Uncoordinated and price incentive based load profiles due to electric vehicle charging

This research aims to build data-based, statistical model that can develop the load profile generated by uncoordinated charging of electric vehicles in the grid. The model development was accomplished by carrying out a literature review on electric vehicle technologies, electric vehicle utilization habits, and the trend in the rise of the number of electric vehicles sold in the automobile market. The load profile generated is intended for use in the production cost modeling of the electric system in order to learn about the possible threats and opportunities that could be caused by electrification of the transportation system. The model was implemented in MATLAB using Monte Carlo simulation, which uses random sampling of charging parameters like an initial state of charge, the final state of charge, charging types and start time of charging, for the number of electric vehicle charging events coupled in the grid. These charging parameters are modeled using statistical probability density functions based upon data collected from a fleet of actual electric vehicles. Production cost modeling was carried out using the software PSO which uses the CPLEX to solve its optimization problem. The optimization problem was formulated as the mixed-integer linear program considering the DC power flow model with an objective to minimize production costs subject to constraints. The test system used for the production cost modeling was the NAU 5-bus system which is a modified form of PJM 5-bus system. Production cost modelling tests were carried out for three different days. A time of use rate which incentivizes EV owners to charge at non-peak hours (though otherwise uncoordinated) led to a decrease in the operating cost of the system when compared to uncoordinated charging behaviors on all the days considered for the test