Travis Newbolt

The transportation infrastructures of the future require a link between vehicle mobility and electric power distribution grids to better sustain electric vehicle (EV) charging access. As new charging technologies, such as dynamic wireless power transfer (DWPT) networks, become more common, the impacts that they pose on load demands need to be thoroughly analyzed to prevent instability in grids. This article presents a novel approach, which is computationally efficient, to simulate large-scale DWPT networks through microscopic simulations (MSs) that incorporate historical traffic flow (TF) data, ten different vehicle models, car-following models, and lane-changing models to better mimic real-world driving conditions. The proposed MS approach considers an EV battery state model that utilizes a modified Toeplitz convolution to develop the charging and discharging profiles. Twenty-two different cases are analyzed that fluctuate EV penetration rates, length of the charging system, preprocessing lane determination ratios, weighted probability lane-changing decision behavior, and number of lanes for latitudinal traversals. A comprehensive comparative analysis for the average hourly load demands of each case is performed to demonstrate the impact on charging load demands produced by large-scale DWPT networks due to vehicle behavior and transportation infrastructure.

As electric vehicles (EVs) become increasingly common in transportation infrastructures, the need to strengthen and diversify the EV charging systems becomes more necessary. DynamicWireless Power Transfer (DWPT) roadways allow EVs to be recharged while in-motion, thus allowing to improve the driving ranges and facilitating the widespread adoption of EVs. One major challenge to adopt large-scale DWPT networks is to efficiently and accurately develop load demand models to comprehend the complex behavior on power distribution grid due to difficulty in developing power electronic simulations for charging systems consisting of either numerous transmitter pads or high traffic volumes. This paper proposes a novel modified Toeplitz convolution method for efficient large-scale DWPT load demand modeling. The proposed method achieves more accurate modeling of DWPT systems from a few transmitter pads to tens of miles in real-world traffic scenarios with light computational load. Test results for a small-scaleDWPT system are first generated to validate the accuracy of the proposed method before scaling to large-scale load demand modeling where real-world traffic flow data is utilized in DWPT networks ranging from 2−10 miles. A comparative analysis is further performed for the scenarios under consideration to demonstrate the efficiency and accuracy of the proposed method.

Numerous articles have been written regarding the potential affects that electric vehicle (EV) charging stations can have on power distribution systems. With the advent to adopt a dynamic wireless power transfer (DWPT) roadway charging infrastructure, there lies a potential for hazardous effects to occur. Therefore, this paper presents a novel methodology to analyze the sustainability and the potential effects that a DWPT roadway can have on electric power distribution grid. Historical traffic flow (HTF) data is converted using a Monte Carlo method to develop an EV traffic flow (EV-TF). Then, a full DWPT simulation of a 1-mile and 10-mile roadway is performed to determine the power fluctuations that can occur as different densities of EVs traverse the roadway. This method creates simulations using an IEEE 33-Bus power distribution system to evaluate if a DWPT roadway for in-motion EV charging causes voltage magnitudes to fluctuate beyond operational standards.