Liana Cipcigan - Grid Integration of Electric Vehicles

Grid Integration of Electric Vehicles

Dr. Liana Cipcigan

Lecturer

Energy Institute

[email protected]

Research team

Panos Papadopoulos, PhD student

Inaki Grau, PhD student

Spyros Skarvelis-Kazakos, PhD student

Joint Supervision: Prof. Nick Jenkins, Energy Institute Leader

1

mailto:[email protected]

EVs Grid Integration -What Questions are we trying to answer?

Analysis

• How many EV? – EV uptake scenarios, impact on generation system, impact on

distribution networks

• When will they charge? – temporal analysis

• Where will they connect for charging? – spatial analysis

Evaluation & Control

• What are the infrastructure challenges of EV fleet?

• What are the options for managing the spatial-temporal nature of the load?

• What is the role of the Aggregator, locating the charger inside the aggregator?

• Intelligent charging?

• Synergies with Smart Grids?

Experimental, Validation, Framework, Standards

• Algorithms validation, experiment with aggregator?

• Framework, standards development

2

SupplierR&D

Cardiff University Integrated approach of EVs integration

Intelligent infrastructure / Smart Grids

SocialR&D

AutomotiveR&D

3

AutomotiveBusiness Models

Electricity Markets

Business Models

INTEGRATED MODEL

4

EVCE Core TeamHuw Davies, ENGIN

Liana Cipcigan, ENGINPaul Nieuwenhuis, CARBS

BRASSEnvironmental regulations

Waste flows, biofuelsfeedstock

PSYCHConsumer psychology

Travel behaviour

ENGINVehicle engineering, powertrain,

safety, lightweight structuresSmart grids

COMPRoad Traffic

Management Systems

CAIR/CARBSSustainable automobility

New business modelsSocial, economic & regulatory impacts

JOMECDissemination to non-

expert audience

CPLANTransport and built

environmentTravel behaviour

Low Carbon Research Institute

Centre for Sustainable Places

Electric Vehicle Centre of Excellence

• EVCE is based in School of Engineering at Cardiff University.

• Its purpose is the co-ordination and promotion of research activities in the EV area.

• The centre draws upon skills and competencies from across the University.

• Present emphasis is on energy management, structures & materials and impact assessment.

5

Energy ManagementDr. Liana Cipcigan

ENGIN

Structures & MaterialsDr. Huw Davies

ENGIN

Impact AssessmentDr. Paul Nieuwenhuis

CARBS

ELECTRIC VEHICLE CENTRE OF

EXCELLENCE

http://www.engin.cf.ac.uk/research/resTheme.asp?ThemeNo=5

http://www.engin.cf.ac.uk/research/resTheme.asp?ThemeNo=5

Study cases

Analysis

EVs penetration

EVs charging regimes

Uncontrolled Dual

tariff

Dynamic

price

Impact on

distribution

system

Technical

constraints

Control

Algorithms

Assumptions

Validation

Experimental

Charging

Infrastructure

Toolkit

SG Scenarios

Standards

Impact on

generation

system

6

EV uptake projections

In Europe[1]

In the UK[2]

[1] Hacker F., et al. ―Environmental impacts and impact on the electricity market of a large scale introduction of electric cars in Europe - Critical Review of Literature’,

The European Topic Centre on Air and Climate Change, 2009.[2] Department for Business Enterprise and Regulatory Reform: Department for Transport: ’Investigation into the scope for the transport sector to switch to electric

vehicles and plug-in hybrid vehicles’, 2008.7

8

EV impact on generation system

• Case Study for 2030 and EV penetration levels projected by [1] for GB and Spain in collaboration

with TECNALIA, Spain

P. Papadopoulos, O. Akizu, L. M. Cipcigan, N. Jenkins, E. Zabala,

Electricity Demand with Electric Cars: Comparing GB and Spain, Proc. IMechE Vol. 225 Part A: J. Power and Energy, pp.551-566,

(2011)

Ref

EV uptake predictions in 2030 by country, level, and type

of vehicle

9

Traffic distributions

Low EV uptake

Uncontrolled case

Nb. of commuters starting the charging process

High EV uptake

Electricity Demand with Electric Vehicles in 2030

10

British winter day peak demand by 3.2 GW (3.1%) for low EV uptake case (7%)

British winter day peak demand by 37GW (59.6%) for high EV uptake case (48.5%)

Uncontrolled EV charging regime increase

British predicted energy demand for uncontrolled charging in 2030

Selected results and conclusions 2030E

lect

rici

ty D

eman

d (

GW

)

Dem

and

wit

ho

ut

EV

s

Dem

and

wit

ho

ut

EV

s

4.9

Lo

w E

V

Up

tak

e

Inst

alle

d G

ener

atio

n

Inst

alle

d G

ener

atio

n

40%

Electricity D

eman

d (G

W)

32%

Eff

ecti

ve G

ener

atio

n

Eff

ecti

ve G

ener

atio

n

GBSPAIN

67%

P. Papadopoulos, O. Akizu, L. M. Cipcigan, N. Jenkins, E. Zabala,

Electricity Demand with Electric Cars: Comparing GB and Spain, Proc. IMechE Vol. 225 Part A: J. Power and Energy, pp.551-566,

(2011)

11

0

20

40

60

80

100

120

0

20

40

60

80

100

120

3.2

Lo

w E

V

Up

tak

e

67.5

120

70.7

Load FactorLoad Factor

69.9

75

107.8

~ 3mil cars of ~42mil vehicle fleet

(7% Low market EV penetration prediction)

• Isn’t enough to make a real impact on energy demand at the national

level

• EVs impact is expected to be at the local level

• Impact on LV distribution hotspots depends on clustering

EV impact on Generation at National Level

12

Study cases

Analysis

EVs penetration


Uncontrolled Dual

tariff

Dynamic

price

Impact on

distribution

system

Technical

constraints

Control

Algorithms

Assumptions

Validation

Experimental

Impact on

generation

system

13

Charging

Infrastructure

Toolkit

SG Scenarios

Standards

Case study for 2030

11kV/0.433kV

Source

500 MVA

~

96 customers

384 customers

3072 customers UK GENERIC

NETWORK

33/11.5kV

S. Ingram, and S Probert, ―The impact of small scale embedded generation on the operating parameters of distribution networks‖,

P B Power, Department of Trade and Industry (DTI), 2003.

INPUTS FOR 2030 (PROJECTIONS PER 3,072

CUSTOMERS)

14

Parameter Nominal

Rating

Transformer loading 500 kVA

185mm2 cable

loading

347A

Voltage 230V (1 phase)

Ref

Type of EV Low Medium High

BEV (35kWh) 128 256 640

PHEV (9kWh) 256 768 1536

Total384

(12%)

1024

(33%)

2176

(70%)

15

Probabilistic Tool for the Evaluation of EV Impacts on LV Networks

Uncertainties concerned with EV integration in residential networks

Behavioural Technical (Type of EV and Equipment)

• Ownership (Location)

• Charging Time Occurrence

• Charging Duration

• EV Charger Ratings

• EV Battery Capacities

• EV Charger and Battery Efficiencies

Outputs

• Impact on Distribution Transformer and Cable Thermal Loadings

• Impact on Steady State Voltage

• Impact on Distribution system efficiency (losses)

• Residential charging of EV batteries will overload distribution networks and

modify voltage profile of feeders.

• The distribution transformer was found to be overloaded for medium and

high EV penetration.

• The voltage limits would be violated for medium and high EV penetrations.

• The 185mm2 cable was found to be overloaded for most 2030 cases.

• The results from this research are used for the design of algorithms to allow

the efficient management of charging infrastructure

16

Results

P. Papadopoulos, S. Skarvelis-Kazakos, I. Grau, L. M. Cipcigan, N. Jenkins,

Predicting Electric Vehicle Impacts on Residential Distribution Networks with Distributed Generation, IEEE VPPC(2010).

P. Papadopoulos, S. Skarvelis-Kazakos, I. Grau, B. Awad, L. M. Cipcigan, N. Jenkins,

Impact of Residential Charging of Electric Vehicles on Distribution Networks, a Probabilistic Approach, UPEC, Cardiff, (2010).

Ref

Study cases

Analysis

EVs penetration


Uncontrolled Dual

tariff

Dynamic

price

Impact on

distribution

system

Technical

constraints

Control

Algorithms

Assumptions

Validation

Experimental

Impact on

generation

system

17

Charging

Infrastructure

Toolkit

SG Scenarios

Standards

Collaborative Research FP7 MERGE Mobile Energy Resources in Grids of Electricity

http://www.ev-merge.eu/18

Deliverable 2: Extend Concepts of MicroGrid by Identifying Several EV Smart Control

Approaches to be embedded in the Smart Grid Concept to manage EV individually

or in Clusters

Deliverable3: Controls and EV Aggregation for Virtual Power Plants

Virtual Power Plant (VPP)

• The virtual power plant offers the opportunity to aggregate Distributed

Energy Resources and create a single flexible portfolio. This way it enables

their participation in the wholesale electricity and ancillary services

markets.

• Early VPP definitions considered only Distributed Generators. Updated

definitions consider DER, which include:

• DG

• Controllable loads

• Energy storage

* Virtual Power Plant Concept in Electrical Networks. Juan Martí (2007) [FENIX project]

Virtual Power Plant

• EVs ???

*

19

Ref

Electric Vehicle Supplier / Aggregator

EV Aggregator: Entity which sells electricity to the EV owners, aggregates and

manages their load demand.

Market Forecast

Decision Making

Monitoring

Scheduling

Communications InterfaceBilling

Short Term

Medium Term

Long Term

Load Forecast

Short Term

Medium Term

Long Term

Control

Provide information for Share information with

EV Aggregator basic functions:

21Regulators govern the future of Aggregators

Centralized

Direct Control

De-Centralised

Distributed Control

Hierarchical

Control

Aggregator

EV EV EV EV EV

Control

EV EV EV EV EVControl

Aggregator

Aggregator

Level 2

Level 1

Level n

EV

Aggregator Aggregator Aggregator

Agg Agg Agg

EV EV

Possible architectures of the EV Aggregator (EVA)

22

RefI. Grau, P. Papadopoulos, S. Skarvelis-Kazakos, L. M. Cipcigan, N. Jenkins, Virtual Power Plants with Electric Vehicles,

2nd European Conference SmartGrids and E-Mobility, Brussels, Belgium, (2010)

Interaction between the VPP Control Center and the VPP resources,

DSO, TSO and market in the direct control approach

RefA. F. Raab, M. Ferdowsi, E. Karfopoulos, I. Grau Unda, S. Skarvelis-Kazakos, P. Papadopoulos, E. Abbasi, L.M. Cipcigan, N. Jenkins, N.

Hatziargyriou, and K. Strunz, Virtual Power Plant Control Concepts with Electric Vehicles, ISAP 2011, Crete, Greece, 2011 23

Interaction between the VPP control center and the VPP

resources, DSO, TSO and market in the hierarchical approachRef

A. F. Raab, M. Ferdowsi, E. Karfopoulos, I. Grau Unda, S. Skarvelis-Kazakos, P. Papadopoulos, E. Abbasi, L.M. Cipcigan, N. Jenkins, N.


Interaction between the VPP control center and the VPP resources,

DSO, TSO and market in the distributed control approachRef

A. F. Raab, M. Ferdowsi, E. Karfopoulos, I. Grau Unda, S. Skarvelis-Kazakos, P. Papadopoulos, E. Abbasi, L.M. Cipcigan, N. Jenkins, N.


Study cases

Analysis

EVs penetration


Uncontrolled Dual

tariff

Dynamic

price

Impact on

distribution

system

Technical

constraints

Control

Algorithms

Assumptions

Validation

Experimental

Impact on

generation

system

26

Charging

Infrastructure

Toolkit

SG Scenarios

Standards

Distributed Energy Resources Research Infrastructure

CAMC

agent

KEY

Normal/Alert operation communications Emergency operation communications

EVA Electric Vehicle Aggregator CAMC Central Autonomous Management Controller

MGAU MicroGrid Aggregation Unit CVC Clusters of Vehicles Controllers

CVC

agent

EV

agent

EV

agent

EV

agent

MARKET

EVA

agent

DSO

MGAU

agent

Project 1 –Electric Vehicle Operated Low Voltage Electricity networks with

Multi- Agent Systems, TECNALIA-LAB, Spain

27

33/11.5kV

~

Grid Supply

500 MVA

Residential area

. . .

EV

agent

EV

agent

EV

agent

EV

agent

EV

agent

EV

agent

CVC

agent

MGAU

agentEV

agent

Commercial area

CAMC

agent

EV

agent

EV

agent

EV

agent

. . .

RAU

agent

Adaptation of UK Generic Distribution Network to

TECNALIA Laboratory Microgrid

UK Generic Network

28

Two way

communication

CAMC

Agent

MGAU

Agent

RAU

Agent

EV

Agent

Disconnection

Instruction

One way

Communication

KEY

Load Banks

Controller

Avtron Millenium

Avtron K595 DMMS300

Monitoring

Grid

EV

Network configuration

CSDER/IEC 61850

Test Network in TECNALIA Laboratory Microgrid

Agent System

Communication of MAS

with Equipment

29

Project 2 – Electric Vehicles in VPP

Title: Carbon Agents for a Virtual Power Plant, in National Technical University of

Athens (NTUA) and Center for Renewable Energy Sources (CRES), Greece

30

The laboratory system, NTUA and CRES

Distributed Energy Resources Research Infrastructure

A Agent

G

A

G

G G G

A A A

A

AVPP Aggregator

CRES Micro-Grid

Aggregator

NTUA

PV

System

CRES

Diesel

Engine

CRES

PV

System

CRES

Fuel

Cell

ANTUA Micro-Grid

Aggregator

Micro-Generator

48

49

50

51

52

53

54

55

56

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Micro-generation penetration level

Em

issi

on

facto

r (g

CO

2/k

m)

WinterSummer

Lo

w P

en

etr

ati

on

Hig

h P

en

etr

ati

on

EV emission factor improves by increasing

micro-generation penetration [Ref]

S. Skarvelis-Kazakos, P. Papadopoulos, I. Grau, A. Gerber, L.M. Cipcigan, N. Jenkins and L. Carradore, (2010), “Carbon OptimizedVirtual Power Plant with Electric Vehicles”, 45th Universities Power Engineering Conference (UPEC), Cardiff, 31 Aug – 3 Sept 2011

Ref

Smart Management of Electric Vehicles

EVs load forecasting

Smart Management of EVs

Evaluate the performances of the algorithms through case

studies

Laboratory evaluation

31

Partners:

E.ON

UPL

Future Transport Systems

Mott MacDonald (PhD student

industrial placement)

TECNALIA Lab, Spain

WAG

http://www.theengineer.co.uk/sectors/energy-and-environment/news/research-aims-to-deliver-ev-power-management-systems/1009752.article




















Study cases

Analysis

EVs penetration


Uncontrolled Dual

tariff

Dynamic

price

Impact on

distribution

system

Technical

constraints

Control

Algorithms

Assumptions

Validation

Experimental

Impact on

generation

system

32

Charging

Infrastructure

Toolkit

SG Scenarios

Standards

Lead Partner: Automotive Technology Centre (NL)

11 partners from Belgium, Germany, UK. Ireland and France

CU is leading WP3 – Market Drivers and Mobility Concepts

Budget €5.04 m (50% funded) Priority 1.1

http://www.enevate.eu/Project application in NW zone 33

http://www.enevate.eu/

WP 1: ElectricVehicle

Technology

•Supply chain

analysis

•Instruments to

develop strong

supply chain

WP 2:SustainableEnergy supply infrastructure

•Knowledge Building

•Transnational

Consultation &

Research

•Tool Kit

Development &

evaluation

WP 3: Market drivers and

mobility concepts

•Define integrated

sustainable e-

Mobility concepts

•Market analysis

user acceptance

•Scenario building

for future

sustainable

integrated e-Mobility

concepts

•Developing support

instruments

WP 4: Pilots

•Analysis of existing

EV Pilots in NWE

•Implementation of

ENEVATE findings

in regional pilots

•Finalising

guidelines and

lessons learned

WP 5: Enabling / Innovation Accelerator

- Create E-Mobility roadmap - Provide Policy Recommendations

-Stimulation and active coaching of EV - Development and implementation supply

chain development and innovations training programs

-Facilitate acceleration of e-mobility innovation & implementation34

WP 2 Sustainable

Energy supply infrastructure

Tool Kit Development & evaluation

35

• Vision

– To develop a practical Tool Kit that can be used by developers to de-risk

and optimise the effective and efficient roll out of electric vehicle

infrastructure.

– To create an integrated delivery process spanning from the sources of

sustainable electricity through to the electric vehicle itself.

– To apply, test and optimise the Tool Kit using the leading trial projects

being delivered across Northern Europe.

• Components of the Tool Kit

– Outline of key issues

– Process map

– Project plan with critical path

– Guidance notes

– Roles & Responsibilities/Stakeholder table

– Risk register

– Regional variations

WP2 Leader

36

Scenarios for the development of

Smart Grids in the UK

• Identify critical steps in the development of SGs

• Identify how differences in fuel generation and sources,

geography, environmental concerns, the regulatory

environment governing investment and market access,

funding complexity, and consumer values present

incentives or pose barriers for the deployment of SGs

• Develop socio-technical scenarios for UK SG

deployment in the period to 2050

• Explore expert/stakeholder and public perceptions of

transition points and fully developed scenarios,

highlighting social, behavioural and regulatory/market

opportunities and barriers.

Partners:

National Grid

E.ON

UK Power Networks

UPL

IBM

Nottingham Horizon Digital

Economy

Durham University, LCNF project

Low Carbon Research Institute ,CU

EcoTown

SustainabilityFirst

FDT Fintry Development Trust

USA Smart Grid Policy, Edison

Electric Institute

37

Study cases

Analysis

EVs penetration


Uncontrolled Dual

tariff

Dynamic

price

Impact on

distribution

system

Technical

constraints

Control

Algorithms

Assumptions

Validation

Experimental

Impact on

generation

system

38

Charging

Infrastructure

Toolkit

SG Scenarios

Standards

IEEE Standards Association

WG p.2030.1, Guide for Transportation Electrification

39

http://grouper.ieee.org/groups/scc21/2030.1/2030.1_index.html

Concluding remarks

We need to understand many components

• Electricity as a transportation fuel

• Make charging infrastructure convenient for the EV user – strong support to EV purchase

• Minimize stress upon the grid

• Benefits for driver

– charging as value-added service

– combination with loyalty programs

– discount on power for spending

– automatic notification about status

– web / SMS services

40


• Complex management of large EV fleets

• Integrated analysis of electricity / smart grids / transportation / market

• There is an important investments in charging infrastructure

• Interaction with the grid – EVs becomes an active participant in grid operations– Potential for energy storage

– Ancillary services

– Grid regulation

• EVs synergistic with Smart Grid– Digital Communications - Information flow between vehicle and utility—on

some level—is critical to maximizing value

– Information Flow Control

– Power Flow Control

– Decision Algorithms

41


• Pilot projects and experimental work – experiences of what works, what

doesn’t and commonalities for standardization

• Benefits for station providers

– additional revenue streams

– differentiation to competitors

– holding customers for longer time

– attracting customers during slow periods

– promotion and special rates by SMS or

– location-based services

– combination with loyalty programs

• Infrastructure standards are crucial

• Emissions reductions and environmental image42

POLARUK’s first privately funded nationwide EV charging network

• Private sector led initiative - entirely privately funded with no

Government or local authority financial support.

• Chargemaster Plc, the leading provider of EV charging

infrastructure in Europe

• POLAR - 100 towns and cities across the UK

• 4,000 fully installed electric vehicle charging bays by the end of

2012

• In each of the 100 towns and cities, POLAR will operate around 40

publically available charging bays

• Chargemaster will work with each PiP areas

• The initial rollout over the first nine months will involve 50 towns

and cities: Basingstoke, Bristol, Cardiff, Bournemouth,

Cheltenham, Crawley, Derby, Eastbourne, Exeter, Gloucester,

Guildford, High Wycombe, Maidenhead, Maidstone, Newbury,

Plymouth, Poole, Portsmouth, Reading, Rochester, Slough, Staines

Southend-on-Sea, St. Albans, Southampton, Swansea, Swindon,

Taunton, Telford, Warwick and Wokingham 43

Electric Highway

44