1. Examining Power Grids Response to PHEVs Charging Demand
Student(s): Zahra Darabi, Electrical and Computer Eng.
Faculty Advisor: Dr. Mehdi Ferdowsi, Electrical and Computer Eng.
Motivations
Statistical Study- Driving pattern
Case Study
A future grid should be able to cope with the extra demand due to PHEV charging.
Two solutions for determining driving patterns data (plugging-in time, miles driven, and vehicle type):
Extracting Data from transportation surveys
Making assumptions
Case study 1: ECAR region
National Household Travel Survey (NHTS) 2001: comprehensive reference for transportation data
Number of vehicle: 27.7 million
Max. generation: 90 GW
Charging levels: 1.4, 7.68 kW
Number of vehicle: 25.8 million
Max. generation: 40 GW
A higher charging level causes larger deformation
CNV has larger deformation than ECAR
Daily mileage
The most common mileage is in the range of miles.
55% of vehicles are driven 30 miles daily or less.
Background
Uncertainty regarding driving patterns makes the researchers generate probability distribution functions (PDFs)
Previous studies
compared total load profiles with and without PHEVs
presented valley filling approaches
defined power grid potential: Difference between the highest demand during the off-peak and the maximum generation capacity
allowed PHEVs to be plugged in and then measurement of how much the power system constraints were violated
Case study 2: CNV region
Arriving time
Arriving time of vehicles gives some guide for plugging-in time.
Approach
Power grid is a collection of outlets.
Arriving vehicles make a virtual queue
This approach first sets the red lines, and then allows the PHEVs to be charged as long as the constraints are not violated.
Vehicle type
The first four types of vehicles are selected for this study.
Type 1 is the most common vehicle.
Conclusion
This study:
set the power system’s limitation so that the total load profile never surpasses the power grid’s limits during the examination.
considered the customers’ behaviors for charging requests.
indicated that the power system’s ability to supply the charging demand is different for each region.
determined driving patterns PDFs to permit their use in studies related to stochastic analyses.
Charging level & battery capacity
Three charging levels based on two references
Battery Capacities of four types of PHEVs.
Acknowledgment
This work has been partially supported by the Intelligent Systems Center at the Missouri University of Science and Technology