Economic Indicator-Based Power Quality Assessment of Distribution Network Incorporating Electric Vehicle Stations-Reference-Cited by-同舟云学术

Economic Indicator-Based Power Quality Assessment of Distribution Network Incorporating Electric Vehicle Stations

Published:2024-03-21 Issue: Volume: Page:
ISSN:
Container-title:
language:
Short-container-title:

Author:

Shi Shuaibin¹,Liu Yongli¹,Wang Qing¹,Cen Baoyi²

Affiliation:

1. China Southern Power Grid (China)

2. CET (China)

Abstract

The access of electric vehicle charging stations (EVCS) brings challenges to the stable operation of the distribution network.At present, there is a lack of indicator to quantify the economic losses caused by the decrease in power quality of the distribution network due to the access of EVCSs.In the paper, the travel trajectories of electric vehicle users are constructed through trip chain and state transition matrices,thereby obtaining the spatiotemporal distribution of charging loads.In addition, the voltage deviation and line loss caused by charging loads are unified into economic indicator to quantify.The simulations are conducted in a road network coupled to the IEEE 33-node distribution network.The result shows that the charging load of electric vehicle charging stations have a significant impact on the power quality of the distribution network.At the same time, optimizing the location of charging stations and guiding electric vehicle users’ charging behaviorcan effectively improve the power quality and economic efficiency of distribution networks.

Publisher

Springer Science and Business Media LLC

Reference24 articles.

1. Agbesi, Phillip and Ruffino, Rico and Hakovirta, Marko (2024) Creating an optimal electric vehicle ecosystem: an investigation of electric vehicle stakeholders and ecosystem trends in the US. SN Business and Economics 4: https://doi.org/10.1007/s43546-024-00624-7, 02

2. Itani, Khaled and De Bernardinis, Alexandre (2023) Review on New-Generation Batteries Technologies: Trends and Future Directions. Energies 16(22) https://doi.org/10.3390/en16227530, Battery technologies have recently undergone significant advancements in design and manufacturing to meet the performance requirements of a wide range of applications, including electromobility and stationary domains. For e-mobility, batteries are essential components in various types of electric vehicles (EVs), including battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), and fuel cell electric vehicles (FCEVs). These EVs rely on diverse charging systems, including conventional charging, fast-charging, and vehicle-to-everything (V2X) systems. In stationary applications, batteries are increasingly being employed for the electrical management of micro/smart grids as transient buffer energy storage. Batteries are commonly used in conjunction with power electronic interfaces to adapt to the specific requirements of various applications. Furthermore, power electronic interfaces to batteries themselves have evolved technologically, resulting in more efficient, thermally efficient, compact, and robust power converter architectures. This article offers a comprehensive review of new-generation battery technologies. The topic is approached from the perspective of applications, emerging trends, and future directions. The article explores new battery technologies utilizing innovative electrode and electrolyte materials, their application domains, and technological limitations. In conclusion, a discussion and analysis are provided, synthesizing the technological evolution of batteries while highlighting new trends, directions, and prospects., 1996-1073, https://www.mdpi.com/1996-1073/16/22/7530, 7530

3. Peng, Shurong and Zhang, Heng and Yang, Yunhao and Li, Bin and Su, Sheng and Huang, Shijun and Zheng, Guodong (2022) Spatial-temporal Dynamic Forecasting of EVs Charging Load Based on DCC-2D. Chinese Journal of Electrical Engineering 8(1): 53-62 https://doi.org/10.23919/CJEE.2022.000005, Convolution;Power system dynamics;Neural networks;Stacking;Predictive models;Electric vehicle charging;Data models;Time and space dynamic prediction;dilated convolution;charging load;convolutional neural network

4. Zhang, Xian and Chan, Ka Wing and Li, Hairong and Wang, Huaizhi and Qiu, Jing and Wang, Guibin (2021) Deep-Learning-Based Probabilistic Forecasting of Electric Vehicle Charging Load With a Novel Queuing Model. IEEE Transactions on Cybernetics 51(6): 3157-3170 https://doi.org/10.1109/TCYB.2020.2975134, Electric vehicle charging;Load modeling;Predictive models;Forecasting;Load forecasting;Data models;Artificial neural networks;Convolutional neural network (CNN);deep learning;driver behavior;electric vehicle (EV);load forecast;queuing model

5. Huang, Xingshuai and Wu, Di and Boulet, Benoit (2023) MetaProbformer for Charging Load Probabilistic Forecasting of Electric Vehicle Charging Stations. IEEE Transactions on Intelligent Transportation Systems 24(10): 10445-10455 https://doi.org/10.1109/TITS.2023.3276947, Forecasting;Predictive models;Load forecasting;Load modeling;Adaptation models;Transformers;Task analysis;Transformer;meta-learning;charging load forecasting