NSF Convergence Accelerator-Track D: AI-Enabled Provably Resilient Networked Microgrids

NSF 融合加速器 - 轨道 D：人工智能支持的可证明弹性的网络微电网

• 批准号: 2040599
• 负责人: Peng Zhang
• 金额: $ 100万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040599&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; AI

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. The broader impact and potential societal benefit of this Convergence Accelerator Phase I project is to develop coordinated networked microgrids (NMs) that have the potential to help restore neighboring distribution grids after a major blackout. These NMs also hold promise to significantly enhance the day-to day reliability of the powergrid. Many organizations (including the US Department of Energy) believe that, ultimately, NM R&D will lead to the next wave of smart-grid technology, which has the potential to achieve the vision of a highly resilient grid. Specifically, this project aims to develop and demonstrate Artificial Intelligence (AI) enabled, provably resilient networked microgrids (AI-grid). AI-Grid has the potential to transform today’s community power infrastructures into tomorrow’s autonomic microgrids and flexible services immune to cyber-attacks, high levels of renewable energy penetration, faults, and disastrous events, which have the potential to benefit various commercial sectors as well as the military.The key innovation proposed in this project is a programmable platform that integrates continuous-depth deep neural networks, reachability analysis, formal control, and cybersecurity technologies to enable scalable, self-protecting, autonomic and ultra-resilient microgrids and NMs capable of coordinating ultra-scale distributed energy systems and cultivating America’s smart communities and cities. For example, the Grid at the Energy & Innovation Park (EIP), a $1 billion microgrid project aimed at transforming “Hardware City” — New Britain, CT— from a traditional manufacturing Center into the new Center for Connecticut’s digital economy. This project will share with the relevant communities the resultant learning and control models, as well as the monitoring data from test beds and operational microgrids run by their collaborators. The adoption of AI-Grid in the EIP data center microgrid will demonstrate the tremendous potential in empowering our nation’s digital economic engine – the swiftly growing array of Data Centers. This project will provide a powerful living laboratory/classroom and big data sets to stimulate innovations towards smart, replicable, sustainable, and connected communities and infrastructures.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.

NSF融合加速器支持以使用为灵感，以团队为基础，多学科的努力，以应对国家重要性的挑战，并将在不久的将来为社会提供有价值的成果。融合加速器第一阶段项目的更广泛影响和潜在社会效益是开发协调的网络微电网（NM），这些微电网有可能在大停电后帮助恢复邻近的配电网。 这些NM还有望显著提高电网的日常可靠性。许多组织（包括美国能源部）认为，最终，NM研发将导致下一波智能电网技术，这有可能实现高弹性电网的愿景。具体而言，该项目旨在开发和展示人工智能（AI）支持的，可证明具有弹性的联网微电网（AI-grid）。AI-Grid有潜力将今天的社区电力基础设施转变为明天的自主微电网和灵活的服务，免受网络攻击，高水平的可再生能源渗透，故障和灾难性事件的影响，这有可能使各种商业部门以及军事受益。该项目提出的关键创新是一个可编程平台，集成了连续深度神经网络，可达性分析、正式控制和网络安全技术，以实现可扩展、自我保护、自主和超弹性的微电网和网络管理，能够协调超大规模的分布式能源系统，培养美国的智能社区和城市。例如，能源创新园（EIP）的电网，这是一个10亿美元的微电网项目，旨在将“硬件城”-新不列颠，康涅狄格州-从传统的制造中心转变为康涅狄格州数字经济的新中心。该项目将与相关社区分享由此产生的学习和控制模型，以及来自其合作者运行的测试台和运营微电网的监测数据。 在EIP数据中心微电网中采用AI-Grid将展示我们国家数字经济引擎的巨大潜力-快速增长的数据中心阵列。该项目将提供一个强大的生活实验室/教室和大数据集，以刺激智能，可复制，可持续和互联社区和基础设施的创新。该奖项反映了NSF的法定使命，并通过使用基金会的智力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

Noisy-Intermediate-Scale Quantum Electromagnetic Transients Program

噪声中尺度量子电磁瞬变计划

• DOI: 10.1109/tpwrs.2022.3172655
• 发表时间: 2022
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Zhou, Yifan;Zhang, Peng;Feng, Fei
• 通讯作者: Feng, Fei

Reachable Power Flow: Theory to Practice

• DOI: 10.1109/tpwrs.2020.3031196
• 发表时间: 2021-05
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Yifan Zhou;Peng Zhang

Programmable Adaptive Security Scanning for Networked Microgrids

• DOI: 10.1016/j.eng.2021.06.007
• 发表时间: 2021-06
• 期刊: Engineering
• 影响因子: 12.8
• 作者: Zimin Jiang;Zefan Tang;Peng Zhang;Yanyuan Qin

Quantum Distributed Microgrid Control

• DOI: 10.1109/pesgm48719.2022.9916709
• 发表时间: 2022-07
• 期刊: 2022 IEEE Power & Energy Society General Meeting (PESGM)
• 作者: P. Babahajiani;Peng Zhang

Quantum Power Flow

量子能流

• DOI: 10.1109/tpwrs.2021.3077382
• 发表时间: 2021
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Feng, Fei;Zhou, Yifan;Zhang, Peng
• 通讯作者: Zhang, Peng