This document summarizes research into the grid integration of electric vehicles. The research team aims to analyze how many EVs there may be, when and where they will charge, and evaluate infrastructure challenges and options for managing EV load. Experimental work and framework development will validate algorithms for intelligent charging and synergies with smart grids. An integrated model looks at automotive R&D, social impacts, electricity markets and intelligent infrastructure. The team analyzes impacts of EV penetration on distribution and generation systems using UK and Spanish case studies for 2030. Uncontrolled EV charging could increase peak demand significantly but controlled approaches may reduce impacts.

1. Grid Integration of Electric Vehicles
Dr. Liana Cipcigan
Lecturer
Energy Institute
CipciganLM@Cardiff.ac.uk
Research team
Panos Papadopoulos, PhD student
Inaki Grau, PhD student
Spyros Skarvelis-Kazakos, PhD student
Joint Supervision: Prof. Nick Jenkins, Energy Institute Leader
1

2. 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

3. Cardiff University Integrated approach of EVs integration
Automotive Automotive Social Supplier Electricity Markets
R&D Business Models R&D R&D Business Models
Intelligent infrastructure / Smart Grids
INTEGRATED MODEL
3

4. CAIR/CARBS
BRASS
JOMEC Sustainable automobility
Environmental regulations
Dissemination to non- New business models
Waste flows, biofuels
expert audience Social, economic &
feedstock
regulatory impacts
CPLAN
Transport and built
environment
Travel behaviour EVCE Core Team
PSYCH
Huw Davies, ENGIN
Consumer psychology
Liana Cipcigan, ENGIN
Travel behaviour
Paul Nieuwenhuis, CARBS
Low Carbon Research
Institute
COMP
Road Traffic
Management Systems
Centre for Sustainable
Places ENGIN
Vehicle engineering, powertrain,
safety, lightweight structures
Smart grids 4

5. 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.
Energy Management
Dr. Liana Cipcigan
ENGIN
ELECTRIC VEHICLE
CENTRE OF
EXCELLENCE
Structures & Materials Impact Assessment
Dr. Huw Davies Dr. Paul Nieuwenhuis
ENGIN CARBS
5
http://www.engin.cf.ac.uk/research/resTheme.asp?ThemeNo=5

6. Assumptions
Study cases EVs penetration
Analysis EVs charging regimes
Impact on Impact on
Uncontrolled Dual Dynamic
distribution generation
tariff price
system system
Charging
Infrastructure
Control Validation Toolkit
Technical Algorithms Experimental SG Scenarios
constraints
Standards
6

7. 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
7
vehicles and plug-in hybrid vehicles’, 2008.

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
EV uptake predictions in 2030 by country, level, and type
of vehicle
Ref
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)
8

9. Traffic distributions
Uncontrolled case
Nb. of commuters starting the charging process
Low EV uptake High EV uptake
9

10. Electricity Demand with Electric Vehicles in 2030
British predicted energy demand for uncontrolled charging in 2030
Uncontrolled EV charging regime increase
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%)
10

11. Selected results and conclusions 2030
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)
Load Factor Load Factor
SPAIN GB
Electricity Demand (GW)
Electricity Demand (GW)
120 120
67% 120
100 107.8 100
Installed Generation
Installed Generation
80 80
Effective Generation
4.9
3.2
69.9
Effective Generation
60 67.5 60
without EVs
without EVs
Low EV
Low EV
Uptake
Demand
Uptake
Demand
40 40%
32%
40
20 20
75 70.7
0 0
11

12. EV impact on Generation at National Level
~ 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
12

13. Assumptions
Study cases EVs penetration
Analysis EVs charging regimes
Impact on Impact on
Uncontrolled Dual Dynamic
distribution generation
tariff price
system system
Charging
Infrastructure
Control Validation Toolkit
Technical Algorithms Experimental SG Scenarios
constraints
Standards
13

14. Case study for 2030
33/11.5kV
Source 3072 customers UK GENERIC
NETWORK
~
500 MVA
11kV/0.433kV
384 customers
96 customers
Parameter Nominal
Rating INPUTS FOR 2030 (PROJECTIONS PER 3,072
CUSTOMERS)
Transformer loading 500 kVA
Type of EV Low Medium High
BEV (35kWh) 128 256 640
185mm2 cable 347A
PHEV (9kWh) 256 768 1536
loading
384 1024 2176
Voltage 230V (1 phase) Total
(12%) (33%) (70%)
Ref
S. Ingram, and S Probert, ―The impact of small scale embedded generation on the operating parameters of distribution networks‖, 14
P B Power, Department of Trade and Industry (DTI), 2003.

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) • EV Charger Ratings
• Charging Time Occurrence • EV Battery Capacities
• Charging Duration • EV Charger and Battery Efficiencies
Outputs
• Impact on Distribution Transformer and Cable Thermal Loadings
• Impact on Steady State Voltage
15
• Impact on Distribution system efficiency (losses)

16. Results
• 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
Ref
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).
16

17. Assumptions
Study cases EVs penetration
Analysis EVs charging regimes
Impact on Impact on
Uncontrolled Dual Dynamic
distribution generation
tariff price
system system
Charging
Infrastructure
Control Validation Toolkit
Technical Algorithms Experimental SG Scenarios
constraints
Standards
17

18. Collaborative Research FP7 MERGE
Mobile Energy Resources in Grids of Electricity
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
18
http://www.ev-merge.eu/

19. Virtual Power Plant
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
• EVs ???
Ref
* Virtual Power Plant Concept in Electrical Networks. Juan Martí (2007) [FENIX project] 19

20. Electric Vehicle Supplier / Aggregator
EV Aggregator: Entity which sells electricity to the EV owners, aggregates and
manages their load demand.
EV Aggregator basic functions:
Market Forecast
Short Term Scheduling Decision Making
Medium Term
Control
Long Term
Monitoring
Load Forecast
Short Term
Billing Communications Interface
Medium Term
Long Term
Provide information for Share information with
Regulators govern the future of Aggregators
21

21. Possible architectures of the EV Aggregator (EVA)
Control Aggregator
Centralized
EV EV EV EV EV Direct Control
Aggregator
De-Centralised
Control EV EV EV EV EV
Distributed Control
Aggregator Level 1
Aggregator Aggregator Aggregator Level 2 Hierarchical
Agg Agg Agg Level n
Control
EV EV EV
Ref
I. 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) 22

22. Interaction between the VPP Control Center and the VPP resources,
DSO, TSO and market in the direct control approach
Ref
A. 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

23. Interaction between the VPP control center and the VPP
resources, DSO, TSO and market in the hierarchical approach
Ref
A. 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 24

24. Interaction between the VPP control center and the VPP resources,
DSO, TSO and market in the distributed control approach
Ref
A. 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 25

25. Assumptions
Study cases EVs penetration
Analysis EVs charging regimes
Impact on Impact on
Uncontrolled Dual Dynamic
distribution generation
tariff price
system system
Charging
Infrastructure
Control Validation Toolkit
Technical Algorithms Experimental SG Scenarios
constraints
Standards
26

26. Distributed Energy Resources Research Infrastructure
Project 1 –Electric Vehicle Operated Low Voltage Electricity networks with
Multi- Agent Systems, TECNALIA-LAB, Spain
DSO
MARKET
EVA
CAMC agent
agent
CVC
MGAU agent
agent
EV EV EV
agent agent 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
27

27. Adaptation of UK Generic Distribution Network to
TECNALIA Laboratory Microgrid
UK Generic Network
Commercial area
EV
agent
RAU
EV
agent
agent
EV EV
33/11.5kV agent agent
Grid Supply EV
agent
~ ...
CVC
agent
EV
agent
500 MVA ...
CAMC
MGAU
agent EV EV
agent agent agent
EV EV
agent agent
Residential area
28

28. Test Network in TECNALIA Laboratory Microgrid
RAU
Network configuration Agent
Agent System
CAMC MGAU
Agent Agent
CSDER/IEC
61850
EV
Agent
Grid
Communication of MAS
with Equipment
Load Banks
Controller
KEY
Two way
Monitoring communication EV
Avtron Millenium
One way Disconnection
Communication Instruction Avtron K595 DMMS300
29

29. Distributed Energy Resources Research Infrastructure
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
56 VPP Aggregator A
55 Winter
Emission factor (gCO 2 /km)
Summer
54 NTUA Micro-Grid CRES Micro-Grid
53 Aggregator A A Aggregator
High Penetration
52
Low Penetration
NTUA
51
PV
A A A A
50
System
49 G G G G
48
CRES CRES CRES
0% 10% 20% 30% 40% 50% 60% 70%
Micro-generation penetration level
80% 90% 100%
A Agent
Diesel PV Fuel
G Micro-Generator Engine System Cell
EV emission factor improves by increasing
micro-generation penetration [Ref] The laboratory system, NTUA and CRES
Ref
S. Skarvelis-Kazakos, P. Papadopoulos, I. Grau, A. Gerber, L.M. Cipcigan, N. Jenkins and L. Carradore, (2010), “Carbon Optimized
Virtual Power Plant with Electric Vehicles”, 45th Universities Power Engineering Conference (UPEC), Cardiff, 31 Aug – 3 Sept 2011
30

30. Smart Management of Electric Vehicles
EVs load forecasting
Smart Management of EVs
Evaluate the performances of the algorithms through case
studies
Laboratory evaluation
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- 31
power-management-systems/1009752.article

31. Assumptions
Study cases EVs penetration
Analysis EVs charging regimes
Impact on Impact on
Uncontrolled Dual Dynamic
distribution generation
tariff price
system system
Charging
Infrastructure
Control Validation Toolkit
Technical Algorithms Experimental SG Scenarios
constraints
Standards
32

32. 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
Project application in NW zone http://www.enevate.eu/ 33

33. WP 1: Electric WP 2:Sustainable WP 3: Market WP 4: Pilots
Vehicle Energy supply drivers and
Technology infrastructure mobility concepts
•Analysis of existing
•Supply chain •Knowledge Building •Define integrated EV Pilots in NWE
analysis sustainable e-
•Transnational Mobility concepts •Implementation of
•Instruments to Consultation & •Market analysis ENEVATE findings
develop strong Research user acceptance in regional pilots
supply chain •Scenario building
•Tool Kit for future •Finalising
Development & sustainable guidelines and
evaluation integrated e-Mobility lessons learned
concepts
•Developing support
instruments
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 & implementation 34

34. WP 2 Sustainable
Energy supply infrastructure
WP2 Leader
Tool Kit Development & evaluation
• 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 35

35. 36

36. Scenarios for the development of
Smart Grids in the UK
Partners:
• Identify critical steps in the development of SGs National Grid
• Identify how differences in fuel generation and sources, E.ON
geography, environmental concerns, the regulatory UK Power Networks
environment governing investment and market access,
UPL
funding complexity, and consumer values present
incentives or pose barriers for the deployment of SGs IBM
Nottingham Horizon Digital
• Develop socio-technical scenarios for UK SG
Economy
deployment in the period to 2050
Durham University, LCNF project
• Explore expert/stakeholder and public perceptions of
Low Carbon Research Institute ,CU
transition points and fully developed scenarios,
highlighting social, behavioural and regulatory/market EcoTown
opportunities and barriers. SustainabilityFirst
FDT Fintry Development Trust
USA Smart Grid Policy, Edison
Electric Institute
37

37. Assumptions
Study cases EVs penetration
Analysis EVs charging regimes
Impact on Impact on
Uncontrolled Dual Dynamic
distribution generation
tariff price
system system
Charging
Infrastructure
Control Validation Toolkit
Technical Algorithms Experimental SG Scenarios
constraints
Standards
38

38. IEEE Standards Association
WG p.2030.1, Guide for Transportation Electrification
http://grouper.ieee.org/groups/scc21/2030.1/2030.1_index.html
39

39. 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

40. We need to understand many components
• 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

41. We need to understand many components
• 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 image
42

42. POLAR
UK’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

43. Electric Highway
44