The document discusses the Electric Vehicle (EV) Grid Simulator, which analyzes various scenarios regarding the impact of EVs on distribution transformers across multiple use cases. It highlights inputs like vehicle energy efficiency and peak load characteristics, and outputs insights on optimized charging strategies. Case studies, including one in Suzhou, China, demonstrate challenges and strategies for managing EV electrification and charging demand.

1. IMAGE | GREENTECHMEDIA
ELECTRIC VEHICLE GRID SIMULATOR
WRI Electric Mobility | April 2, 2020

2. AGENDA
 Current model inputs and outputs
 Case studies
 Future model improvements

3. EV GRID SIMULATOR

4. SIMULATOR INPUTS
 Setting: Multiple use cases (commercial, industrial, residential)
 Level of analysis: Distribution transformer
 Time interval: 15-minute intervals over 24 hours

5. SIMULATOR INPUTS
 Vehicle energy efficiency (kWh/100km)
 Number of EVs
 Daily mileage*
 Arrival time
 Number of EVs participating in managed
charging
 Parking duration
 EV quantity to participate in V2G
 Base load
 Rated capacity
 Utility rate
 Number of chargers
 Charging power (kW)
 Charging frequency*
 Charging starting SOC
 Charging ending SOC*
 Number of smart chargers*
 Charging power: upper/lower limit
 Discharging power: upper/lower limit
* Indicates input is preferred but optional

6. SIMULATOR OUTPUTS
Distribution transformer impact
 Increases in peak load
 Increased capacity required
 Time of peak load
 Peak-valley difference
EV load characteristics
 Max EV load
 EV ratio in peak load
 EV load over time
 Utilization factor

7. MODEL INSIGHTS
 Optimized EV charging strategy
 Time-sensitive electricity cost analysis
 Electricity cost and demand charge mitigation
 Determine VGI potential based on vehicle and infrastructure
specifications.
 Energy potential of parked vehicles
 Viability of various VGI applications

8. CASE STUDY: FREIGHT DEPOT
 Hypothetical scenario at commercial freight facility.
 Developed to observe a large quantity of vehicles returning to
charge at a single location.
 Key questions:
 Will distribution transformer be overloaded as electrification increases?
 How will managed charging mitigate overloading and potentially avoid
demand charges?

9. CASE STUDY: FREIGHT DEPOT
Unmanaged Charging Managed Charging
Number of vehicles: 150
Max capacity: 2400 kW

10. CASE STUDY: SUZHOU, CHINA
 City of Suzhou is looking to re-align their energy system
to accommodate future growth and test new concepts
 Second largest city in China by electricity consumption
 One of the wealthiest cities in China, witnessing rapid EV growth

11. CASE STUDY: SUZHOU, CHINA
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33%EV penetration 66%EV penetration
100%EV penetration Base load
Distribution capacity 80% of Distribution capacity
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33%EV penetration 66%EV penetration
100%EV penetration Base load
Distribution capacity 80% of Distribution capacity
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33%EV penetration 66%EV penetration
100%EV penetration Base load
Distribution capacity 80% of Distribution capacity
Case 1: Office Case 2: Commercial Complex Case 3: Residential Neighborhood
Suzhou modelling results indicate that residential distribution feeders
are most vulnerable to (or potentially hinder) large EV deployment

12. EV GRID SIMULATOR