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Electric Vehicle Grid Simulator

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.

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Electric Vehicles Overview•
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IMAGE | GREENTECHMEDIA ELECTRIC VEHICLE GRID SIMULATOR WRI Electric Mobility | April 2, 2020
AGENDA  Current model inputs and outputs  Case studies  Future model improvements
EV GRID SIMULATOR
SIMULATOR INPUTS  Setting: Multiple use cases (commercial, industrial, residential)  Level of analysis: Distribution transformer  Time interval: 15-minute intervals over 24 hours
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
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
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
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?
CASE STUDY: FREIGHT DEPOT Unmanaged Charging Managed Charging Number of vehicles: 150 Max capacity: 2400 kW
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
CASE STUDY: SUZHOU, CHINA 0 1000 2000 3000 4000 5000 6000 0:00 1:15 2:30 3:45 5:00 6:15 7:30 8:45 10:00 11:15 12:30 13:45 15:00 16:15 17:30 18:45 20:00 21:15 22:30 23:45 kW 33%EV penetration 66%EV penetration 100%EV penetration Base load Distribution capacity 80% of Distribution capacity 0 500 1000 1500 2000 2500 3000 3500 4000 4500 00:00 01:15 02:30 03:45 05:00 06:15 07:30 08:45 10:00 11:15 12:30 13:45 15:00 16:15 17:30 18:45 20:00 21:15 22:30 23:45 kW 33%EV penetration 66%EV penetration 100%EV penetration Base load Distribution capacity 80% of Distribution capacity 0 1000 2000 3000 4000 5000 6000 7000 8000 0:00 1:15 2:30 3:45 5:00 6:15 7:30 8:45 10:00 11:15 12:30 13:45 15:00 16:15 17:30 18:45 20:00 21:15 22:30 23:45 kW 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
EV GRID SIMULATOR