Smart grid concepts in transportation József Ladányi Dr. Senior lecturer

Villamos Energetika Tanszék

Villamos Művek és Környezet Csoport

• Smart concepts in transportation

„Smart transportation” in world-wide

Who deals with smart transportation? IBM, Cisco, MIT,…BME, ….etc

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• Smart concepts in transportation

Smart concepts in railways Our R&D project:  identification of key areas regards to energy saving  optimize the energy consumption with smart intelligent solutions (on board of the locomotive, switch point

heating control, energy management system, public lighting)

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Smart concepts in e-mobility

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Smart grid concepts in transportation – E-mobility Csaba Farkas PhD student

Department Of Electric Power Engineering

Power Systems & Environment Group

Enumeration • E-mobility in general • Impact assessment of electric car charging on the grid • Computer simulations regarding the low-voltage grid • Stochastic modeling of electric car charging stations: queuing theory and Petri-nets • Vehicle-to-grid (V2G) effects on frequency control • Current projects, future plans

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Introduction: distribution, production and use of electricity in 20??

Source: Joao Pecas Lopes: The MERGE control concept - Microgrids and EVs - EES-UETP Course, DTU, Lyngby, Copenhagen

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Impact assessment of electric car charging on the low-voltage grid

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Electric car charging • Two options for EV charging: • Slow charging at home or at specially erected charging points

(including delayed charging): 6-8 hours • Fast charging at charging stations or battery switching stations: 520 mins • Real topology, real measurement data used • Loading of the grid simulated in DIgSILENT Power Factory using

load-flow calculations conducted in 15mins resolution

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Effect on the LV network – Investigated grid

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Effect on the LV network – Load profile Load profile (15 min averages) 1,8 1,6 1,4

Power [kW]

1,2 1 0,8 0,6 0,4 0,2

0:00 0:45 1:30 2:15 3:00 3:45 4:30 5:15 6:00 6:45 7:30 8:15 9:00 9:45 10:30 11:15 12:00 12:45 13:30 14:15 15:00 15:45 16:30 17:15 18:00 18:45 19:30 20:15 21:00 21:45 22:30 23:15 0:00

0

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19:59:36 20:10:00 20:20:00 20:30:02 20:40:02 20:50:00 21:00:00 21:10:00 21:20:00 21:30:02 21:40:02 21:50:02 22:00:02 22:10:02 22:20:00 22:30:02 22:40:02 22:50:02 23:00:02 23:10:00 23:20:00 23:30:00 23:40:00 23:50:00 0:00:00 0:10:00 0:20:00 0:30:00 0:40:00 0:50:00 1:00:00 1:10:00 1:20:00 1:30:00 1:40:00 1:50:00 2:00:00

Power [kW]

Effect on the LV network – Power consumption of a single car Charging power

2,5

2

1,5

1

0,5

0

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Effects on the LV network Individual driving distance • No smart charging strategy exists yet,distribution so the Availability of cars during the daydumb and a Percentage of cars [%] Percentage of available cars [%]

35 delayed charging type is investigated here 100 3099 distribution of the arrival time has to be set • The 98 • This can be based on statistical data 25 97

2096 95 15 94 10 93 592 91 0 90 1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Driving [km] Hourdistance of the day Source: EDISON Work Project 2.2., Potential analysis for electric vehicle grid integration

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Effect on the LV network – Interarrival time distribution (example) Interarrival distribution 35

30

Number of arrived cars

25

20

15

10

5

0 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

Charging group no.

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Effect on the LV network – Simulation results 160

Loading of the MV/LV transformer at different EV 100% penetration, Remaining voltage at the end of a line, EV 6h charging penetration 1

Loading [%] Remaining voltage [p.u]

140 0,99 0,98 120

0,97 100

0% autó

0,96 80

Alapeset (0% autó)

0,95 60 0,94

autó 6 órás töltés60% (eltolás nélkül)

40 0,93

20% autó

8 órás töltés

autó 6 órás töltés80% (22:00-ra) 6 órás töltés100% (23:00-ra) autó

20 0,92 0:00 0:45 0:00 1:30 1:00 2:15 2:00 3:00 3:00 3:45 4:00 4:30 5:00 5:15 6:00 6:00 6:45 7:00 7:30 8:00 8:15 9:00 9:00 10:00 9:45 11:00 10:30 12:00 11:15 13:00 12:00 12:45 14:00 13:30 15:00 14:15 16:00 15:00 17:00 15:45 18:00 16:30 19:00 17:15 20:00 18:00 18:45 21:00 19:30 22:00 20:15 23:00 21:00 0:00 21:45 22:30 23:15 0:00

0 0,91

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Stochastic modeling of charging stations

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Stochastic modeling of fast charging stations – Investigated grid

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Stochastic modeling of fast charging stations – Battery chargers • Two types of battery chargers were used: • Direct fast charging • A 50kWh battery should be fully recharged in 15mins→200kW charging

capacity is needed (it is not too realistic though) • Battery switching • A 50kWh battery should be fully recharged in 60mins →50kW charging

power is needed (commercially available chargers are capable of it – though they are not designed for battery switching stations)

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Stochastic modeling of fast charging stations – Queuing theory • Electric car arrival and service process is modeled as an

M/M/c/n queuing model:

Flow diagram

• Governing equations: • Average waiting time:  0    c c N c N c   E Lq    1    N  c    1    2 c!1    • Average queue length (using Little’s law):



E Wq   22.11.2013



E Lq 

 1   N 

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Stochastic modeling of fast charging stations – Queuing theory • where • Stationary distributions: 1 0  i j c N 1  1 1              j c c!    i  0 i!    j  c 1 c • λ: arrival intensity [1/min]

• μ: charging intensity [1/min] • c: the number of chargers • N: the capacity of the charging station (parking slots+charging

slots)

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Stochastic modeling of battery switching stations - The required number of chargers

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Stochastic modeling of fast charging stations – Petri-nets • A Petri net is a directed bipartite graph, it consist of

places, transitions and arcs

• Places in a Petri net may contain a discrete number of

marks called tokens. Any distribution of tokens over the places will represent a configuration of the net called a marking. • A transition of a Petri net may fire if it is enabled (logical or timing conditions); when the transition fires, it consumes the required input tokens, and creates tokens in its output places • In a standard Petri net the tokens are indistinguishable. In a coloured Petri net, every token has a value→different types of EVs can be modelled 22.11.2013

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Stochastic modeling of fast charging stations – Petri-nets, example result Actual number of operating chargers for different number of available chargers (∑300 cars)

Number of operating chargers

16

14 12 10 14 fej 10 fej

8

6 fej

6

5 fej 4 fej

4 2

0:00 0:45 1:30 2:15 3:00 3:45 4:30 5:15 6:00 6:45 7:30 8:15 9:00 9:45 10:30 11:15 12:00 12:45 13:30 14:15 15:00 15:45 16:30 17:15 18:00 18:45 19:30 20:15 21:00 21:45 22:30 23:15

0

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Stochastic modeling of battery switching stations • The aim is to have enough battery in stock so that the

service would be continuous • M/M/c/n model – similar to the one previously used Cars arriving to charge

λ

Waiting and charging (with c chargers)

μ

Battery stock

Charged batteries

• The stochastic model: So the probability that an incoming need cannot be served is less than a predefined δ value. N’ is the battery stock required for this. 22.11.2013

c   c   N  c   '

 N' 

i

1       i  0 i!    c

N'

c

j  c 1

c! 1

j c



1     c!   

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j



24

Stochastic modeling of battery switching stations The required battery stock (including the batteries under charging) as a function of the number of chargers Number of battery stock

80 70 60 50 40 30 20 10 0 8

9

10

11

12

13

14

15

16

17

18

19

20

Number of chargers

• A number of 14 chargers is appropriate (10 was used in the simulations) 22.11.2013

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158,74

Loading of the secondary transformer Loading of the HV/MV transformer Max. remaining voltage at the MV line DIgSILENT

87,50 98,00

DIgSILENT

Stochastic modeling of fast charging and battery switching stations – Simulation results 75,00 158,735 97,00

Loading [%]

158,73

Cable (household), battery switch

62,50 158,725 96,00 158,72

Cable (transformer), battery switch

50,00 95,00

158,715

158,71

37,50 94,00

158,705

0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 0:00

158,725,00

-0,0000 19,200 38,400 93,00 -0,0000 120/20kV: 19,200 Loading in % 38,400 C3\BB: Voltage, Magnitude in %

57,600 57,600

Time [in 15mins]

76,800 [-] 96,000 76,800 [-] 96,000 Transzformátor terhelõdés

Date: 2/4/2013

Annex: /3 Feszültségesés35512 Date: 2/4/2013

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Annex: /9

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Aggregation and frequency control

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Electric car aggregation

Source: Sergio Vazquez, Srdjan M. Lukic, Eduardo Galvan, Leopoldo G. Franquelo, Juan M. Carrasco – Energy storage systems for transport and grid applications, IEEE Transactions on Industrial Electronics, vol.57. no. 12. December 2010.

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Aggregated electric cars used for frequency control purposes – Investigated grid

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Aggregated electric cars used for frequency control purposes – Isochronous turbine governor

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Aggregated electric cars used for frequency control purposes – EV power controller block diagram

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50,00 50,40 2,50

DIgSILENT

DIgSILENT

50,40 50,70 3,75

DIgSILENT

Aggregated electric cars used for frequency control purposes Insufficient With electricgenerator cars regulating capacity Electric cars’ power during regulation

Islanding

49,60 1,25 50,10

0,00 49,20 49,80 -1,25 48,80 49,50 -2,50 48,40 -0,1000 59,918 119,94 49,20 -0,1000 59,918 119,94 Electrical Frequency in Hz -3,75 MV busbar: -0,1000 MV busbar: 59,918 119,94 Electrical Frequency in Hz EVs: Total Active Power in MW

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179,96 179,95 179,96

2nd Green Waves - Autumn Academy - Renewables & Smart Cities, A

239,97 [s] 299,99 239,97 [s] 299,99 239,97 [s] 299,99 MV bus frequency Date: 10/11/2012 /2 Date: 10/11/2012 MV bus frequencyAnnex: Annex: /2 EV power Date: 10/11/2012 Magyar Tudomány Ünnepe 32 Annex: /5

Aggregated electric cars used for frequency control purposes MV bus frequency for different controllers 51

Frequency [Hz]

50,5

Without cars_without relays

50

49,5

Without cars_with relays

49 48,5

With cars_without relays

48

With cars_with relays -0,1 10,6 21,3 32,1 42,8 53,5 64,2 74,9 85,7 96,4 107,1 117,8 128,5 139,2 150,0 160,7 171,4 182,1 192,8 203,6 214,3 225,0 235,7 246,4 257,2 267,9 278,6 289,3

47,5

Time [s] 22.11.2013

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Aggregated electric cars used for frequency control purposes • The aggregation of ~700 cars can improve the dynamics

of frequency regulation:

• The frequency drop was smaller • Oscillations are better damped • Less under-frequency relays should be operated

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…end of briefing

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Current projects, future plans • Investigations on interaction of electric car chargers with

each other and also with other DGs • Proper battery model in DIgSILENT→state of charge can be monitored • Stochastic modeling of the state of charge in a fleet of electric cars based on statistical data and probability theory • Investigations on voltage control capabilities on the LV grid

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Thank you for your attention!

Source: Brian McBeth – Mercedes Benz E-mobility concept - EES-UETP Course, DTU, Lyngby, Copenhagen, 2010.

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