CAREER: Universal Modeling of Real and Virtual Energy Storage with Connected Power Electronics

职业：利用互联电力电子技术对真实和虚拟储能进行通用建模

• 批准号: 2146350
• 负责人: Yue Cao
• 金额: $ 50万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146350&HistoricalAwards=false
• 关键词: CAREER; Universal; Modeling; Real; Virtual

Energy storage supports the uninterruptable operation of electricity, mechanics, heat, etc. Modern highly-dynamic electrical systems, such as renewable-dominated grids, electric vehicles and aircraft, require energy storage to provide resilience, mitigating power variations at much smaller timescales. Multiple “real” energy storage physics exist, such as batteries, supercapacitors, fuel cells, but are limited in energy or power bandwidth. Recently, modulated load control, particularly through flexible thermal loads, such as building HVAC systems or water heaters, has gained understanding and is regarded as “virtual” storage, given its inherent existence as an energy buffer around a nominal load. A typical design of an energy storage system considers one or multiple storage physics and aggregates their respective electric models for control and energy management; this way overlooks the system-level picture and handles the complexity after it is created. This research explores a top-down co-design process by utilizing a universal energy storage electric model, combining real and virtual energy storage and connected power electronics. The proposed process directly yields optimal energy storage requirements among a cluster of energy storage options, and allows for most resiliency while reducing the complex energy management burden and sustaining an extended lifetime. The work will benefit many stakeholders in transportation electrification, utilities, and building sectors. Further, the project will enhance undergraduate and graduate curriculum by combining the advances in power electronics tied energy storage systems. The project will work with management consulting and international educational partners to train STEM students toward leadership and global vision. In addition, the project will reach out to local small businesses and Native American tribes to provide education in emerging energy storage technologies, thereby enabling pursuit of prosperous careers in the field.This project develops a novel universal modeling and design framework for energy storage systems. Conventional heuristic selection of energy storage is suboptimal, dynamic interaction of real and virtual storage is lacking, the power electronics side of virtual energy storage is not well understood, and the lifetime impact of combined storage is not known. Fundamental research in modeling and design emphasizes bandwidth selections using an enhanced multi-timescale equivalent impedance network, followed by a co-design of connected power electronics, multi-objective optimization, dynamic energy management, and reliability analysis. Several machine learning-based methods in intelligent search, deep reinforcement learning, and big-data-driven control will also play a role in the work. Scientific knowledge will be gained through in-depth electrical-thermal-mechanical physics, modern power electronic converters and controls, emerging data science and artificial intelligence, and a hardware testbed.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.

能量存储支持电力、机械、热能等的不间断运行。现代高动态电力系统，如以可再生能源为主的电网、电动汽车和飞机，需要能量存储来提供弹性，在更小的时间尺度上缓解功率变化。存在多种“真正的”储能物理，如电池、超级电容器、燃料电池，但在能量或功率带宽上是有限的。最近，调制负荷控制，特别是通过灵活的热负荷，如建筑暖通空调系统或热水器，已经得到了理解，并被视为“虚拟”存储，因为它作为额定负荷周围的能量缓冲器的固有存在。能量存储系统的典型设计考虑了一个或多个存储物理，并聚合了它们各自的电气模型以用于控制和能量管理；这种方式忽略了系统级的情况，并在创建系统级之后处理了复杂性。本研究利用通用储能电气模型，结合真实储能与虚拟储能及互联电力电子技术，探索一种自上而下的协同设计过程。建议的流程直接在一系列能源储存选项中产生最佳的能源储存需求，并允许最大的弹性，同时减少复杂的能源管理负担并维持更长的使用寿命。这项工作将使交通电气化、公用事业和建筑行业的许多利益相关者受益。此外，该项目将结合电力电子与储能系统的进步，加强本科生和研究生的课程。该项目将与管理咨询和国际教育合作伙伴合作，培养STEM学生的领导力和全球视野。此外，该项目将接触当地小企业和美洲原住民部落，提供新兴能源存储技术方面的教育，从而使人们能够在该领域追求成功的职业生涯。该项目为能源存储系统开发了一个新的通用建模和设计框架。传统的启发式储能方法是次优的，缺乏真实储能和虚拟储能的动态交互，对虚拟储能的电力电子学方面了解不多，组合储能对寿命的影响也是未知的。建模和设计的基础研究强调使用增强型多时间尺度等效阻抗网络选择带宽，然后是互联电力电子设备的联合设计、多目标优化、动态能量管理和可靠性分析。智能搜索、深度强化学习和大数据驱动控制中的几种基于机器学习的方法也将在工作中发挥作用。科学知识将通过深入的电-热-机械物理、现代电力电子转换器和控制、新兴数据科学和人工智能以及硬件测试获得。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Sizing BESS and On-site Renewable for Battery-electric Freight Rail Charging Station

电池电动货运铁路充电站的 BESS 和现场可再生能源选型

• DOI: 10.1109/pedg54999.2022.9923179
• 发表时间: 2022
• 期刊: 2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems (PEDG
• 作者: Guov, Vinson;Jackson, Derek;Obi, Manasseh;Cao, Yue
• 通讯作者: Cao, Yue

Aircraft Distributed Electric Propulsion Technologies—A Review

• DOI: 10.1109/tte.2022.3197332
• 发表时间: 2022-12
• 期刊: IEEE Transactions on Transportation Electrification
• 影响因子: 7
• 作者: M. T. Fard;Jiangbiao He;Hao Huang;Yue Cao

Bi-Level Optimization Framework for Heavy-Duty Electric Truck Charging Station Design

重型电动卡车充电站设计双层优化框架

• DOI: 10.1109/itec53557.2022.9813815
• 发表时间: 2022
• 期刊: 2022 IEEE Transportation Electrification Conference & Expo (ITEC
• 作者: Jackson, Derek;Cao, Yue;Beil, Ian
• 通讯作者: Beil, Ian