impact of high penetration of electric vehicles, heat pumps and … · piet hensel, ph.d. dublin,...
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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 [email protected] Tao Mu [email protected] Denis Bekasow [email protected]
Tel.: +49 (0)211 601273 00
RZVN Wehr GmbH Wiesenstr. 21 40549 Düsseldorf, Germany
Any Questions?
www.rzvn.de
Thank you!