Document Type
Article
Publication Date
3-2023
Department
Department of Electrical and Computer Engineering; Department of Mechanical Engineering-Engineering Mechanics
Abstract
Extreme fast charging (XFC) for electric vehicles (EVs) has emerged recently because of the short charging period. However, the extreme high charging power of EVs at XFC stations may severely impact distribution networks. This paper addresses the estimation of the charging power demand of XFC stations and the design of multiple XFC stations with renewable energy resources in current distribution networks. First, a Monte Carlo (MC) simulation tool was created utilizing the EV arrival time and state-of-charge (SOC) distributions obtained from the dataset of vehicle travel surveys. Various impact factors are considered to obtain a realistic estimation of the charging power demand of XFC stations. Then, a method for determining the optimal energy capacity of the energy storage system (ESS), ESS rated power, and size of photovoltaic (PV) panels for multiple XFC stations in a distribution network is presented, with the goal of achieving an optimal configuration. The optimal power flow technique is applied to this optimization so that the optimal solutions meet not only the charging demand but also the operational constraints related to XFC, ESS, PV panels, and distribution networks. Simulation results of a use case indicate that the presented MC simulation can estimate approximate real-world XFC charging demand, and the optimized ESS and PV units in multiple XFC stations in the distribution network can reduce the annual total cost of XFC stations and improve the performance of the distribution network.
Publication Title
Energies
Recommended Citation
Wu, Z.,
Bhat, P.,
&
Chen, B.
(2023).
Optimal Configuration of Extreme Fast Charging Stations Integrated with Energy Storage System and Photovoltaic Panels in Distribution Networks.
Energies,
16(5).
http://doi.org/10.3390/en16052385
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16999
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. Publisher’s version of record: https://doi.org/10.3390/en16052385