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 25-30 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