We present a new algorithm for solving large-scale security-constrained optimal power flow in polar form (AC-SCOPF). The method builds on Nonlinearly Constrained augmented Lagrangian (NCL), an augmented Lagrangian method in which the subproblems are solved using an interior-point method. NCL has two key advantages for large-scale SCOPF. First, NCL handles difficult problems such as infeasible ones or models with complementarity constraints. Second, the augmented Lagrangian term naturally regularizes the Newton linear systems within the interior-point method, enabling solution of the Newton systems with a pivoting-free factorization that can be efficiently parallelized on GPUs. We assess the performance of our implementation, called MadNCL, on large-scale corrective AC-SCOPFs, with complementarity constraints modeling the corrective actions. Numerical results show that MadNCL can solve AC-SCOPF with 500 buses and 256 contingencies fully on the GPU in less than 3 minutes, whereas Knitro takes more than 3 hours to find an equivalent solution.
We report on the solution optimized for characterizing SNSPDs by rejecting electromagnetic interference (EMI) from various sources. The proposed readout method enhances measurement stability and enables reliable device characterization at low bias currents, where the signal-to-noise ratio is typically limited. By effectively suppressing EMI-induced noise, the method improves the ability to distinguish genuine detection events from spurious signals and reduces the effort required for data analysis. The approach has been applied to preliminary measurements of SNSPDs exposed to a particles emitted from a 241Am source, demonstrating stable operation and clean signal acquisition. While a detailed study of a detection is underway, the method establishes a foundation for further characterization of SNSPDs with various incident particles. The demonstrated EMI rejection technique is expected to facilitate future research in particle detection and support ongoing SNSPD development for applications in nuclear and accelerator-based experiments.
This paper introduces a framework based on Large Deviation Theory (LDT) to accurately and efficiently compute the rare probabilities of voltage collapse. We formulate the problem as finding the most probable failure point (the instanton) on the stability boundary and derive both first-order and second-order approximations for the collapse probability. The second-order method incorporates the local curvature of the stability boundary, yielding higher accuracy. This LDT framework generalizes methods based on Mahalanobis distance and is extensible to non-Gaussian uncertainties. We validate our approach on test systems, demonstrating that the LDT estimates converge to Monte Carlo results in the rare-event regime where direct sampling becomes computationally prohibitive.
As an emerging mode of trucking operations, platooning refers to virtual coupling of multiple trucks in a convoy, which brings energy and driver labor savings. While centralized truck platooning is expected to yield the most benefit, the issue of truck compliance with a central matching plan has not received much attention despite its practical relevance. Assuming the presence of a platform for centralized truck platooning matching, this study tackles the problem of maximizing total truck utility gain from platooning while minimizing the probability of truck noncompliance with the matching plan. Considering trucks with different origins and destinations, multiple routing possibilities, time window constraints, and different positions in the formed platoon, we first propose a computational procedure to develop the truck platooning matching graph. Then, we characterize the truck investigation process of a matching plan and show the approximate equivalence between the minimum probability of truck noncompliance with a matching plan and the minimum number of blocking pairs. Two integer programming models are subsequently formulated, without and with the possibility of money transfer between trucks in a platoon. To enable compact model formulations, a series of theoretical investigations is conducted to reduce the matching graph by identifying and eliminating edges that will surely not appear in the optimal matching plan. For the model with money transfer which is more challenging to solve, an efficient Benders decomposition-based algorithm is proposed. We further extend the problem to allow a truck to platoon multiple times in its trip, which is plausible for long-haul trips. As the original matching graph is no longer sufficient for multiple-time platooning, a new algorithm is developed with the core idea of constructing connected and time-compatible subgraphs, based on which a modified integer programming model is formulated. Extensive numerical experiments are performed to validate the theoretical investigations, models, and algorithms, providing rich insights about centralized truck platooning matching with compliance considerations. These insights help inform truck platooning deployment toward greater system compliance and efficiency.
Decarbonizing decentralized power networks requires autonomous hybrid renewable microgrids equipped with sustainable bulk energy storage. Tower-based solid gravity energy storage is emerging as a promising alternative to electrochemical batteries, but its techno-economic and environmental viability within off-grid systems has not been comprehensively assessed at a global geospatial scale. This study maps the global boundaries of gravity storage integration by evaluating its specific cost, life-cycle carbon footprint, and optimal sizing dynamics across diverse climatic conditions. The modeled district comprises commercial and residential buildings with typical dimensions, schedules, and specifications, and its electricity demand is dynamically captured through annual hourly simulations based on local weather data and responsive air conditioning, lighting, and equipment load calculations. A high-resolution, multi-objective optimization framework was applied to 6017 worldwide sites, using country- and region-specific cost and emission data, to size autonomous systems comprising solar photovoltaics, wind turbines, backup diesel generators, and solid gravity storage. The algorithm minimized the demand-based levelized cost of energy and life-cycle carbon intensity while enforcing near-perfect grid reliability, and structural robustness was validated through a multidimensional Monte Carlo sensitivity analysis. Gravity storage is universally indispensable for autonomous reliability, with optimal capacities ranging from 7.77 MWh in equatorial zones to nearly 79 MWh in extreme high latitudes. Except for extreme-climate sites, gravity storage offers a highly competitive (either comparable or clearly superior) alternative to conventional battery storage, with drastically lower environmental implications. In favorable climates, the technology achieves a median specific storage cost of ∼0.10 USD/kWh. The optimized microgrids exhibit strong performance globally, yielding an interquartile demand-based cost range of 0.058 to 0.120 USD/kWh. However, extreme northern climates impose severe economic and carbon penalties due to the massive infrastructural oversizing required for extended periods of resource deficits. These findings provide a generalizable guide for siting and sizing gravity-based off-grid networks and advancing the commercial deployment of the technology.
