

6%

NEGLIGIBLE

27

MARKET AGENT MODEL

Results

2030

CURRENT AND EXTENDED CO2 2: HIGH PURCHASE SUBSIDIES

58%

613%

27

MARKET AGENT MODEL

Results

2030

CURRENT AND EXTENDED CO2 3: HIGH INFRASTRUCTURE SUBSIDIES

683%

NEGLIGIBLE

27

MARKET AGENT MODEL

Results

2030

CURRENT AND EXTENDED CO2 4: HIGH SUBSIDIES

11,153%

27

MARKET AGENT MODEL

Results

Sensitivities of EV uptake to the desired ratio of EV to charging point

Reducing ratio below 10EV/CP significantly increases costs for

limited additional success

Sales market share is relatively independent of infrastructure

provision until stock share is over 5%

28

MARKET AGENT MODEL

Infrastructure

• Ongoing model development: • Data • Structure • Scenarios • User preferences

• Development of analysis regarding

individual countries • Integration with other EMM models –

especially Dione (Fleet Impact Model)

29

MARKET AGENT MODEL

Current Research

• Tool for vehicle stock projection and scenario evaluation

• Contains up-to-date vehicle stock datasets and energy and emission

factors of all vehicle types in each EU MS and EU28

• Calibrated baseline

• Fuel consumption and emission calculations

• Can be used for Defining and Running scenarios

• Output from PTTMAM can be input to Dione

30

DIONE (Fleet Impact)

Overview

31

INPUTS OUTPUTS

• Vehicle stock • New registrations • Survival rates • Activity • Efficiency • Allocation of mileage to different

fuels for flex-fuel vehicles • Fuel pathways for energy

consumption and emissions • Biofuel admixture shares for

conventional fuels • Driving patterns • Custom Vehicles … for single or groups of EU Member States or custom regions

• Vehicle Stock and Scrappage • Activity • Energy Consumption (by Fuel) • GHG and Air Pollutant Emissions

Display Options: As time series or for specific years, for single vehicle types or aggregate, graphics or data, for baseline and/or one or several user defined scenarios,…


Overview

• German 2050 PC fleet composition ("Electric Vehicle dominated” Scenario*)

5% ICE 35% PHEV 35% BEV 25% FCEV

• Linear development of stock towards 2050 target

• Roughly keeping Baseline 2050 overall PC fleet size (ca. 37m) and LPG/CNG

vehicles (ca. 1m)

*Source: ‘A portfolio of power-trains for Europe’ (McKinsey 2010)

ENERGY CO2 EMISSIONS


Example

32

• Urban planning spatial analysis approach

• Use of GIS software tools (ArcGIS, QGIS, GRASS…)

• The required input data may vary, depending on the purpose, needs and the examined area but may include:

• Road network grid

• Parking areas and lots

• Residential and labor statistics

• Traffic density

• Electric power distribution network

• Existing charging points

• Service areas (gas stations, restaurants etc)

• Public transport stations

• Landscape morphology

33

GEOSPATIAL ALLOCATION

Overview

SUMMARY

34

Modelling is an important research tool in gathering scientific support for policy

There are many types of electro-mobility modelling activities being carried out in the JRC to help inform policy decision making in the European Commission

We welcome opportunities for networking or collaboration and hosting visiting scientists

THANK YOU

The Commission's Strategic Energy Technologies Information System:

http://setis.ec.europa.eu

[email protected] [email protected]

electro-mobility modelling

Environment

energy demand

renewable energy sustainable

technology costs energy

innovation policies

transport university

total car market

new sales

santi jrc institute