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This paper examines an application of a two-lane microscopic Traffic Flow (TF) simulation to comprehend the impact of the complex behavior of Dynamic Wireless Power Transfer (DWPT) charging systems onto electric power distribution grids. The proposed approach utilizes real-world data to determine a more accurate TF density at each time interval. The simulation is carried out considering all vehicles, whether electric vehicles (EVs) or non-electric, and they have a randomized lane changing behavior and fluctuating velocities following a leading car model. Three different scenarios are conducted for 5 mile, 10 mile, and 15 mile DWPT networks that are proportionally connected to an IEEE 33-bus distribution grid. Our findings indicate that EVs' average State-of-Charge (SOC) increases proportionally and significantly at each DWPT network length. Furthermore, the load demand generated from the DWPT network also increases proportionally with its length; and this increment in load demand causes adverse impacts on distribution grid voltage magnitudes exceeding operational standards that leads to equipment failure or blackout events. 

As electric vehicles (EVs) become increasingly common in transportation infrastructures, the need to strengthen and diversify the EV charging systems becomes more necessary. Dynamic Wireless 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-scale DWPT 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. 

This paper presents the implementation of battery electric vehicles (BEVs) and battery energy storage systems (BESS) within residential networked microgrids that incorporate distributed energy resources (DERs) to produce electrical power, as well as an updated daily load curve for residential households, using Renewable Alternative Power Systems Simulation (RAPSim) Software. It is projected that the number of electric vehicles within the residential neighborhoods will increase, and therefore, it is essential that we provide a description of how to implement BEVs and BESS into a microgrid simulation software. Furthermore, this paper provides insight into the behavior of a microgrid considering case studies simulated within RAPSim software to advance electric power engineering education and research at undergraduate (senior) and graduate levels in the area of DER-integrated distribution systems.