Collaborative Research: Learning for Safe and Secure Operation of Grid-Edge Resources

协作研究：学习电网边缘资源的安全可靠运行

• 批准号: 2330155
• 负责人: Amir-Hamed Mohsenian-Rad
• 金额: $ 14.59万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330155&HistoricalAwards=false
• 关键词: Collaborative; Research; Learning; Safe; Secure

This NSF project aims to address the challenges and opportunities presented by the rapid proliferation of Grid Edge Resources (GERs) in modern power systems. Examples include distributed generators and smart inverters, smart thermostatically controlled loads, electric vehicles, and battery energy storage systems. Since GERs operate beyond traditional utility network boundaries and are controlled by customers, they introduce variable levels of controllability, observability, and vulnerability to cyber-attacks. The project will bring transformative change to the field of power system management through the development of a new analytical foundation and data-driven control methodologies to ensure the safe and secure operation of GERs. The intellectual merits of the project include the development of novel algorithmically robust data-driven control strategies that can withstand the unavoidable cyber vulnerabilities of GERs, and the advancement of our understanding of GER behavior and its impact on power system dynamics. The broader impacts of the project include enhancing the safety and security of the nation's critical energy infrastructure, improving the reliability of artificial intelligence and data-driven control methods across various safety-critical engineering systems, and promoting diversity and inclusion in two minority-serving institutions.The technical objectives of this project will be achieved by introducing a novel combination of model-based and data-driven control methods to guarantee that GERs are operated without violating power distribution systems’ constraints, despite the lack of direct control and validation capabilities in managing GERs in real-world power systems. Our approach ensures network-safe exploration and data-driven control at any stage of operation, despite model uncertainty. To address the challenge of unavoidable corrupt inputs from GERs, such as corruption in sensed load, we will develop grid edge control algorithms that are algorithmically robust to vulnerabilities in GERs. The proposed methods and results will be tested under realistic scenarios, considering diverse characteristics of various GREs, and under different network operating conditions and constraints, using real-world GER data and industry-standard computer simulations.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NSF项目旨在应对现代电力系统中电网边缘资源（GER）快速扩散带来的挑战和机遇。示例包括分布式发电机和智能逆变器，智能恒温控制负载，电动汽车和电池储能系统。由于GERS超越了传统的公用网络边界，并且受客户的控制，因此他们引入了可控性，可观察性和对网络攻击的脆弱性。该项目将通过开发新的分析基础和数据驱动的控制方法来为电力系统管理领域带来变革性的变化，以确保GERS的安全和安全操作。该项目的智力优点包括开发新颖的算法可靠的数据驱动的控制策略，这些控制策略可以承受GERS不可避免的网络脆弱性，以及我们对GER行为及其对电力系统动态的影响的理解的发展。该项目的更广泛的影响包括增强国家关键能源基础设施的安全性和安全性，提高人工智能和数据驱动的控制方法的可靠性，并提高各种安全 - 关键工程系统的数据驱动的控制方法，以及在两个少数族裔服务机构中的多样性和包容性。将通过引入基于模型的方法来确保该项目的技术和数据来促进该项目的数据，以确保模型的分配方式，以实现模型的分配方法，以实现模型的数据，以实现模型的数据，以实现模型的数据。尽管缺乏直接控制和验证能力，但在管理现实世界电源系统中的GER方面缺乏限制。我们的方法可确保在操作的任何阶段（所需的模型不确定性）下网络安全的探索和数据驱动的控制。为了解决GERS不可避免的损坏输入的挑战，例如在感知负载中的损坏，我们将开发网格边缘控制算法，这些算法对GERS中的脆弱性具有强大的算法。提出的方法和结果将在现实的场景下，考虑各种GRE的各种特征，以及在不同的网络操作条件和约束下，使用现实世界中的GER数据和行业标准的计算机模拟。这一奖项反映了NSF的法定任务，并通过基金会的知识优点和广泛的影响来评估NSF的法定任务，并被认为是宝贵的支持。