Impact of PEV Charging Loads on Distribution System Operations and Optimal Siting and Sizing of PEV Charging Stations
Smart grid has emerged as a promising paradigm to promote and deliver a clean, modern and efficient electricity grid to all customers, and it allows Local Distribution Companies (LDC) to integrate renewable sources more reliably, efficiently, safely and economically. Smart grid realizes Plug-in Electric Vehicles (PEVs) as a potential solution to reduce green house gas (GHG) emissions. However, large scale penetration of PEVs can significantly impact distribution system operations.
This thesis first presents an extensive study of PEV characteristics such as, owner driving behavior, mobility trends of the system as a whole, battery capacity, State of Charge (SOC), different charging levels and energy required for charging the battery. The US National Household Travel Survey (NHTS) 2009 data set is explored to model the PEV load characteristics by representing customers' charging behavior in close to reality. This includes the study of the number of trips covered each day, during weekdays and weekends, over different seasons, the miles traveled, and the home arrival and departure times. Using the developed PEV load profiles, distribution system impact analysis and optimal operational studies are carried out to examine how the LDC can accommodate such loads.
The NHTS data set is also used to develop probability density functions (pdfs) of certain mobility patterns such as initial SOC and starting time of charging. Using these pdfs, a Stochastic Distribution Optimal Power Flow (SDOPF) model with various objectives such as minimization of feeder loss, minimization of energy drawn and minimization of PEV charging cost, subject to feeder operational constraints is presented. Various scenarios of uncontrolled and smart charging are studied. In the uncontrolled charging case, the worst case scenarios are discussed. The smart charging scenarios provides with the optimal charging schedules which result in flattening the load profile.
This thesis further presents an approach to optimally siting and sizing of Electric Vehicle Charging Stations (EVCS). Various aspects in identifying the optimal location of EVCS, from both the LDC's and customers' perspectives are discussed. A new approach to modeling the initial SOC of PEVs considering the travel distance from home to EVCS in relation to the feeder sections' electrical parameters is presented. A heuristic approach to determine the optimal siting and sizing of EVCS considering minimum feeder loss, peak demand and customer charging cost is proposed.