RZVN Wehr GmbH

An analysis of a German case

Piet Hensel, Ph.D.

Dublin, 14.10.2019

Impact of high penetration of electric vehicles, heat pumps and photovoltaic

generation on distribution grids

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Contents

Introduction

Modelling the electric demand of EVs

Modelling the electric demand of HPs

Modelling the generation of PVs

Results and Discussion

Conclusion and outlook

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RZVN Wehr GmbH

Consulting and software development for grid planning and optimization

Location: Düsseldorf, Germany

Founded 1961

Employees: 21

Customers: > 300

Company profile

Electricity

Water

Gas

District Heating

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Software

CITYCOCKPIT City- wide energy concepts and sector coupling

RIKA Asset simulation

ROKA3 Hydraulic (/electric) simulation

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1. Introduction Trends in energy demand and supply

Thermal insulation

• Micro- CHP • Heat- pump

PV + Solar thermal

„Prosumer“

E- Mobility

Source: ©KfW

Wind- Power

Battery

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1. Introduction Challenges to electrical utilities (numbers for Germany)

Electrification of the transportation sector EV • 2025, 1.7- 3.1 million (4- 6.5 %) • 2030, 4.2- 7 million (10- 15%)

2030 climate protection target HP • 5- 6 million

CO2- free energy source PV • cost • efficiency

Source: German National Platform for Electric Mobility, Progress Report 2018 – Market ramp- up phase,” Berlin, May 2018.

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2. Modelling the electric demand of EVs Normal charging stations - > “Area principle”

subgrid

SCP (Subgrid charging profile)

LCP (Local charging profile)

VCS n VCS 2 VCS 1 …

ICP m ICP 2 ICP 1 …

assign

11 kW

22 kW

� 33 kW

8.25 kW

8.25 kW

8.25 kW

8.25 kW

VCS: Virtual charging station

ICP: Individual charging profile

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2. Modelling the electric demand of EVs Parameter of charging profile

Parameter Distribution function Value

Start time of charging Normal distribution Expectation: 18:00 (charging at home) 9:00 (charging at work)

Driving profile Constant 50 km/d

Power consumption Constant 25 kWh/100 km

Battery capacity Uniform distribution 20 – 40 kWh 60 – 80 kWh

Charging power Random choice

3.7 kW 11 kW 22 kW 44 kW

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2. Modelling the electric demand of EVs

SCP with low simultaneity SCP with high simultaneity

Randomly generated SCPs

0

40

80

120

160

Cha

rgin

g L

oad

(kW

)

Time

11 kW 22 kW 3,7 kW 44 kW

0

40

80

120

Cha

rgin

g L

oad

(kW

)

Time

11 kW 22 kW 3,7 kW 44 kW3.7 kW 3.7 kW

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Fast charging stations

• Charging power more than 44 kW • Based on the long-term power system planning of the local utility • Connected directly to the nearest substation with the specified power

2. Modelling the electric demand of EVs

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3. Modelling the electric demand of HPs Simulating heating sector in CITYCOCKPIT software

Gas boiler

District Heating Heat Pump

Micro CHP Wood (Pellet)

Oil

Unknown

Energy cadastre

Gas Grid DH grid

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3. Modelling the electric demand of HPs Deriving annual electricity consumption from Annual Heating Demand

Typical building area

Annual heating demand

Annual electricity consumption

Annual electricity consumption considering the penetration degree

AHDi = Ai × ha

AECi = AHDi / COP

AECPi = AECi × sp

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3. Modelling the electric demand of HPs Temperature- dependent load profile

00,10,20,30,40,50,60,70,80,9

Load

T ime

-12°C -8°C -4°C 0°C 4°C 8°C 12°C

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4. Modelling the generation of PVs

• Based on a PV potential study of the utility • Considering the building architecture,

particularly the roof shape • Based on an exact solar cadastre of all

buildings • Integrated into the simulation model using

the specified GIS-ID of the connection points

0

0,2

0,4

0,6

0,8

1

00:0003:0006:0009:0012:0015:0018:0021:00

Pow

er (

p.u.

)

T ime

Typical 24- hour feed- in profiles of PV

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5. Results and Discussion Characteristic data of the distribution grid

Electric Component Amount

HV/MV Substation 3

MV/LV Substation 211 House Connection Point 9,877

Decentralized Generator 230

Lines (MV+LV) 480 km

Load Case Power from High Voltage Grid (MW)

Decentralized Generatio (MW)

Load (MW)

Heavy Load Case 30.2 1.3 31.5

Light Load Case 14.8 12.0 26.8

• The utility supplies electricity to a medium sized town in Germany. • The population is about 40,000 (~27,000 cars). • Mostly single / double family houses

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Scenarios

5. Results and Discussion

Scenario Basic load Market share of EVs (%) Charging power Charging period REP of HP (% REP of PV (%

1 Heavy Load 6

3.7 kW (73.7%) 11 kW (21.5%) 22 kW (3.5%) 44 kW (1.3%)

every day 7 0

2 Heavy Load 15

3.7 kW (29.4%) 11 kW (36.9%) 22 kW (27.4%) 44 kW (6.3%)

every day 16 0

3 Heavy Load 45

3.7 kW (29.4%) 11 kW (36.9%) 22 kW (27.4%) 44 kW (6.3%)

every three days 16 0

4 Light Load 0 - - 0 100

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5. Results and Discussion

Load curve of scenario 1 Load curve of scenario 2

Result comparison between scenario 1 and 2

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

Loa

d (k

VA

)

Time

Basic Load Load of EVs Load of HPs

0

10000

20000

30000

40000

50000

60000

Loa

d (k

VA

)

Time

Basic Load Load of EVs Load of HPs

+26.4% +74.3%

Simultaneity factor E-Mobility

0,62

Simultaneity factor E-Mobility

0,33

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Voltage

Result comparison between scenario 1 and 2

Present Scenario 1 Szenario 2

< 0.95 p.u.

< 0.90 p.u.

Voltage < 0.9 p.u.

Voltage < 0.95 p.u.

75 (0.4%) 1987 (12%)

Voltage < 0.9 p.u.

Voltage < 0.95 p.u.

999 (6%) 5542 (33%)

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Load of power lines

Result comparison between scenario 1 and 2

Present Scenario 1 Scenario 2

Load ≥ 100 %

Load ≥ 60 %

6 (0.23 km) 122 (2.2 km)

Load ≥ 100 %

Load ≥ 60 %

181 (3.1 km) 698 (13.9 km)

> 60%

> 100%

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Result comparison between scenario 1 and 2

Minimum voltage in the low- voltage grid

Maximum load of power lines in the low- voltage grid

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Result comparison between scenario 1 and 2

Scenario 1 Scenario 2

Load of Transformers

Time

Load

(%

)

T ime

Load

(%

)

Load ≥ 100 % max

0 (0%) 87.4%

Load ≥ 100 % max

17 (12%) 177%

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Result Comparison between Scenario 2 and 3

5. Results and Discussion

0

0,1

0,2

0,3

0,4

0,5

0,6

0:00

1:00

2:00

3:00

4:00

5:00

6:00

7:00

8:00

9:00

10:0

011

:00

12:0

013

:00

14:0

015

:00

16:0

017

:00

18:0

019

:00

20:0

021

:00

22:0

023

:00

Cha

rgin

g Lo

ad (k

W)

T ime

Scenario 2 Scenario 3

Scenario 2 Scenario 3 Charging period every day every three days Peak charging load (kW) 0.47 0.48 Simultaneity factor 0.33 0,23

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Scenario 4

5. Results and Discussion

Load of power lines in the low- voltage grid

Voltage in the low- voltage grid

Load ≥ 100 %

Load ≥ 60 %

10 (0.3 km) 57 (6.6 km)

Voltage > 1.1 p.u.

Voltage > 1.05 p.u.

0 14 (0.08%)

0

5

10

15

20

25

Present Scenario 4

Dec

entra

lized

gen

erat

ion

(MW

) PV: +73%

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Results • The maximum load increases by 26.4% and 74.3% in the scenario 1 and 2 respectively. • With full expansion of PV, the feed- in power of decentralized generation increases by 73%. • As the proportion of EVs and HPs rose, the risk of low voltage increases. • Area- wide voltage problem will occur in the low voltage distribution grid. • There will be no area- wide overloads of power lines and transformers. • A high penetration of PV in the low- voltage grid will not lead to overvoltage. • Simultaneity factors depened heavily on assumed charging behavior and can vary substantially

Outlook • Based on this study the measures against low voltage and overload will be investigated in the future. • Especially, a quantitative analysis of the potential of load management will be conducted. • Simulation framework will be integrated in the software package ROKA3 - CityCockpit, which will allow

the simulatenous simulation of electricity- , district- heating- and gas- grids in one system, to take full advantage of the potentials of sector coupling

6. Conclusion and outlook

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Piet Hensel hensel@rzvn.de Tao Mu mu@rzvn.de Denis Bekasow bekasow@rzvn.de

Tel.: +49 (0)211 601273 00

RZVN Wehr GmbH Wiesenstr. 21 40549 Düsseldorf, Germany

Any Questions?

www.rzvn.de

Thank you!