Introduction - tu-dresden.de · Table: Characteristics of diﬀerent Electric Vehicles penetration...

Escuela Tecnica Superior de Ingenierıa - ICAI

Instituto de Investigacion Tecnologica

How can the use of electric vehicles affect thecurtailment of renewable generation?

Kristin Dietrich, Jesus M. Latorre, Luis Olmos, Andres Ramos

ENERDAY 2011

Dresden, 08.04.2011

1

Introduction

Introduction

Large-scale integration of wind energy

Instituto de Investigacion TecnologicaEscuela Tecnica Superior de Ingenierıa - ICAIUniversidad Pontificia Comillas

EV and curtailment of RES - K.Dietrich, J.M.Latorre, L.Olmos, A.Ramos08.04.2011 3

Introduction


• Installed wind capacity on 31.12.2010: 19,813 MW / 20.3%.

• Energy Share in demand coverage 2010: 16% of demand.



Introduction




• Max. average hourly power: 11th January 2010 - 44,122 MW.

• Max. daily energy: 12th January 2010 - 895 GWh.

• Period of summer: 19th July 2010 - historic record average hourlypower 40,934 MW.



Introduction




• Max. average hourly power: 11th January 2010 - 44,122 MW.

• Max. daily energy: 12th January 2010 - 895 GWh.

• Period of summer: 19th July 2010 - historic record average hourlypower 40,934 MW.

• Records of daily wind energy: 9th November 2010 - 315,258 MWh(43% of daily demand).

• Records of monthly wind energy: February 2010 - 21 %.



Introduction

Large-scale integration of wind energy II



Introduction

Large-scale integration of wind energy II• Records of demand coverage by wind 2010:

09.11. 3.35 a.m. 54% versus 26.06. 10.32 a.m.: less than 1%.



Introduction

Large-scale integration of wind energy II• Records of demand coverage by wind 2010:

09.11. 3.35 a.m. 54% versus 26.06. 10.32 a.m.: less than 1%.

Intermittent EnergiesIntermittent energies are characterised by their variability and uncertainty inprediction.



Introduction

Electric Vehicle penetrations in Spain



Introduction


• With higher share of Wind energy - problems duringoffpeak hours.

• Other energy technologies need to be prepared to cover demand peaks.



Introduction


• With higher share of Wind energy - problems duringoffpeak hours.

• Other energy technologies need to be prepared to cover demand peaks.

• Electric Vehicles consumption pattern opposite to normal electricitydemand.

• Increase of demand during offpeak hours.

• Possible generation contribution to peak hours.

• Conditioned by application of smart charge technologies.



2

Model for the UC Problem

Basic Model

Modeling approach for the Unit Commitment



Basic Model


Cost Minimization



Basic Model


Cost Minimizations.t.Demand balanceReserve up/downGeneration limits min/maxRamping constraints up/downLogic coherence for start-up and shutdown ties



EV Model

Modeling approach for Electric Vehicles



EV Model

Modeling approach for Electric Vehicles

Cost Minimizations.t.Demand balanceBattery balanceReserve up/downGeneration limits min/maxBattery limits min/maxRamping constraints up/downRamping constraints for charge and discharge of batteryLogic coherence for start-up and shutdown ties



3

Data and assumptions

Data

Generation and Demand



Data

Generation and Demand• Installed Capacity of:

– conventional generation: 53 GW– renewable generation: 77 GW

• Energy demand: 373 TWh



Data

Generation and Demand• Installed Capacity of:

– conventional generation: 53 GW– renewable generation: 77 GW

• Energy demand: 373 TWh• Wind data: Based on historic time series of wind production and error of

wind forecast• Demand data: Historic time series for Spain

5%5%

19%

2%

13%

4%

29%

10%

13%

Share of total installed generation capacity

NuclearCoalCombined CyclesGas TurbinesHydro StoragePumped StorageWindCombined HeatOther RES



Assumptions EV

Data about driving pattern

• Weekday:– Distance: 35km– EV usage: 7-10 a.m. and 5-8 p.m.– Connection: 8 p.m. - 7 a.m. (no connection during day)



Assumptions EV

Data about driving pattern

• Weekday:– Distance: 35km– EV usage: 7-10 a.m. and 5-8 p.m.– Connection: 8 p.m. - 7 a.m. (no connection during day)

• Weekend:– Distance: 35km– EV usage: 10 a.m. - 8 p.m.– Connection: 8 p.m. - 10 a.m. (no connection during day)



Assumptions EV

Data about connection pattern

2 4 6 8 10 12 14 16 18 20 22 240

20

40

60

80

100

Hours

%

weekdayweekend

Figure: Percent of Plugged-in Electric Vehicles



Results

Changing the EV penetration



Results


Table: Characteristics of different Electric Vehicles penetration scenarios

Scenario name 0kEV 100kEV 250kEV 500kEV 1000kEV

Number of EV 0 100,000 250,000 500,000 1,000,000Charging behaviour Smart Charge in all scenarios



Results

Changing the EV penetration II



Results

Changing the EV penetration II

Scenario name 0EV 100kEV 250kEV 500kEV 1000kEVNumber of EV 0 100,000 250,000 500,000 1,000,000Charging behaviour Smart Charge in all scenariosWind curtailment 990 1,040 1,096 780 722[GWh]Annual Cost 13,703 13,747 13,759 13,766 13,835[Mio. Euro]



Results

Wind curtailment profile under different

penetrations of EV



Results


penetrations of EV

2 4 6 8 10 12 14 16 18 20 22 240

100

200

300

400

500

600

700

Hours

Win

d cu

rtai

lmen

t [M

W]

0EV100EV250EV500EV1000EV

(a) Weekday

2 4 6 8 10 12 14 16 18 20 22 240

100

200

300

400

500

600

700

Hours

Win

d cu

rtai

lmen

t [M

W]

0EV100EV250EV500EV1000EV

(b) Weekend

Figure: Wind curtailment profile for different EV scenarios (a) and (b)



Results

Annual productions by technologies



Results

Annual productions by technologies

100kEV 250kEV 500kEV 1000kEV−400

−200

0

200

400

600

800

1000

1200

1400

1600

GW

h

CoalGas TurbinesCombined CyclesHydroPump HydroUnsched. HydroWind

Figure: Difference to 0EV scenario in annual productions



Results




Results


Scenario name DumbCharge SmartCharge SmartCharge+Generation

Number of EV 1,000,000 1,000,000 1,000,000Charge of EV predefined result of result of

schedule optimisation optimisationGeneration of EV - - result of

optimisation



Results


charging behaviour



Results


charging behaviour

2 4 6 8 10 12 14 16 18 20 22 240

100

200

300

400

500

600

700

Hours

Win

d cu

rtai

lmen

t [M

W]

Dumb ChargeSmart ChargeSmart Charge+Gen.

(a) Weekday

2 4 6 8 10 12 14 16 18 20 22 240

100

200

300

400

500

600

700

Hours

Cha

rge

and

gene

ratio

n pr

ofile

EV

[MW

]

Charge: Dumb ChargeCharge: Smart ChargeCharge: Smart Charge+Gen.

(b) Weekend

Figure: Wind curtailment profile for different charging scenarios for (a) and(b)Instituto de Investigacion TecnologicaEscuela Tecnica Superior de Ingenierıa - ICAIUniversidad Pontificia Comillas


Results

Charge and generation profile under different

charging behaviour



Results

Charge and generation profile under different

charging behaviour

2 4 6 8 10 12 14 16 18 20 22 240

500

1000

1500

2000

2500

3000

3500

4000

Hours

Cha

rge

and

gene

ratio

n pr

ofile

EV

[MW

]

Charge: Dumb ChargeCharge: Smart ChargeCharge: Smart Charge+Gen.Generation: Smart Charge+Gen.

(a) Weekday

2 4 6 8 10 12 14 16 18 20 22 240

500

1000

1500

2000

2500

3000

3500

4000

Hours

Cha

rge

and

gene

ratio

n pr

ofile

EV

[MW

]

Charge: Dumb ChargeCharge: Smart ChargeCharge: Smart Charge+Gen.Generation: Smart Charge+Gen.

(b) Weekend

Figure: Charge and generation profile EV (a) and (b)



4

Conclusions and Future Work

Conclusions

Conclusions



Conclusions

Conclusions

• Decreasing of wind curtailment with high numbers of EV inthe electric system.

• Increasing cost of operation due to higher demand.

• Importance of intelligent charge.



Thank you for your attention!

Questions?

Alberto Aguilera 23, E-28015 Madrid - Tel: +34 91 542 2800 - Fax: +34 91 542 3176 - http://www.iit.upcomillas.es

Introduction - tu-dresden.de · Table: Characteristics of diﬀerent Electric Vehicles penetration...

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Transcript of Introduction - tu-dresden.de · Table: Characteristics of diﬀerent Electric Vehicles penetration...