106 NOTICES OF THE AMERICAN MATHEMATICAL SOCIETY VOLUME 66, NUMBER 1 GOV MATH of how such fluctuations interact with electromechanical timescales such as inter-area low-frequency oscillation periods that are in the order of seconds [1], and what the effect is of such interactions on the dynamics and stability of power systems. This becomes a fundamental challenge in assessing and optimizing power system operations under uncertainties to ensure reliability, resilience, and security of the critical electric infrastructure. At Pacific Northwest National Laboratory, we are tackling this challenge by combining our unique power system domain expertise and computational mathematics capabilities. The so-called Probability Density Function (PDF) method is a tool for analyzing the one-point PDF, i.e., the probability of being at a certain configuration at a certain time, of the state of general dynamical systems driven by autocorrelated noises [2]. We have successfully adapted and applied the PDF method to the stochastic differential equations governing power system dynamics. Such PDF- based analysis reveals that power generation fluctuations with a specific correlation time magnify the amplitude of the fluctuations around certain operating conditions [3]. Furthermore, this analysis shows that the resonance correlation time is related to the electromechanical times- cales of the power system [4]. This “stochastic resonance” phenomenon illustrates the importance of quantifying and accounting for the effect of the correlation time of stochastic drivers on dynamical systems. This is a founda- tional discovery for large-scale power system uncertainty assessment. It offers a whole new insight in how to assess and mitigate the impact of uncertainties on power systems and potentially help to answer fundamental questions such as how much renewable energy a power system can David Barajas-Solano is a mathematician in the Computational Mathe- matics Group within the Physical and Computational Sciences Directorate at Pacific Northwest National Laboratory. His email address is David. Barajas-Solano@pnnl.gov. Zhenyu Huang is an electrical engineer in the Electricity Infrastructure and Buildings Group within the Energy and Environment Directorate at Pacific Northwest National Laboratory. His email address is Zhenyu. Huang@pnnl.gov. Communicated by Notices Associate Editor Emilie Purvine. For permission to reprint this article, please contact: reprint -permission@ams.org. DOI: http://dx.doi.org/10.1090/noti1766 National laboratories are interdisciplinary institutions that tackle important problems from energy and infrastructure to national security. In addition to current discoveries in these technical domains, national labs have a long history of highly impactful breakthroughs. In this column we learn about a current application of differential equations at Pacific Northwest National Laboratory to understand correlated noise that occurs in power systems. We also hear about how the Monte Carlo method for solving complex problems in a variety of disciplines, introduced by Los Alamos National Laboratory, is still being used and advanced there today. Stochastic Resonance When Uncertainty Meets Dynamics David Barajas-Solano and Zhenyu Huang Due to the increase in penetration of renewable energy sources and intelligent load devices, power systems are subject to significantly larger uncertainties in power gen- eration and consumption stemming from fluctuating, dif- ficult-to-predict weather conditions and modern demand profiles. Such power fluctuations are characterized by multiple, non-trivial correlation timescales ranging from seconds to minutes. Therefore, the question arises in terms