Rising electricity demand and the growing integration of renewables are intensifying congestion in transmission grids. Grid topology optimization through busbar splitting (BuS) and optimal transmission switching can alleviate grid congestion and reduce the generation costs in a power system. However, BuS optimization requires a large number of binary variables, and analyzing all the substations for potential new topological actions is computationally intractable, particularly in large grids. To tackle this issue, we propose a set of metrics to identify and rank promising candidates for BuS, focusing on finding buses where topology optimization can reduce generation costs. To assess the effect of BuS on the identified buses, we use a combined mixed-integer convex-quadratic BuS model to compute the optimal topology and test it with the non-linear non-convex AC optimal power flow (OPF) simulation to show its AC feasibility and generation cost reduction compared to the AC-OPF simulations. By testing and validating the proposed metrics on test cases of different sizes, we show that they are able to identify busbars that reduce the total generation costs when their topology is optimized. Thus, the metrics enable effective selection of busbars for BuS, with no need to test every busbar in the grid, one at a time.
Generation Expansion Planning is used to obtain future planning decisions that ensure the system reliability while minimizing cost. However, the past decade has seen an increase in weather-dependent faults in the electric grid, higher shares of weather-dependent renewable generation, and increased reliance on weather-dependent dynamic line ratings. To account for those dependencies, we propose a two-stage stochastic generation expansion planning model integrates leverages a spatial weather model to assess the generation and transmission line availability. This includes both variations of renewable generation capacity factors and dynamic line rating, and weather-dependent outage rates. The proposed framework is tested using a synthetic network, demonstrating that dynamic line rating outperforms static line ratings by reducing total operational costs and load shedding.
Much scholarship, public discourse, and policymaking on ‘energy transition materials’ (ETMs) imagines a supply chain from material production to climate mitigation, despite the absence of any mechanism ensuring that mined materials will become an electrification technology, let alone replace fossil fuels or avoid warming impacts. Scholarship about energy transition material (ETM) discourse, including criticism, largely neglects this material-discursive contradiction and truncates final analyses to paradoxes productive toward extractive futures. Leveraging a review of 99 peer-reviewed articles published between 2013-2025, this article brings mining scholarship into conversation with climate knowledge politics. It charts the dematerialization of climate knowledge from warming impacts to values negotiated along three spectra (urgency, scale, and sacrifice) and rematerialization reflected in extractive rationales (developmental, Indigenous rights, nature, and futures). Most ETM discourse is enclosed in three narrative boundaries, which lean on the imagined supply chain and foreclose non-extractive possibilities: (1) The contingencies that link climate policy and mining, such as materials science, technological manufacturing and use, are taken for granted; (2) Scalar formulations separate and compare anthropogenic climate change and mining impacts; and (3) ETMs wield climate knowledge itself as a supply chain, dialectically illustrating a problem to its own solution. Pathways to overcome the boundaries can center the materiality of mining and climate change over the abstractions of supply chains and imagined climate benefits, critically reflect on the relationships between material production and technopolitical knowledge, and take seriously actually occurring alternative livelihoods.
This paper presents an interaction-enhanced Lane-Changing Early Warning (LCEW) system designed to issue reliable early warning signals through a spatial risk-propagation-aware approach. The system is built upon interpretable multi-vehicle trajectory predictions and collision risk identification within an extended LC zone, including multiple downstream and upstream vehicles. We first investigate the stochasticity and hazardousness of LCs, characterized by (i) variable-size multi-vehicle interactions within the LC zone and (ii) the resulting risk propagation from downstream to upstream traffic. To model these stochastic interactions, a Social Spatio-Temporal Graph Convolutional Neural Network framework informed by mutual information (social STGCNN-MI) is introduced to predict multi-vehicle trajectories. By leveraging a mutual information-based kernel function, the framework enhances trajectory prediction accuracy while providing interpretable representations of vehicle interactions. Then, oriented bounding box detection is employed on the predicted trajectories to identify direct collision risks between the LC vehicle and adjacent vehicles and indirect risks among non-adjacent vehicles. These components jointly quantify and characterize how risks originating from downstream interactions influence LC behavior and subsequently propagate to upstream traffic. Finally, awareness of this spatial risk propagation enables the generation of an early warning signal that informs the LC decision-making with potential collision locations within the predicted time window. Traffic simulation experiments conducted in SUMO demonstrate that the proposed LCEW effectively improves vehicle-level safety and comfort, while maintaining system-level traffic safety and efficiency.
AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not sufficiently difficult to meaningfully measure frontier models. To this end, we present Terminal-Bench 2.0: a carefully curated hard benchmark composed of 89 tasks in computer terminal environments inspired by problems from real workflows. Each task features a unique environment, human-written solution, and comprehensive tests for verification. We show that frontier models and agents score less than 65% on the benchmark and conduct an error analysis to identify areas for model and agent improvement. We publish the dataset and evaluation harness to assist developers and researchers in future work at tbench.ai.
