Savings and Peak Reduction Due to Optimally-Timed Charging of Electric Vehicles on the Oahu Power System
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2015-12
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University of Hawaii at Manoa
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“EVs” which includes both the plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) have been widely recognized as an important step towards electrification of the passenger vehicle transportation system. Combined with their integration with intermittent renewable energy resources, they present a promising solution to achieve the greenhouse gas (GHG) emissions goal of limiting the average global temperature to 2° C. With its unique geography and current fossil fuel based energy infrastructure combined with its aggressive renewable energy goals, Hawaii forms an ideal site for large scale adoption of EVs in the future. The research study presented here develops an in-depth Hawaii specific EV model which is then integrated with the Oahu power system to study the effects of large scale EV integration into the grid, and also intends to provide a better understanding as to how different optimally-timed EV charging strategies can benefit such a unique power system. The EV model developed in this study uses actual driving pattern data collected from the 2009 National Household Travel Survey (NHTS) to replicate the travel pattern behavior in Hawaii. Assuming this nationally representative sample of daily vehicle usage, to provide a realistic idea of how EVs might be used in Hawaii, realistic charging profiles for individual vehicles were generated. Using the driving pattern distributions, potential EV charging windows/timeslots were calculated by determining the maximum possible time a vehicle is parked in a potential charging location (i.e. home and workplace). These potential charging timeslots provide the EV owner the most comprehensive and realistic charging options available, as each option is unique to that particular vehicle and is derived from thorough analysis of its driving pattern behavior. Rather than assuming all the vehicles to have a fixed number of miles as the daily distance driven by the vehicles, each vehicle is modelled individually, which helps in achieving a more accurate and realistic estimation of the amount of energy needed to charge each individual EV. Half-hourly EV charging electricity demand profiles were then calculated for each individual modelled vehicle by depending on different shares of charging locations (workplace/home charging) and different rates of charging (1.4kW/3.3kW/6.6kW). Two different optimized smart recharging scenarios were then implemented and their effect on system load, and power system costs were then compared with a business-as-usual (BAU) charging scenario. This research study could help researchers and policymakers to develop an optimal plan for power system expansion and operation, considering large scale adoption of EVs, and show how to develop better time-of-use electricity pricing schemes to incentivize EV owners in order to obtain a smarter and more efficient grid. Analyzing simulation results, we find that smart recharging strategies were successfully able to mitigate the amount of power drawn during the peak periods of the day, and also provide savings to the EV owners by reducing the EV charging costs by 8-35% compared to the business-as-usual (BAU) charging scenario.
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Hawaii--Oahu
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Theses for the degree of Master of Science (University of Hawaii at Manoa). Electrical Engineering
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