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

这个NSF项目旨在解决现代电力系统中网格边缘资源（GERs）快速扩散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在传统的公用事业网络边界之外运行，并且由客户控制，因此它们引入了不同程度的可控性、可观察性和易受网络攻击的脆弱性。该项目将通过开发新的分析基础和数据驱动控制方法，为电力系统管理领域带来革命性的变化，以确保ger的安全运行。该项目的智力优势包括开发新颖的算法鲁棒数据驱动控制策略，该策略可以抵御不可避免的GER网络漏洞，并提高我们对GER行为及其对电力系统动力学的影响的理解。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和保障，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，并促进两个少数民族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颖组合来实现，以确保GERs在不违反配电系统约束的情况下运行，尽管在实际电力系统中管理ger缺乏直接控制和验证能力。我们的方法确保了在任何操作阶段的网络安全勘探和数据驱动控制，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，我们将开发对GERs中的漏洞具有算法鲁棒性的网格边缘控制算法。本文提出的方法和结果将在现实场景下进行测试，考虑到各种GREs的不同特征，并在不同的网络运行条件和约束下，使用真实的GER数据和工业标准计算机模拟。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.