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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. 

Due to environmental concerns, electric vehicles (EVs) have become increasingly popular in recent decades. While EV s offer several benefits, they also present challenges such as prolonged charging times and range anxiety. To address these issues and enhance EV market participation, dynamic wireless power transfer (DWPT) is gaining a great attention in electrified-transportation sector, leading to an emergence of DWPT for EVs. DWPT offers advantages like charging while in-motion. However, DWPT roadways also impose additional demands on the power system, potentially increasing operational costs. The main objective of this paper is to manage effectively the additional load caused by DWPT roadways, and this paper presents the utilization of distributed energy resources (DERs), such as photovoltaic (PV) systems and battery storage system (BSS), to minimize the system costs. The importance of our proposed load management strategy becomes even more critical during extreme events. Therefore, this paper further examines two scenarios, i.e., normal operations and under extreme conditions considering line outages, to compare the costs associated with DWPT systems. The efficiency of the proposed method is validated using IEEE 33-bus distribution systems through a mixed integer linear programming (MILP) optimization problem. Test results demonstrate that integrating DWPT system increases the system costs under both normal and extreme conditions, however, the DER-based mechanism is capable of mitigating these costs optimally. 

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. 

The development of new technologies is increasing transportation electrification and electric vehicles (EVs) are expected to become even more popular in coming years. High EV adoption rates can increase the potential to use EVs as an energy resource and operate in vehicle-to-grid (V2G) and vehicle-to-home (V2H). This paper focuses on the resilience analysis of using EVs and roof-top solar photovoltaic systems (PVs) to provide power support in network microgrids (MGs) experiencing an outage due to extreme weather conditions. To determine the effectiveness of using EVs and PVs as backup energy resources, a set of resilience metrics are evaluated for different cases and duration. Simulation results show that the management of EVs and PVs in residential networked MGs could provide power support for several hours during the restoration of a distribution system experiencing an outage. 

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. 

The transition to electric vehicles (EVs) is underway globally and EVs are expected to become more widely adopted in the coming years. One of the main characteristics of EVs is that they are not only seen as mean for transportation but also potentially as a flexible energy storage resource in vehicle-to-grid (V2G) applications. This paper proposes a resilience analysis on the feasibility of using EVs for power restoration and supply of residential networked microgrids (MGs) experiencing a power outage due to extreme weather. In order to evaluate the effectiveness of utilizing EVs as a backup power supply during an outage, various case studies are presented considering different scenarios and resilience metrics. Test results demonstrate that EVs can satisfy the energy requirements of a residential household for more than 6 hours but, also provide power to the distribution grid through MG aggregation.