Content CLEEN/SGEM Introduction • CLEEN – Cluster for Energy and Environment • SGEM – Smart Grids and Energy Markets SGEM and Electric Vehicles • What is the impact to the Grid? • What is the benefit to the Grid? • What is the benefit to the energy system? • What solutions have been developed?

CLEEN - The vehicle for SGEM Strategic Centre for Science, Technology and Innovation (CSTI)

Co-operative and Industry Driven R&D&I company for Energy and Environment founded in 2008. Unique in it’s way of serving 28 industry and 17 research shareholders by driving – Renewal of industry – Novel collaboration across industries – Strategic focus – Industry’s Commitment and Guidance – World class competence and resources Research volume (2012): 37,8 M€ in 6 different research programmes

SGEM overview Main Research Topics • •

•

•

•

Drivers & Visions of Smart Grid (WP1) Future infrastructure of Power Systems – MV and LV networks (WP2) – HV Networks (WP3) Active network components – Active customer and home (WP4) – Distributed energy resources, electric vehicles, energy storages (WP5) Intelligent management and operation of Smart Grid (WP6) – Enablincg ICT technologies – Active network management – Network analysis and planning Energy Markets (WP7)

Facts •

• • • •

Focus on power distribution and interfaces CLEEN program with Tekes public funding 5 year program 2009-2014 57 M€ budget. Industry make up 53% of the volume. 19 industry & 8 research partners. – 5 from Energy Technology sector – 6 from ICT Sector – 5 DSOs – 1 TSO – 2 Energy retailers

Electric vehicles research in SGEM Electric vehicles

Target: COST MINIMIZATION MINIMIZATION min  CInv - Net (t )Target:  COpex - COST Net (t )  C Inv - Storage (t )  COpex - Storage (t ) dt t

min  CInv - Net (t )  COpex - Net (t )  CInv - Storage (t )  COpex - Storage (t ) dt t

0

Electricity utility Electric vehicles Electricity end-user

0 Present peak load

z z

Electricity markets

Present peak load

Edischarge

PPeak

Pcut

EdischargeE

discharge

Power [kW]

Electricity end-user

Power [kW]

Electricity end-user z

Electricity utility

Electricity utility

PPeak

Pcut

E1

OR Edischarge = E1 = E2 OR

E1 day

evening day

= E1 = E2

E2 night

evening1.5 days

E2

night

Electric vehicles

1.5 days

Targets of EV-related research work in SGEM  Study effects of the future traffic  For the local distribution systems  For the energy market  Define and demonstrate the new business opportunities of electric vehicles with controllable Grid-to-Vehicle (G2) and Vehicle-to-Grid (V2G) operations

day day

What is the impact to the Grid?

Case: Network Effects of Electric Vehicles as a load (G2V) City

Case area o Located in Fortum Distribution network, Finland

o 20 kV network

(6 feeders)

from city, urban and rural areas

o Peak load on the feeder*:

3.6–8 MW /feeder

o Annual energy*:

10–32 GWh /feeder

o Number of delivery sites:

390–5200

/feeder

o Estimated number of cars: 980–4000

/feeder

Urban area

* Without electric vehicles

EV information

- Driving distance:

50 km/day per car

- Consumption:

0.2 kWh/km

- Charging power:

3.6 kW/car

Lassila Jukka, Haakana Juha, Partanen Jarmo, Koivuranta Kari, Peltonen Saara Network Effects of Electric Vehicles - Case from Nordic Country, CIRED, 21st International Conference on Electricity Distribution, Frankfurt, 6-9 June 2011, 2011, p. CD, art nro. 773.

Rural area

Case: Network Effects of Electric Vehicles as a load (G2V) 8.0

Load curves with EVs (100% and 50% penetration levels)

7.0

Peak power [MW]

6.0 5.0 4.0 Present load

3.0 2.0 Without EVs

100 % penetration level

50 % penetration level

1.0

Mon

Tue

Wed

Thu

Fri

Sat

Sun 12:00

0:00

12:00

0:00

12:00

0:00

12:00

0:00

12:00

0:00

12:00

0:00

12:00

0:00

0.0

One-year load curve with and without EVs Peak power with EVs (7.6 MW)

In residential area (urban area) evening and nighthours are the most challenging from the network capacity point of view Lassila Jukka, Haakana Juha, Partanen Jarmo, Koivuranta Kari, Peltonen Saara Network Effects of Electric Vehicles - Case from Nordic Country, CIRED, 21st International Conference on Electricity Distribution, Frankfurt, 6-9 June 2011, 2011, p. CD, art nro. 773.

Present peak power (5.6 MW)

Autumn

Winter

Spring

Summer

Case: Network Effects of Electric Vehicles as a load (G2V) Summary of the feeder-specific results (with control) Optimised charging

Optimised charging (red curve) for the feeder. All the energy for EVs can be taken from the network without increasing the present peak power.

There is demand for Smart Grid functions to optimize charging of electric vehicles. With successful optimization reinforcement of 7 M€ could be avoided in this case area. 



Intelligent control of charging of EVs is strongly recommended in order to  a) avoid unnecessary reinforcement investments and  b) avoid increase in distribution fees paid by the end-customers Similar case studies also made by Tampere University of Technology for Tampereen Sähköverkko Oy and JE-Siirto Oy

Lassila Jukka, Haakana Juha, Partanen Jarmo, Koivuranta Kari, Peltonen Saara, Network Effects of Electric Vehicles - Case from Nordic Country, CIRED, 21st International Conference on Electricity Distribution, Frankfurt, 6-9 June 2011, 2011, p. CD, art nro. 773. Unkuri Ari, Sähköautojen vaikutukset kaupungin sähkönjakeluverkkoon. TTY, Diplomityö, 2011 Tammi Antti, Sähköautojoen vaikutukset sähköverkkoyhtiön jakeluverkkoon ja liiketoimintaan. TTY, Diplmityö, 2011

What is the benefit to the Grid?

Case: Electric Vehicles as a storage (V2G) How much energy is needed to cut a certain peak level in one year?

25

Analysis

20

Peak power cut [kW]

PPeak = ? EPeak = ?

15

10

The shape of the curve depends on how spiky original load curve (without electric vehicles) is.

5

0

0

0.5

1

1.5

2

2.5

3

3.5

Needed energy for peak cutting [MWh/year]

Case: Electric Vehicles as a storage (V2G) Where is to optimum operating point (=the highest economic feasibility)? 150

25

Maximum savings

Peak power cut [kW]

20

Savings [€/a]

100

50

Break-even point

15

11 kW 10

5

0

0.35 MWh/a 0

1.75 MWh/a

0.35 MWh/a -50

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

EV charging

2

2.5

3

3.5

40

35

35

30

30

25

25

EV charging (optimal)

20

20

Base load

15

Cut distribution fee by -46% in the case network (compared to dumb charging) 10

10

5

5

0

1.5

2

The highest feasibility is reached when the peak is cut by 11 kW and with 0.35 MWh/a annual energy. 15

1

45

Charged/discharged energy to peak cutting [MWh/year] 40 Peak power [kW]

0.5

Needed energy for peak cutting [MWh/year]

45

Cut peak -30% in the case network

0

2 4 - Case 6 8 10 from 12 14 Lassila Jukka, Tikka Ville, Haakana Juha, Partanen Jarmo. Electric Cars as Energy Storages Study Nordic Country, European Conference Smart Grids and Mobility. Munich, Germany October 17th - 18th,Hour 2011.

16

18

20

22

24

0

Base load

2

4

6

8

10

12

Hour

14

16

18

20

22

24

What is the benefit to the energy market?

Modelling EVs in the energy system •

Include the effect of EVs both to investment decisions and operational costs of the energy system – Investment decisions and costs from Balmorel (www.balmorel.com) – Operational costs from Wilmar (www.wilmar.risoe.dk) – Grid impacts not included in this research case

•

Example case: Finland 2035 – Two extreme cases in charging: all Evs are ’smart’ and all Evs are ’dumb’ charging – ½ million full electric vehicles (FEVs), ½ million PHEVs – Estimated driving ranges, plug-in patterns and driving patterns

Kiviluoma, J., Meibom, P., Methodology for modelling plug-in electric vehicles in the power system and cost estimates for a system with either smart or dumb electric vehicles, Energy, epublished, DOI: 10.1016/j.energy.2010.12.053.

Storage [MWh] & charging [MW]

Storage capacity and usage (model results) 24 000 20 000 16 000 Storage capacity

12 000 8 000 Stored

4 000 0We

Th

Fr

Charging/discharging

Sa

-4 000 Thurs

Friday

Satur

Sunda

System benefits of Smart Charging

Day-ahead planning 36 %

Spinning reserves 17 %

250

Intraday Flexibility 47 %

€/vehicle/a

200 150 100 50

0

Total system benefit 227 €/vehicle/year V2G Scenario

Market benef it of smart Evs

Market price of smart Evs

Market price of dumb Evs

Reduction to benefit (€/vehicle/year)

No V2G allowed

53

V2G half of the vehicles

6,7

V2G in all vehicles

System benef it of smart Evs

0

Kiviluoma, J., Meibom, P., Methodology for modelling plug-in electric vehicles in the power system and cost estimates for a system with either smart or dumb electric vehicles, Energy, epublished, DOI: 10.1016/j.energy.2010.12.053.

CO2 emission impacts

Change in CO2 emissions of the power system (kgCO2/vehicle/year) kgCO2/vehicle/year

200

150 100

50 0 -50

Dumb

Smart

-100 -150 -200 -250

Smart charging allows for higher amount of wind power, when the availability of EV is taken into account in investment decisions

What solutions have been developed?

EV Charging concept based on ISO/IEC-15118 EV Charging pole EVCC

SECC Server

• EVCC

Cable/wireless

PLC

EVCC

Wlan, BT ZigBee

Smartphone & Tablet

User Interface & Back end service

Service/Electricity provider

Systemic support for Demand Reponse

Need for a holistic view on Demand Response, including EVs and also other active resources • Business models • Information exchange: Interfaces, load control messages • Active pilot environment: Test ecosystem viability

Laboratory demonstration of Interactive Customer Interface at TUT DNO control centre Network overload management Distribution Management System

Aggregator Information aggregation Supervision of ICT

Tout, forecast

Monitoring of reserves OPC-UA

Open EMS Suite

DB

XML

Home / building automation ThereGate Frequency dependent load shedding Peak load reduction

AMI

Tin

f

Thermostat

Discon nection

DG

User interface

Electric vehicle

SCADA COM 600

RTDB

Green Campus Living Lab at LUT 40 kW

Wind Turbine

Electricity, district heating and water grids LOAS

20 kW

(heating, lighting etc.)

(in operation)

200 kW

Solar Panels LUT

20 kW

(heating, lighting etc.)

(in operation)

158 kW

500–1000 kWh 1–2 MW

(in installation)

Battery Pack

Energy Management System

30 kWh

PHEV

PHEVs & EVs

(in laboratory tests)

(4.4 kWh, G2V + V2G, in operation)

Communication

BEV (24 kWh, G2V, in operation)

20–30 kW

E to Gas

PHEV (4.4 kWh, G2V + V2G, in operation)

BEV (24 kWh, G2V, in operation)

FlyWheels

Conclusions • Smart charging is required for EVs, in order in incorporate larger shares of EV • If the charging is smart, also more benefits are possible – Grid Operators – reduce/postpone network investments – Energy Markets – Allows larger share of wind power and increase of flexibility – Society – reduction of CO2 emissions

• In order to achieve smart charging and all benefits from it, collaboration between many shareholders is needed – – – – – –

Grid Owners EV owners Energy retailers Technology providers Demand Response service providers Research institutes

http://www.cleen.fi/en/sgem