1. MC-based Risk Analysis on the Capacity of Distribution Grids to Charge PEVs on 3-ph 0.4-kV Distribution Grids Considering Time and Location Uncertainties
Sven Bohn, Daniel Beyer, Robert Feustel, Michael Agsten
Fraunhofer IOSB-AST Power Systems Research Group
Am Vogelherd Ilmenau European Union / Germany
Company
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2. Fraunhofer IOSB Director Prof. Dr.-Ing. habil. Jürgen Beyerer
Lemgo
Director Prof. Dr.-Ing. habil. Jürgen Beyerer
Operational costs in Mio €
Permanent employees 442
of which scientists and engineers 327
Graduate assistants 175
Ilmenau
The IOSB is connected to Karlsruhe Institute of Technology KIT
Department of Computer Sciences, Institute for Anthropomatics, Vision and Fusion Laboratory
Beijing
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3. Fraunhofer IOSB Advanced Systems Technology Branch
Director Prof. Dr.-Ing. habil. Thomas Rauschenbach
Deputies Dr.-Ing. Peter Bretschneider Prof. Dr.-Ing. habil. Jürgen Wernstedt
Focus
Energy, water and mobile systems related consulting of enterprises, governmental and non-governmental organizations
Science transfer, applied research and development projects
International activities, networking and scientific cooperation
What’s behind systems technology – an example – a view on the German power system?
Multi-Modal Energy Systems
Electricity Grid Capacity and Quality
Private and Industrial Demand and Behavior
Business Models and Business related Interactions
Time Scale Dependent Electric System Behavior
Fluctuating Generation and Demand
Liberalized Energy Markets and Legal Frameworks
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4. Introduction Overview Project Managed Charging 3.0
Project Objectives
Objective TSO: Negative Secondary Control Power
Objective DSO: Integration of one-phase charging electric vehicles into the tri-phase system (EU)
Architecture
TSO
Smart Charging Control System
DSO 4 1 2 3 1
Negative Secondary Control Power Request (TSO)
Distribution Grid Capacity (DSO)
Up to 7,2 kVA additional load 4
Smart Charging Control Systems contains:
4.1 Aggregator
4.2 EV Pool
4.3 Intelligent EVSE EV 2 3
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5. DSO Typical Grid Layouts in EU / Germany
0.4-kV 3ph Urban Distribution Grid
270m
250m
0.4-kV 3ph Rural Distribution Grid
400m
700m
Legend
Voltage
Loading Transformers
Loading Cables/Lines
KPI
Urban Grid
Rural Grid
# Households
176 10
Peak Demand [single household]
2.52 KW cosphi=0.95
7.5 kW cosphi=0.95
Transformator Loading
436 kVA / ~70%
172 kVA / ~70%
Topology
Meshed
Radial
Balanced Condition V2/V1 0%
Longest distance between transformer and load
~460m
~800m
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6. DSO Possible Charging Technologies
Grid unfriendly Worst case
Grid friendly L1 L2 L3
Alternating chosen phase
Optimal solutions for network disturbances when 1- and 2-Phase loading are available
Must be ensured by the electrical installation and/or by infrastructure functions (e.g. coordination of charging planes)
Worst case
All PEV‘s are the same phase
Follows conventional regulations
2-Phase
16A 8A
5.33A
AC/DC
5.33A AC
Best Case
PEV represents a symmetric load
Each phase will be loaded with 1/3 of the total load
1-Phase
3-Phase N
Loading Type
TR: 20/ 0.4 kV
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7. DSO Typical Charging Use Cases and Solutions
UC 1: Charging of dispersed fleets
UC 2: Charging of aggregated fleets
UC 1 Solution: Active Management PLM
UC 2 Solution: Active Management LLM
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8. DSO Open Question: “What is the real grid capacity for EVs?”
Definition of Electric Grid Capacity
Grid Capacity Limiting KPIs [Time scales]
Steady state
Transients and Dynamics
Subtransients
µs..ms
s...min
min…h
Slow voltage changes
Flicker/Voltage Dips
Voltage level
Thermal rating L/C/T under n. cond.
Thermal rating lines/cables/transformers under failure conditions
Protection
Fast voltage control
Changes of generation
Harmonics
Changes of demand
KPI
Value
Max. voltage level
+10% [EN50160]
Min. voltage level
-10% [EN50160]
Max. unbalance V2/V1 [neg. seq./pos. seq]
<=2% [EN50160]
Thermal rating transformers
typical between 60% … 100%
Thermal rating cables
Power quality
ref. EN50160
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9. DSO Open Question: “What are the uncertainties in EV grid integration”
Which car a customer buys in near or far future? Examples from the market:
Car
Charging time
Charging Technology
Tesla Models 1
up to 8h
11 up to 22 kVA, tri-phase AC
Renault Zoe
between 1 and 2h
BMWi3
up to 4h
3,6 up to 7,2 kVA, one-phase AC
Where are the charging spots located over time in near or far future? Which spots are used simultaneously?
Influenced only by the customers!
On which one of the three phases are charging one-phase AC EVs? Can be influenced by the customers, installer (and the DSO)!
L2N
Dispersed
Concentrated
L1N
L2N
L3N
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10. DSO Challenge Customer behavior Application to the grid
Possible Scenarios
EVs charge simultaneously 1ph
9.17E105
EVs charge simultaneously 3ph
9.57E52
It is impossible for a grid planner to calculate enough representative scenarios to determine the grid capacity for EVs, and the right decisions
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11. DSO Solution MC based risk analysis
MC based grid capacity estimator
N0%
N100%
no actions required
actions depending on the DSOs risk strategy
DSO must
act
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12. DSO Example Analysis Scenario Description
0.4-kV 3ph Urban Distribution Grid
0.4-kV 3ph Rural Distribution Grid
Criteria
Value
Analyse Area
Whole grid
Loading
Peak
Max. PEVs per Node 2
Charging Technology
4,6 kVA1ph cosphi=0,95
Max. Cable Loading
80%
Max. Transformer Loading
100%
Max. voltage deviatation
±10%
Max. unbalance 2%
KPI
Urban Grid
Rural Grid
# EV N0% 3
# EV N30% 6
# EV N100% 10 1
# Cable Loading
failed in 698 / 1000 simulated scenarios
failed in 219 / 1000 simulated scenarios
# Transformer Loading
failed in 370 / 1000 simulated scenarios
failed in 113 / 1000 simulated scenarios
# max. voltage deviatation
failed in 343 / 1000 simulated scenarios
failed in 999 / 1000 simulated scenarios
# Unbalance
failed in 12 / 1000 simulated scenarios
failed in 114 / 1000 simulated scenarios
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13. DSO Conclusion EVs in the EU charge at a AC tri-phase system
EVs are in the EU available as AC tri-phase or one-phase loads
EVs are a volatile load over space, grid phases and time
Planning the grid of tomorrow with EVs becomes very complex
The number of possible development paths in a single distribution grid is extremely high
It is impossible for a grid planner to calculate enough representative scenarios to
determine the grid capacity for EVs,
and the right decisions
With a MC based approach a small sample of all possible scenarios can be chosen to estimate the grid capacity for EVs
Benefits for DSOs
Estimate the capacity for every single Low Voltage Distribution Grid (e.g. only in Germany round about  Big Data Problem)
Classify critical and uncritical distribution grids
Actually the best available input for DSOs strategy
An available evaluator of the grid capacity for EVs (…and naturally PV, CHP, Storages, Voltage Controlled Transformers, etc.)
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14. Acknowledgments
The work done by S. Bohn and M. Agsten is partly funded by the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB) under the program “Erneuerbar Mobil”, grand no. 16EM1074.
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15. Contact Information
Thank you for your attention
Dr.-Ing. Michael Agsten
Head of the Power Systems Research Group
Energy Department
Fraunhofer Advanced Systems Technology Branch (IOSB-AST)
Am Vogelherd 50
98693 Ilmenau
European Union / Germany
Phone +49 (0)
Mail
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