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(GERS)迅速激增带来的挑战和机遇。例如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GER在传统公用事业网络边界之外运行，并由客户控制，因此它们引入了不同程度的可控性、可观察性和网络攻击脆弱性。该项目将通过开发新的分析基础和数据驱动的控制方法，为电力系统管理领域带来革命性的变化，以确保GERS的安全和可靠运行。该项目的学术价值包括开发了新的算法健壮的数据驱动控制策略，可以抵御GERS不可避免的网络漏洞，以及促进了我们对GER行为及其对电力系统动态影响的理解。该项目的更广泛影响包括增强国家关键能源基础设施的安全和安保，提高各种安全关键工程系统中人工智能和数据驱动控制方法的可靠性，以及促进两个少数群体服务机构的多样性和包容性。该项目的技术目标将通过引入基于模型和数据驱动的控制方法的新组合来实现，以确保GERS在不违反配电系统约束的情况下运行，尽管在现实世界的电力系统中管理GERS缺乏直接控制和验证能力。我们的方法可确保在运营的任何阶段进行网络安全探索和数据驱动控制，尽管模型存在不确定性。为了解决来自GERS的不可避免的损坏输入的挑战，例如感知负载的损坏，我们将开发在算法上对GERS中的漏洞具有健壮性的网格边缘控制算法。建议的方法和结果将在现实场景中进行测试，考虑到各种GRE的不同特点，并使用真实世界的GER数据和行业标准的计算机模拟，在不同的网络运营条件和约束下进行测试。该奖项反映了NSF的法定使命，并已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。