MOBILE ENERGY RESOURCES IN GRIDS OF · Ed Bower UK RT 5c/4a MERGE: Mobile Energy Resou rces in...

www.ev-merge.eu

MOBILE ENERGY RESOURCES IN GRIDS OF

ELECTRICITY

ACRONYM: MERGE

GRANT AGREEMENT: 241399

DELIVERABLE

DISSEMINATION

22 FEBRUARY 2012

www.ev-merge.eu

REVISION HISTORY

VER. DATE NOTES (including revision author)

01 20/02/2012 First draft completed

02 22/02/2012 Final version approved

03

04

AUTHORS

Nikos Hatziargyriou [email protected]

João Peças Lopes [email protected]

mailto:[email protected]

SECOND MERGE WORKSHOP

The second Workshop was organized during “CIRED 2011: The 21st International Conference and Exhibition on Electricity Distribution” in

Frankfurt on 6 – 9 June 2011.

The round table called “RT.4a/5c: Integration of Plug-in-Vehicles in Distribution networks. Contributions from 2 major EU FP7 projects:

MERGE and G4V (Grid for Vehicles), was convened by Prof. Nikos Hatziargyriou.

The site of this CIRED 2011 is http://www.cired2011.org.

The presentations from the MERGE and G4V projects are attached to this document.

www.ev-merge.eu

http://www.cired2011.org/

CIRED 2011 Secretariat c/o AIM – 31, Rue Saint-Gilles, 4000 Liège, Belgium

Tel : +32 4 222 29 46 – Fax : +32 4 222 23 88 – [email protected] – www.cired2011.org

Conveners/Contributors Biographies: RT5c/4a - INTEGRATION OF PLUG-IN-VEHICLES IN DISTRIBUTION

NETWORKS. CONTRIBUTIONS FROM 2 MAJOR EU FP7 PROJECTS: MERGE AND G4V (GRID FOR VEHICLES)

June 8, 14.00 hrs - 15.30 hrs

Convener

Nikos Hatziargyriou

Nikos Hatziargyriou's biography: Professor Nikos Hatziargyriou is deputy CEO of PPC,

responsible for Transmission and Distribution Networks, island DNO and the Center of

Testing, Research and Prototyping. From 1984 he is with the Power Division of the School

of Electrical Engineering of NTUA, where he is full professor, since 1995. He is chair of

SCC6 of CIGRE, Fellow Member of IEEE, Chair of the Power System Dynamic

Performance Committee, member of the BoD of EURELECTRIC and was member of the

EU Advisory Council of the Technology Platform on SmartGrids. He is coordinator of the

EU MERGE project, he has participated in more than 60 R&DD Projects, he is author of

more than 250 scientific publications.

Scope

Electric and plug-in hybrid vehicles (EV, PHEV) have the potential to contribute

significantly to solving future environmental and economic challenges of future mobility.

A mass introduction of EV and PHEV will imply both, challenges and opportunities not

only for the automotive industry but to a large extent also to the energy sector. A

standardised infrastructure solution is needed to facilitate the mass roll out of EV and

favour customer acceptance. There are two EU FP7 projects (MERGE and G4V) that are

currently investigating the impact of a large-scale introduction of EVs, PHEVs on the EU

electricity infrastructure while making use of the inherent opportunities that smart grid

technologies offer. This Round Table will discuss and contrast the work conducted in the

projects covering approaches and methodologies use needed to understand the effects of

a mass introduction of electric mobility within an EU context.

Contributors

Thomas Theisen

Thomas Theisen's biography: Actually Mr. Theisen works with the RWE Deutschland AG

since January 2011 as an executive R&D manager. With his team he is responsible for

all innovation projects dealing with distribution of electricity, gas and water. Thomas

Theisen had been in charge of research and development at RWE Energy since 2004.

There he managed R&D projects related to transportation and the distribution and use

of electricity, gas and water. From 1999 to 2003, Mr. Theisen chaired the Marketing &

Communications Committee of the European Natural Gas Vehicle Association. He is

leading the Eurelectric Task Force on Electric Vehicles since 2008. Mr. Theisen studied

CIRED 2011 Secretariat c/o AIM – 31, Rue Saint-Gilles, 4000 Liège, Belgium

Tel : +32 4 222 29 46 – Fax : +32 4 222 23 88 – [email protected] – www.cired2011.org

mechanical engineering, with a specialization in chemical engineering, at the University

of Essen.

J.P. Lopez

João A. Peças Lopes' biography: João A. Peças Lopes is Full Professor at the Faculty of

Engineering of Porto University (FEUP). He is presently Director of INESC Porto. He is

also the Director of the Sustainable Energy Systems PhD program at FEUP. He is

Scientific Coordinator of the EU FP7 research project MERGE – Mobile Energy Resources

for Grids of Electricity. He is the convener of the CIGRE WG C6.20 on Integration of

electric vehicles in electric power systems. His main domains of research are presently

related with large scale integration of renewable power sources, power system

dynamics, microgrids, smart metering and electric vehicle grid integration.

Paolo Scuro

Paolo Scuro's biography: Paolo Scuro is a Business Developer and Project Manager in

the Network Technology Department at Enel. Prior to joining Enel, Scuro was a project

manager and system engineer for several ICT projects in the space industry Additionally

he worked for the European Space Agency in a one-year program as an Assistant

Project Manager. Scuro who is an expert in technology management and business

analysis is now mainly focused on developing solutions for Enel’s smart grids and

electric mobility projects. In 2009, Scuro completed the MBA Program at the Luiss

Business School in Rome.

Ed Bower

Ed Bower's biography: Ed Bower has 11 years of experience at Ricardo UK, based at the

Shoreham Technical Centre near Brighton. Ed works in the in the Technology,

Innovation and Strategy department, which is primarily responsible for Ricardo's

advanced technology projects. Ricardo delivers world class strategy, engineering and

technology programmes to the global automotive, transportation, defense and energy

industries. Ed is a Chartered Engineer with the Institution of Mechanical Engineers

(IMechE) and has a 1st class Masters degree in Mechanical Engineering from the

University of Warwick. Ed will graduate with a Master of Science degree in Automotive

Technology Management from Cranfield University in June 2011.

Frankfurt (Germany), 6-9 June 2011

MERGE:‘Mobile Energy Resources in Grids of Electricity’

An assessment of traffic patterns and consumer beha viour and the impact of plug-in electric vehicle charging requirements o n European electricity

networks

Ed Bower

Ricardo UK

8th June 2011

CIRED

Frankfurt

Ed Bower – UK – RT 5c/4a – MERGE: ‘Mobile Energy Resources in Grids of Electricity’

Agenda

� Introduction

� Questionnaire

� Load profile change model

� Summary of key findings


Introduction

� This brief presentation summarises some of the key findings of a report published by the EC FP7 MERGE project consortium � Report available on MERGE

website:� www.ev-merge.eu

� The report examines the traffic patterns and human behaviours of drivers from across Europe

� To provide a benchmark of current vehicle usage patterns

� To assess the impact of future plug-in electric vehicle charging requirements on European electricity networks


16 partners from 8 countries form the consortium. T he studies primarily focus on the partners’ home countries.

� EON, MIT and Renault are also involved as part of the project advisory committees

Participant organisation name Short name Country Organ isation

1 Power Public Corporation PPC Greece Utility

2 INESC Porto INESC Porto Portugal R&D inst.

3 Cardiff University Cardiff UK University

4 Technische Universität Berlin TU Berlin Germany University

5 Institute Computers Communications Systems of National Technical University Athens

ICCS/NTUA Greece University

6 Universidad Pontifícia Comillas - Madrid

Comillas Spain University

7 Rede Eléctrica Nacional REN Portugal TSO

8 Red Eléctrica de España (REE) REE Spain TSO

9 Iberdrola Iberdrola Spain Utility

10 European Association for Battery Hybrid and Fuel Cell Electric Road Vehicles

AVERE Belgium Non-profit association

11 Ricardo Ricardo UK Vehicle consultant

12 Interactive Media in Retail IMR World UK SME Consultant

13 Regulatory Authority for Energy RAE Greece Regulatory Entity

14 Consulting4Drive C4D Germany Vehicle consultant

15 Electricity Supply Board ESB Ireland Utility

16 InSpire Invest InSpire Norway SME Consultant

Project Partners


Agenda

� Introduction

� Questionnaire




A survey was developed in eight languages and provi ded 1,621 responses, primarily from six countries

� 1,621 responses were received from a cross-section of the European population

� Six countries were focussed on in particular: Germany, Great Britain, Spain, Greece, Portugal and Ireland

� The questionnaire responses provided:� Direct statistics (e.g. proportion of

responders that would participate in multiple-tariff electricity rates)

� Datasets that were inputs to the load profile change model (e.g. profiles of times people return from last journey of the day)

� Datasets that can be used in subsequent tasks (e.g. profiles of times people depart for first journey of the day)

Deutsch English

Français

ελληνικά

Nederlands

Español

Norsk

Português

Questionnaire in 8 languages


There was a spread of ages, sexes, occupations and locations such that no single group dominated the responses


Although the regularity with which users will charg e EVs is not certain, there is consensus that home recharging is preferred

When do you (would you) refuel/recharge?

� The majority of conventional vehicle owners refuel only when the fuel tank is nearly empty

� There was no consensus on when people would recharge an electric vehicle if they owned one

Where do you (would you) refuel/recharge?

� A strong consensus emerged that an EV owner’s home is the preferred location for recharging� 70% at home� 20% at work� 10% elsewhere


Most responders would take advantage of multi-tarif f electricity rates both at night and during the day

Key findings relating to tariffs

� Over 90% of future EV owners would recharge vehicles at night to take advantage of multiple-tariff electricity rates

� Over 80% of future EV owners would recharge vehicles at certain times of day if it was cheaper (other than overnight) if more complex multiple-tariff electricity rates were to incentivise it

� Over 75% of future EV owners would still try to reduce their charging costs even though electricity is cheap (currently) compared to conventional fuels

� Over 60% of future EV owners would still try to reduce their charging costs even if charging costs are paid by their employers


Agenda

� Introduction

� Questionnaire




A high level overview used sensible assumptions to assess the impact of 10% integration of EV on the grids of six countries

Ricardo analysis based on existing charger technology

90%Charger efficiency

Ricardo analysis based on UK Department for Transport statistics and backed up by questionnaire results

40kmAverage distance travelled between charges

Ricardo analysis based on V-SIM (vehicle simulation software) analysis

0.16 kWh/km Vehicle energy requirement

Standard domestic electricity supply, 230 V, 13 A, single phase

3 kWCharger power

Assumption based on a possible scenario driven by range anxiety and ease of access to charging at home

1 charge per dayRegularity of recharging

Assumption based on possible medium-term EV penetration

10%Proportion of EV in vehicle fleet

SOURCEVALUEDESCRIPTION


Ricardo developed a load profile change model to de termine the power required at each time step to charge elec tric vehicles


Dumb charging shows similar trends in each of the s ix European countries studied – a 10% penetration of EV corresponds to a ~10% increase in peak electricity demand…


... whereas Smart charging can prevent an increase in peak total load, whilst also reducing the peak-to-trough variation


Agenda

� Introduction

� Questionnaire




The MERGE project aims to evaluate the impacts that electric vehicles will have on EU electric power systems

� Current vehicle usage and consumer perceptions of EV recharging options in Europe have been examined� A strong consensus emerged from the questionnaire that an EV owner’s home is

the preferred location for recharging

� It also revealed that despite the relatively low cost of re-charging, users were still highly motivated to make use of any low cost tariffs that may be available during off-peak demand

� Basic models of the energy requirements of EVs have shown the effect of dumb and smart charging scenarios in six European countries� Whilst there are some national variations, a 10% penetration of plug-in vehicles

corresponds to a 10% increase in peak electricity demand using a “dumb” re-charging scenario (where users plug-in their vehicles returning home in the early evening)

� Smart charging can prevent an increase in peak total load, whilst also reducing the peak-to-trough variation

� The full results of these studies is being used to provide robust data and models for later and more in-depth analysis by the MERGE project partners


Where should I go for more information?

� The MERGE project is past its half-way point with many reports online already and many more to come later in the year…� www.ev-merge.eu


Power System Impacts from Large Scale Deployment of EV ‐The MERGE project –

João A. Peças Lopes([email protected])

iliana

Stamp

iliana

Stamp


2

How to evaluate grid impacts from the presence of EV?

Steps to be performed:

1. Need to understand the behavior of EV drivers Surveys

2. Impacts in Power System operation and planning:

• Steady state operation

• Dynamic behaviour

• Generation reserve needs

3. Definition of management solutions with implications on battery charging

modes

4. Design of charging infrastructures

Specific simulation tools capableto incorporate EV drivers behavioursand new management and controlconcepts.


EFFECT OF DUMB CHARGING SCENARIO ON PORTUGAL’S ELECTRICITY DEMAND AND DISTRIBUTION SYSTEMS (10% EV)

3

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

00:00 04:00 08:00 12:00 16:00 20:00

Grid

dem

and

(MW

)

Baseline demand EV charging load

CONTROLLED CHARGING IS REQUIRED


33%

23% 20%24%

0%

5%

10%

15%

20%

25%

30%

35%

EV charge at the end of the day

EV charge only when it needs

EV charge whenever possible

EV charge whenever is convenient and the

driver has time

%

Charging approaches:

Drivers´ behaviours:

Charging Modes

Uncontrolled

Dumb Charging (DC)

Multiple Prices Tariff (MPT)

Controlled

Smart Charging (SC)

Vehicle-to-Grid (V2G)

Dumb Charging ‐ EV owners are free to charge their vehicles whenever they want; electricity price is constant along the day.

Multiple Prices Tariff ‐ EV owners are free to charge their vehicles whenever they want; electricity price is not constant along the day.

Smart Charging ‐ envisions an active management system, where there are two hierarchical control structures, one headed by an Aggregator and other by the DSO, that control EV charging according to Aggregator’s market negotiations or according to the grid’s needs.

EV electricity

cost

Behaviours defined according to the findings of a survey made within the framework of the MERGE project

Conceptual Framework for EV Integration Into Electric Power SystemsPossible EV charging approaches and drivers’ behaviours

4


The MERGE control concept

• A two level hierarchical control approach needs to be adopted:

• Local control housed at the EV grid interface, responding locally to grid frequency changes and voltage drops;

• Upper control level designed to deal with:• “short-term programmed” charging to deal with branch

congestion, voltage drops – performed at the aggregation level and involving a dialog with the DSO

• Delivery of reserves (secondary frequency control);• Adjustments in charging according to the availability of power

resources (renewable sources).

5

Adoption of an AC charging mode 3, using the control pilotconnection


Conceptual Framework For EV Integration

• EV must be an active element within the power system

• The Upper Level control requires interactions with:

• An Aggregatingentity to allow:

Controlled charging Reserve

management Market negotiation

DSO

Ele

ctric

ity M

arke

t O

pera

tors

6


EV Voltage / Frequency support modes

Voltage Control

Frequency Control

Local Control

Primary Control (local control)

Secondary Control

Coordinated Control

7

Active rectifiers or shouldbe adopted to interface EV with the grid.

Communicationinfrastructure

Local control to be implemeted ina similar way to SAE 1772


Data and assumptions: Peak load – 25.1 kW Annual energy consumption – 111 MWh Vehicles in the network – 30 30% of the fleet of vehicles are electric Period of lower energy price for Multiple Tariff Policy adherents – 23h to 6h

Single line diagram of the LV test network

Results: Power demand:

0,000

0,005

0,010

0,015

0,020

0,025

0,030

0,035

0,040

0,045

0,050

Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Power dem

and (M

W)

Without EV Dumb chargingDual tariff policy Smart charging

Evaluation of EV Impacts in Distribution Networks – 1 Case study: typical Portuguese LV grid(residential area)

Impossible approaches

8


0,60

0,65

0,70

0,75

0,80

0,85

0,90

0,95

1,00

1,05

1,10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Voltage (p.u.)

Bus

Without EV Dumb chargingDual tariff policy Smart chargingEN 50160 voltage lower limit

Voltage profiles for the peak hour:

Results: Branches’ loading for the peak hour:

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

Without EV Dumb charging Dual tariff policy Smart charging

Wee

kly en

ergy losses (MWh)

Weekly losses:

No EV DumbCharging

Dual TariffPolicy

SmartCharging

Evaluation of EV Impacts in Distribution Networks – 2 Case study: typical Portuguese LV grid(residential area)

9


Evaluation of EV Impacts in Distribution NetworksCase study: typical Portuguese MV grid

Data and assumptions: Peak load – 7.3 MW Annual energy consumption – 32 GWh Power factor for the conventional load – 0.96 Vehicles in the network – 7035 30% of the fleet of vehicles are electric Period of lower energy price for Multiple Tariff Policy adherents – 23h to 6h


Evaluation of EV Impacts in Distribution Networks – Case study: typical Portuguese MV grid

0

2

4

6

8

10

12

14

1 49 97 145 193 241 289

Total N

etwork Load

(MW)

t (1/2 h)

No EV Dumb Charging Multiple Tariff Policy Aggregator's Smart Charging

0.7

0.75

0.8

0.85

0.9

0.95

1

1.05

1.1

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241 251 261 271 281 291 301

Voltages (p

.u.)

Bus

No EV Dumb Charging Multiple Tariff Policy Aggregator's Smart Charging

Load increases after first and final day travels

Valley hour filling


12

Results: Branches’ loading for the peak hour:

No EV DumbCharging

Dual TariffPolicy

SmartCharging

Evaluation of EV Impacts in Distribution Networks – Case study: typical Portuguese MV grid


Droop Control for EVP

f0.2 Hz

0.8 C

0.2 C

1 C

B

-KI/s

fp1

fpm

fpA1

fpAk

fi

Pif1

PifnPifREF

fi

++ -

+

+-

+

+ +-

k

i

m

i 1

inii

1

inii PaPe

ini1Pe

inimPe

ini1Pa

inikPa

Pref1

Prefm

Prefa1

Prefak

fREF

++

-+

+-

++

ACE Aggregators

AGC Model with EV

Control loop for EVs active power set-point

PRIM

ARY

FREQ

UEN

CY C

ON

TRO

L

SECO

ND

ARY

FREQ

UEN

CY C

ON

TRO

L

Reserve Provision with EVLocal Droop Control and Automatic Generation Control (AGC)

13


C15H

C16TC2

H

C17N

W10

W11W2 W1

W4 W5

W7

400 kV150 kV

400 kV

220 kV

400 kV

150 kV

400 kV220 kV

400

kV22

0 kV

~

C1H

C3H

~ ~C4TG

W3

~

C7H~ W6

~C6H~C5

H

~

C8H

~

C9TG

W8

~

C12TC

~

C11TC

~

C14H

~ ~ ~

150 kV220 kV

~C10H

W9

~

C13TC

1

400

kV22

0 kV

211 10

1213

14 1516

17

18

19

22

21

23

8

7

2524 9

4 3

6 5

20

Control Area 1 Control Area 2

Equivalent Generator Types

~

~

C(TG): Conventional Gas

~

C(H): Conventional Hydro

~

C(TC):Conventional Fuel or Coal

N: Conventional Nuclear W: Wind

Simplified Portuguese Transmission Network

Droop Control for EV

P

f0.2 Hz

0.8 C

0.2 C

1 C

Control loop for EVs active power set-point

PRIM

ARY

FREQ

UEN

CY C

ON

TRO

L

SECO

ND

ARY

FREQ

UEN

CY C

ON

TRO

L

Reserve Provision with EV - Local Droop Control and Automatic Generation Control (AGC)

14


0 100 200 300 400 500 600 700 800 900 10000

0.1

0.2

0.3

0.4

0.5

0.6

Time (s)

P Win

d (MW

)

0 100 200 300 400 500 600 700 800 900 10000.9

0.95

1

1.05

Time (s)

P EV (M

W)

EV - Charge ModeEV - Freq. Control

0 100 200 300 400 500 600 700 800 900 100049.4

49.6

49.8

50

50.2

50.4

50.6

Time (s)

Freq

uenc

y (H

z)


0 100 200 300 400 500 600 700 800 900 10000.3

0.35

0.4

0.45

0.5

0.55

Time (s)

P Hyd

ro 4

(Hz)


Reserve Primary Reserve - Frequency Control with EV

15


0 100 200 300 400 500 600 700 800 900-3500

-3000

-2500

-2000

-1500

-1000

-500

0

500

1000

Time (s)

P inte

rcon

nect

ion

(MW

)

With participation of EVWithout participation of EV

-10 -5 0 5 10 15 20 25 30

49.7

49.8

49.9

50

50.1

50.2

Time (s)

Freq

uenc

y (H

z)

With participation of EVWithout participation EV

0 20 40 60 80 100 120

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

Time (s)

I 16-1

8 (p.u

.)


0 100 200 300 400 500 600 700 800 900-3000

-2000

-1000

0

1000

2000

3000

Time (s)

AC

E (M

W)


Secondary Reserve - AGC Operation with EV

16


CONCLUSIONS

• Electric Vehicles will play an important role in the development of the Smart Grid concepts since they are:• High flexible load device• Mobile storage device

• The presence of Electric Vehicles, if properly managed, can:• provide several ancillary services; • allow a larger integration of renewable power sources;• increase system robustness of operation.

• If Smart Charging and other Distributed Intelligent solutions are adopted, the need to reinforce the existing electrical grid and generation infrastructures can be postponed.

17

http://www.ev-merge.eu/

G4V- Grid for vehicles

Thomas Theisen RWE

Analysis of the impact and possibilities of a mass introduction of electric

and plug-in hybrid vehicles on the electricity networks in Europe


Paolo Scuro Enel

Round Table RT5c/4a Integration of plug-in-vehicles in distribution networks.

Contribution from 2 major EU FP7 projects: MERGE and G4V

Agenda

Overview: the G4V project

EVs: Background

Business/Economic, Environmental and Societal Implications for Electro-Mobility

Customer perspective: Results of the European G4V survey

Economic and Environmental impacts of Electro-Mobility

Implications for business models of the key stakeholders

Impact of electromobility on electricity networks

THEISEN – DE – RT 5c/4a – G4V

technical issues

legal framework

business model

customer convenience

environmental aspects

Recommendations

Key – Question: What needs to be started now in order to enable a mass market of EV?

time-horizon: 2030 Project duration: Jan 2010 – June 2011


Scenarios

WP1

Business models

WP 2

ICT

WP4

Grid

Infra-

structure

WP5

Power

system

operation

WP6



EVs: Value for the entire system






Agenda


…technically and economically:

Provide flexibility to the system:

Buffer the variability of intermittent generation coming from renewable energy sources

Tool for managing congestion in the power networks

Demand response services

Load-shaping services

Trading flexibility:

Due to their small scale, the EVs need to be operated as an ensemble

Niche for a new function: Aggregation (that can be taken by an existing or a new actor)

Trade the services that they can provide in the most appropriate markets

… and also ecologically ....

The alterable characteristics of the EVs makes them good candidates to impact the system…









Agenda


Preference of home recharging (70 % )

Interest in delayed charging (with price incentives)

average of 5,8 on a scale from 1-7

Most interested UK: 6,1

Less enthusiastic Spain: 5,6

Main reason not to be interested: being afraid not having the possibility to use their car

V2G: Less interest compared to delayed charging

average of 4,4 on a scale from 1-7

Most interested UK and Portugal

Main reason not to be interested :

benefit too low (50%)

Survey results (1,900 responses were received from 8 countries) :

Preferences of potential users of EVs related to charging – A survey in 8 countries









Agenda


... even at low levels of EV penetration

0

1

2

3

4

5

6

7

8

9

10

0 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

WIn

d Sh

eddi

ng (

% o

f ava

ilabl

e)

EV Penetration (% of total)

NonOpt OptUni OptV2G

Example: UK, 30% wind penetration in the system

Significant avoidance of wind energy curtailment by optimized EV charging


-20

0

20

40

60

80

100

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%CO

2 E

mis

sio

ns

incr

eas

e/d

ecr

eas

e (

%)



Drop in CO2 emissions in optimized EV charging cases is due to greater absorption of wind energy substituting fossil fuel based production

CO2 emission Increase/Decrease


Example: UK, 30% wind penetration in the system

The increase of CO2 emissions is due to the higher demand of energy. The reduction of pollution because of the replacement of ICE cars by EVs is not considered.

-5

0

5

10

15

20

25

30

35

40

0 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Pro

du

ctio

n c

ost

s in

cre

ase

, %

(ab

ove

0%

EV p

en

etr

atio

n le

vel)



Avoidance of wind energy curtailed

Reduced usage of expensive generators

Reduced provision of response by conventional generators

Reduced emission costs

Key reasons for cost savings in Optimized EV charging:

Impact on production costs: Optimized charging leads to reduced overall operational costs









Agenda


A player that manages the EVs as flexible demands is required


Gather the time-varying EVs’ flexibilities build flexibility

services

Price the flexibility services:

Assets usage and wear, e.g. battery lifetime

Infrastructure usage costs

Search to maximise the value of the bundled EVs’ flexibility

services

Awareness of system/market status as well as EVs

availabilities

Make the traded quantities in the market mechanisms

available and deployable to the system at the agreed times

and volumes


Wide system

demand

EVs

schedulingRES

production

Generation

scheduling

Market/ system conditions

pd pc

• Generation scheduling Minimise total operating costs

Power balance constraint (including EVs’ charge and discharge)

Security: system reserve requirements Units technical operating constraints: - Minimum up- and down-times - Up and down ramp rate limits

• EVs scheduling “Maximise revenues”

EVs’ energy requirements EVs operating constraints:

- State of charge - Charging and discharging rates - EV’s status

System operation


The coordination of the aggregator and markets is a large and complex optimisation problem

Aggregators will sell their services only if their price is competitive (the total system welfare increases)

The V2G services are acquired for: Energy arbitrage

Generation schedule changes

Systems with “flat” supply curves would hardly acquire V2G services for energy arbitrage

When the EVs penetration is large enough to flatten the total system demand, there are no opportunities to provide V2G services

Conclusions of the aggregator model









Agenda

SCURO – IT – RT 5c/4a – G4V

Stochastic approach

Inputs: Collection of 200 real grid data (MV & LV)

Driving patterns from mobility study

8 different EVs control strategies

Outputs: Overloads in lines and sub-stations

Required reinforcement investment

Technical parameters (security margins, energy and power in violation)


Tool to evaluate EV impact on Distribution Grids

Uncontrolled

Tariff Control


Control strategies: Conservative Scenario

Examples

Load by EVs is reduced, if secondary

substation is at capacity

Pragmatic solutions envisage

an active role of the DSO

Charging process integrate into

smart grid solutions.

Those solutions are achievable

within today technology and

regulatory conditions

Possible to host higher

percentage of EVs


Control strategies: Pragmatic Scenario

More advanced solutions, for

example using the Aggregator

or multiple agents, can provide

additional benefits for the

electrical system such as

higher integration of

renewables

To introduce them additional

researches are needed; for

example to integrate them in

grid congestion management


Control strategies: Advanced Scenario

Aggregator Model

Multiple Agents

(Powermatcher )

Using control strategies is possible to reduce and postpone grid reinforcement

To apply some of the control strategies it is required to implement smart grid functionalities

Moreover, there is the opportunity to use EVs to offer services to the electric system (e.g. integration of renewable sources)


Simulation results

Presentation of the Final Results

of the G4V Project

30 June 2011

BRUSSELS

Further information & registration:

www.g4v.eu

MOBILE ENERGY RESOURCES IN GRIDS OF · Ed Bower UK RT 5c/4a MERGE: Mobile Energy Resou rces in...

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