CRII: CPS: Internet-Inspired Autonomous Electric Vehicle (EV) Charging

CRII：CPS：互联网启发的自动电动汽车 (EV) 充电

• 批准号: 1755996
• 负责人: Mithat Kisacikoglu
• 金额: $ 17.5万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755996&HistoricalAwards=false
• 关键词: CRII; CPS; Internet; Inspired; Autonomous

Electric vehicles (EVs) are transforming the modern transportation and energy systems. With a growing EV market, the mass penetration of EVs into the utility grid will result in detrimental effects due to coincidence between utility peak power loading and EV charging. This impact will include increased peak loading and voltage drops that will call for over-investments in the network resulting in an overall high cost for the society. Therefore, advanced and practical interaction methods between vehicles, charging stations, and the utility grid need to be developed. Internet-inspired EV charge control offers a new paradigm-shift for EV charge control studies. This project aims to build a new autonomous and fair EV charge control technique that can penetrate into practice at large scales. The significance of the proposed idea lies in the effort to bring together a cyber-physical system that utilizes the prior art in end-to-end internet network congestion control with decentralized autonomous EV charging. The effort is inter-disciplinary and brings together ideas from computer science as well as power engineering to support mass integration of EVs into the conventional electric energy system. The proposed idea can impact the way all grid-connected power electronics systems are controlled. The algorithms developed in this work can be used to accelerate the integration of low-carbon energy sources such as solar photovoltaic systems. The proposed research develops a new family of EV charging algorithms by adopting some of the well-deployed end-to-end congestion control techniques in the Internet. The research objectives include (i) investigating distribution grid congestion through extensive data collection from field, modeling, and simulations to understand and extract the impacts of EV charging on voltage and frequency signatures at the end-nodes, (ii) design of entirely decentralized and localized EV charging algorithms that work autonomously, (iii) investigation of adaptive increase and decrease of EV charging rate according to the power flow congestion on the distribution network to attain proportional or max-min fair charging of EVs in a neighborhood, and (iv) exploration of hybrid charging architectures where more than one EV measurement can be utilized. The cyber-physical system that will be developed with this project will help the researchers understand and resolve issues relating to large scale electric vehicle integration.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

电动汽车（EV）正在改变现代交通和能源系统。 随着电动汽车市场的不断增长，电动汽车大规模渗透到公用电网中将由于公用事业峰值功率负载和电动汽车充电之间的重合而导致不利影响。这种影响将包括增加的峰值负荷和电压下降，这将要求对网络进行过度投资，从而导致社会的总体成本较高。因此，需要开发车辆、充电站和公用电网之间先进实用的交互方法。互联网启发的电动汽车充电控制提供了一个新的范式转变电动汽车充电控制研究。本项目旨在建立一种新的自主和公平的电动汽车充电控制技术，可以大规模渗透到实践中。所提出的想法的重要性在于努力将网络物理系统结合在一起，该系统利用现有技术进行端到端互联网网络拥塞控制，并具有分散的自主EV充电。这项工作是跨学科的，汇集了计算机科学和电力工程的思想，以支持电动汽车大规模集成到传统的电能系统中。所提出的想法可以影响所有并网电力电子系统的控制方式。在这项工作中开发的算法可用于加速低碳能源的整合，如太阳能光伏系统。 该研究开发了一个新的家庭的电动汽车充电算法，采用一些部署良好的端到端的拥塞控制技术在互联网上。研究目标包括：（i）通过从现场、建模和仿真中收集大量数据来调查配电网拥塞，以了解和提取电动汽车充电对端节点电压和频率特征的影响，（ii）设计完全分散和本地化的电动汽车充电算法，（iii）研究根据配电网上的潮流拥塞来自适应地增加和减少EV充电速率，以实现对邻近区域中的EV的按比例或最大-最小公平充电，以及（iv）可以利用多于一个EV测量的混合充电架构的探索。该项目将开发的网络物理系统将帮助研究人员理解和解决与大规模电动汽车集成相关的问题。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Analysis of an Internet-Inspired EV Charging Network in a Distribution Grid

配电网中受互联网启发的电动汽车充电网络分析

• DOI: 10.1109/tdc.2018.8440252
• 发表时间: 2018
• 期刊: IEEE/PES Transmission and Distribution Conference and Exposition
• 作者: Ucer, Emin;Kisacikoglu, Mithat;Yuksel, Murat
• 通讯作者: Yuksel, Murat

A Machine Learning Approach for Understanding Power Distribution System Congestion

用于了解配电系统拥塞的机器学习方法

• DOI: 10.1109/ecce44975.2020.9236353
• 发表时间: 2020
• 期刊: 2020 IEEE Energy Conversion Congress and Exposition (ECCE
• 作者: Ucer, Emin;Kisacikoglu, Mithat;Gurbuz, Ali;Rahman, Shahinur;Yuksel, Murat
• 通讯作者: Yuksel, Murat

Analysis of Decentralized AIMD-based EV Charging Control

基于AIMD的分散式电动汽车充电控制分析

• DOI: 10.1109/pesgm40551.2019.8973725
• 发表时间: 2019
• 期刊: IEEE Power & Energy Society General Meeting (PESGM
• 作者: Ucer, Emin;Kisacikoglu, Mithat C.;Yuksel, Murat
• 通讯作者: Yuksel, Murat

Analysis of AIMD Algorithm for EV Charging

• DOI: 10.1145/3307772.3330179
• 发表时间: 2019-06
• 期刊: Proceedings of the Tenth ACM International Conference on Future Energy Systems
• 作者: Emin Ucer;M. Kisacikoglu;M. Yuksel

Learning EV Integration Impact on a Low Voltage Distribution Grid

• DOI: 10.1109/pesgm.2018.8586208
• 发表时间: 2018-08
• 期刊: 2018 IEEE Power & Energy Society General Meeting (PESGM)
• 作者: Emin Ucer;M. Kisacikoglu;Ali Cafer Gurbuz