Equipment: MRI: Track 1 Acquisition of a Digital Real-Time Simulator to Enhance Research and Student Research Training in Next-Generation Engineering and Computer Science

该NSF MRI奖项支持收购最先进的旗舰数字实时仿真器（DRTS），该仿真器目前正在带来变革性变化，为多个领域的建模和仿真提供支持，包括传输和配电网，智能/微电网，机器人，网络物理系统，航空航天和电动汽车，根据用例使用相同的硬件进行不同级别的仿真。收购这个非常强大的，可扩展的，灵活的，计算上级设计，测试和验证平台将在博伊西州立大学（BSU）的能力进行领先的研究，提供多学科的研究经验，为学生研究人员使用领先的能力，并扩大参与科学和工程研究的多样化的多学科社区。拟议仪器的计划用途构成了令人兴奋的研究领域，这些领域能够实现并推动采购请求，主要包括现代电力和控制系统、机器人技术以及网络物理系统的安全性。计划中的项目将利用研究仪器（1）推进爱达荷州电网的弹性研究;（2）开发退役电动汽车电池的退化模型;（3）为开发用于建立和断开接触的机器人控制器铺平道路（4）开发为无人机设计的控制算法在电力线上安装一系列物品;（5）加强现代电力系统的实时网络安全。这些研究计划将为学生研究人员提供最先进的测试，验证，演示设施和研究工作。这些机会将激励学生追求科学，技术，工程和数学（STEM）教育。最终，这些努力将有助于培养一支高技能的STEM劳动力。集成研究仪器包括硬件在环（HIL）系统、网络物理仿真（CPS）插件和电源硬件在环（PHIL）微电网测试台，沿着必要的软件和硬件组件，这些组件将促进现代电力和控制系统、机器人、以及网络物理系统的安全性。该综合研究工具支持的研究活动包括：1）通过全面考虑所有极端事件，确定适当的弹性指标，以提高爱达荷州电网的弹性; 2）改善高保真电力系统建模平台中二次电池储能系统（SLBESS）的表示，以更好地反映其提供电网服务的独特能力和限制; 3）采用最先进的机器学习技术，从学习专家混合模型到采用注意力机制，为建立和断开接触的全自主机器人系统开发基于被动性的控制器; 4）为无人机开发状态估计和控制算法，使其能够在电力线上安装各种物品; 5）研究网络物理能源系统中的网络攻击检测和预防技术，以防止电网中网络安全问题的灾难性后果，并为分布式能源提供强大的网络安全解决方案; 6）开发新方法，致力于为低惯性微电网提供可互操作、具有成本效益的控制和保护解决方案，以推进BSU的微电网研究和研究培训; 7）开发虚拟集成环境（VIE）框架，其容纳广泛的肢体模型和算法，8）评估爱达荷州以可再生能源为主的电网的基于机器学习的预测电网响应系统，以实施野火前减灾战略; 9）研究实施强大的网络攻击检测和响应系统的解决方案，以保护智能微电网中DER的集成点; 10）提高对上游电网严重故障情况下微电网惯性仿真方法的理解。该项目由电气、通信和网络系统部（ECCS）和激励竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了NSF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 MRI award supports the acquisition of a state-of-the-art flagship digital real-time simulator (DRTS) that is currently bringing transformative change to provide support to model and simulate across multiple domains, including transmission and distribution grids, smart/micro grids, robotics, cyber-physical systems, aerospace, and e-mobility at varying levels of simulation fidelities with the same hardware depending on the use cases. The acquisition of this very powerful, scalable, flexible, computationally superior design, test, and validation platform will make a substantial improvement in Boise State University's (BSU) capabilities to conduct leading-edge research, to provide multi-disciplinary research experiences for student researchers using leading-edge capabilities, and to broaden the participation in science and engineering research by a diverse multi-disciplinary community. The planned uses of the proposed instrument constitute exciting research domains that enable and drive the acquisition request, primarily including modern power and control systems, robotics, and the security of cyber-physical systems. The planned projects will utilize the research instrument to (1) advance the resilience research of Idaho's electrical grid; (2) develop degradation models of retired electric vehicle batteries; (3) pave the way for the development of controllers for robots that make and break contact (4) develop control algorithms designed for drones installing a range of items on power lines; (5) enhance the real-time cybersecurity in modern power systems. These research initiatives will provide student researchers access to state-of-the-art tests, validation, demonstration facilities, and research endeavors. These opportunities will inspire students to pursue education in science, technology, engineering, and mathematics (STEM). Ultimately, these efforts will contribute to developing a highly skilled STEM workforce.The integrated research instrument includes a hardware-in-the-loop (HIL) system, a cyber-physical simulation (CPS) add-on, and a power hardware-in-the-loop (PHIL) microgrid test bench, along with the necessary software and hardware components that will catalyze research activities in modern power and control systems, robotics, and the security of cyber-physical systems. Research activities to be enabled by this integrated research instrument include 1) identifying appropriate resilience metrics to enhance the resilience of Idaho's electric grid through comprehensively considering all extreme events; 2) improving the representation of second-life-battery energy storage systems (SLBESS) in high-fidelity power systems modeling platforms to better reflect their unique capabilities and constraints in delivering grid services; 3) employing state-of-the-art machine learning techniques ranging from learning mixture-of-experts models to employing attention mechanisms for developing passivity-based controllers for fully autonomous robotic systems that make and break contact; 4) developing state estimation and control algorithms for drones with the capability to install a wide range of items on power lines; 5) researching cyber-attack detection and prevention techniques in cyber-physical energy systems to prevent the catastrophic consequences of cyber-security issues in power grids and providing robust cyber-security solutions for distributed energy resources (DERs); 6) developing new approaches to work towards interoperable, cost-effective control and protection solutions for low inertia microgrids to advance microgrid research and research training at BSU; 7) developing a virtual integration environment (VIE) framework that accommodates a wide range of limb models and algorithms, enabling the adequate management of upper-limb prosthetics with superior dexterity; 8) evaluating machine learning-based predictive grid response systems for Idaho's renewable-dominated power grid to implement a pre-wildfire disaster mitigation strategy; 9) researching solutions for implementing a robust cyber-attack detection and response system to safeguard the integration points of DERs in smart microgrids; 10) raising understanding of microgrid inertia emulation methods in case of severe upstream grid failures. This project is jointly funded by the Division of Electrical, Communications, and Cyber Systems (ECCS) and the Established Program to Stimulate Competitive Research (EPSCoR).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.