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: 2330154
• 负责人: Mahnoosh Alizadeh
• 金额: $ 20万
• 依托单位: University of California-Santa Barbara
• 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=2330154&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网格边缘资源（GER）快速增长所带来的挑战和机遇。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的运行超出了传统的公用事业网络边界，并由客户控制，因此它们引入了可变的可控性，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变革性的变化，以确保GER的安全运行。该项目的智力优势包括开发新的算法强大的数据驱动的控制策略，可以承受不可避免的网络漏洞的GER，和我们的理解的进步格尔行为及其对电力系统动态的影响。该项目的更广泛影响包括加强国家关键能源基础设施的安全性，提高各种安全关键工程系统的人工智能和数据驱动控制方法的可靠性，和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的新组合来实现，驱动控制方法，以保证在不违反配电系统约束的情况下操作GER，尽管在现实世界的电力系统中缺乏管理GER的直接控制和验证能力。我们的方法确保在任何操作阶段进行网络安全的探索和数据驱动的控制，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，例如感测负载中的损坏，我们将开发对GERs中的漏洞具有算法鲁棒性的网格边缘控制算法。建议的方法和结果将在现实场景下进行测试，考虑到各种GRES的不同特性，并在不同的网络运行条件和限制下，使用真实世界的格尔数据和行业标准的计算机模拟。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。