CPS: TTP Option: Small: Adaptive Charging Network Research Portal

CPS：TTP 选项：小型：自适应充电网络研究门户

• 批准号: 1932611
• 负责人: Steven Low
• 金额: $ 50万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932611&HistoricalAwards=false
• 关键词: CPS; TTP; Option; Small; Adaptive

This project is motivated by challenges in future energy cyber-physical systems (CPS). It will build a one-of-a-kind research facility that will enable new CPS research on smart grid and electric vehicles. It will advance the state of the art in the clean-energy space and facilitate the development and adoption of these technologies. The result of this project will be integrated with the power system analysis course at California Institute of Technology (Caltech). Preliminary work on this project has already been used at Caltech and other places around the world for classroom projects as well as smart grid research. This project will equip a new generation of students with the passion and the interdisciplinary knowledge for tackling one of the biggest challenges of our time. A seed community of contributors and users has already formed organically. This project will grow this open-source community. The PI's Lab has been working with a Caltech startup and Caltech Facilities department to design, deploy, and manage an Adaptive Charging Network (ACN) for electric vehicles at one of Caltech's parking garages. The primary goal of this project is to develop a suite of open-source software and hardware that will make Caltech ACN not just a production facility, but also a live testbed for CPS researchers at, and outside, Caltech. A secondary goal is to use the ACN Research Portal to study the joint optimization of distributed energy resources to provide both charging service to electric vehicles as well as energy services to electricity markets and utility companies. The key idea is to treat the production ACN as a physical layer and create an inter- mediate layer, called the ACN Research Portal, to allow safe, secure and easy access to charging data as well as push of charging commands for real-world testing of new algorithms on ACN. It will make large high-resolution charging data, data-driven simulator and experimental platform publicly available for the first time. This will allow CPS researchers to learn detailed statistical models, perform data analysis of real-world charging behavior, and easily and fairly compare control algorithms in a common platform driven by detailed real data. This is currently impossible.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.

该项目的动机是未来能源网络物理系统（CPS）面临的挑战。它将建立一个独一无二的研究设施，使智能电网和电动汽车的新CPS研究成为可能。它将推动清洁能源领域的最新技术，并促进这些技术的开发和采用。该项目的结果将与加州理工学院的电力系统分析课程相结合。这个项目的初步工作已经在加州理工学院和世界其他地方用于课堂项目和智能电网研究。这个项目将为新一代学生提供激情和跨学科知识，以应对我们这个时代最大的挑战之一。一个由贡献者和用户组成的种子社区已经形成。这个项目将发展这个开源社区。PI的实验室一直在与加州理工学院的一家初创公司和加州理工学院的设施部门合作，在加州理工学院的一个停车场为电动汽车设计、部署和管理自适应充电网络（ACN）。该项目的主要目标是开发一套开源软件和硬件，使加州理工学院ACN不仅成为生产设施，而且成为加州理工学院内外CPS研究人员的实时测试平台。第二个目标是利用ACN研究门户研究分布式能源资源的联合优化，为电动汽车提供充电服务，同时为电力市场和公用事业公司提供能源服务。关键思想是将生产ACN视为一个物理层，并创建一个中间层，称为ACN研究门户，以允许安全，可靠和轻松地访问充电数据，并为ACN上的新算法的实际测试推送充电命令。它将首次公开大量高分辨率充电数据、数据驱动模拟器和实验平台。这将使CPS研究人员能够学习详细的统计模型，对真实世界的收费行为进行数据分析，并在一个由详细真实数据驱动的公共平台上轻松公平地比较控制算法。这在目前是不可能的。该奖项反映了美国国家科学基金会的法定使命，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

Stability Constrained Reinforcement Learning for Real-Time Voltage Control

• DOI: 10.23919/acc53348.2022.9867476
• 发表时间: 2021-09
• 期刊: 2022 American Control Conference (ACC)
• 作者: Yuanyuan Shi;Guannan Qu;S. Low;Anima Anandkumar;A. Wierman

A Three-phase Power Flow Model and Balanced Network Analysis

三相潮流模型与平衡网络分析

• 发表时间: 2022
• 期刊: 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022
• 作者: Low, Steven H.

Real-time Flexibility Feedback for Closed-loop Aggregator and System Operator Coordination

• DOI: 10.1145/3396851.3397725
• 发表时间: 2020-06
• 期刊: Proceedings of the Eleventh ACM International Conference on Future Energy Systems
• 作者: Tongxin Li;S. Low;A. Wierman

Three-phase Transformer Modeling

三相变压器建模

• DOI: 10.1109/allerton58177.2023.10313400
• 发表时间: 2023
• 期刊: and Computing (Allerton
• 作者: Low, Steven H.

Classification of electric vehicle charging time series with selective clustering

• DOI: 10.1016/j.epsr.2020.106695
• 发表时间: 2020-12
• 期刊: Electric Power Systems Research
• 影响因子: 3.9
• 作者: Chenxi Sun;Tongxin Li;S. Low;V. Li