Dynamic and Safe Optimization-Based Control for Reliable Power Grids

Reliable electric power is essential to economic strength, public safety, domestic manufacturing and national competitiveness. Modern power grids are becoming increasingly dynamic as they support growing electricity demand from data centers, advanced computing, artificial intelligence (AI) and expanding U.S. industry. At the same time, grid operators must manage uncertain and rapidly changing conditions that cannot always be measured or predicted in real time. Maintaining reliable electric service under these conditions requires power systems that can respond quickly to unexpected disturbances while continuously operating within safe operating limits. The research funded by this award will develop new control methods that enable electric power grids to operate safely, reliably and efficiently under uncertainty. The research will focus on creating fast, optimization-based control algorithms that can make real-time decisions using only limited measurements, communication, and computational resources. The research serves national interests by advancing engineering foundations for reliable energy generation and transport services, supporting American leadership in advanced computing and AI, and enabling technologies that require fast and locally implementable control software. The project seeks to develop dynamic, optimization-based, safe controllers for networked engineering systems with uncertain inputs and partially known dynamics. The central goal is to design controllers that approximate optimization-based safety filters in real time while requiring only local or limited measurements. The research will study how these controllers can enforce operational limits, maintain stability, and balance performance with practical constraints on sensing, computation, and communication. The project will develop analytical tools for quantifying the tradeoffs between reliability, constraint satisfaction, and implementation complexity, using methods from control theory, online optimization, contraction analysis, distributed computation, and multi-time-scale dynamical systems. The work will address decentralized implementations, gather-and-broadcast architectures with communication delays, distributed implementations that avoid centralized coordination, sampled-data implementations, and extensions to time-varying operating limits. The methods will be evaluated in motivating applications involving frequency regulation in power grids and congestion control in transportation networks. The expected contributions include new design principles, theoretical guarantees, and computational tools for reliable control of large networked systems operating under uncertainty. Educational and outreach activities will integrate the project outcomes into undergraduate and graduate instruction, student research experiences, and modules for K-12 students focused on modern engineering systems. 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.