The analysis of grid-forming (GFM) voltage-source inverters (VSIs) depends on small-signal models derived from phasor representation; however, by neglecting the voltage-control dynamics of GFM VSIs, conventional small-signal models cannot accurately capture the system stability boundary, dominant oscillation modes, and power coupling behaviour. To address this limitation, this paper presents a novel voltage-control aggregation method alongside a small-signal model capable of accurately representing the dynamic behaviour of GFM VSIs. The effectiveness of the developed method in replicating the system’s critical dynamics is confirmed by theoretical analysis and experimental results.
Fluid antenna systems (FAS) have emerged as a promising technology to achieve high spatial diversity by dynamically reconfiguring multiple closely spaced N antenna ports. However, the inherent spatial correlation among these ports poses significant challenges for accurate performance analysis. Traditional block-correlation modeling algorithms, which partition the N & times;N Toeplitz-structured correlation matrix into independent D blocks with constant correlation coefficients, often yield substantial approximation errors to block-correlation models, especially in scenarios with limited ports. In this paper, we revisit the spatial block-correlation model for FAS and introduce a novel block-correlation modeling algorithm in tuning the model parameters, which realizes the variable block-correlation model in practice. Our proposed approach derives closed-form expressions for the optimal block-specific correlation coefficients and develops a low-complexity heuristic algorithm that reduces the computational complexity from exponential DN-D to linear (N-D)& times;D searches, thereby achieving significantly lower approximation error compared to constant correlation models. To validate the effectiveness of our variable block-correlation modeling algorithm, we first apply it to point-to-point FAS communications with closely spaced ports, deriving analytical expressions for the joint probability density function (PDF) of channel amplitudes and outage probability. Our analysis shows that the proposed algorithm offers tractable performance evaluation and superior accuracy, particularly when the number of ports is small (N<20 ). Furthermore, we extend our framework to classical FAS-assisted reconfigurable intelligent surface (FAS-RIS) communications, where the interplay between direct and RIS-assisted links complicates the channel statistics. By integrating the central limit theorem (CLT) with variable block-correlation model, we derive tractable outage probability expressions that capture the coupling effects among different channel coefficients. Extensive numerical simulations under various system configurations demonstrate that variable block-correlation model not only outperforms conventional constant-correlation approaches in terms of approximation accuracy and robustness, but also provides reliable performance prediction in threshold-sensitive and challenging propagation environments. These results underscore the practical value of our approach for the design and optimization of next-generation FAS-based wireless networks.
Scenarios serve as a critical tool in climate change analysis, enabling the exploration of future evolution of the climate system, climate impacts, and the human system (including mitigation and adaptation actions). This paper describes the scenario framework for ScenarioMIP as part of CMIP7. The design process has involved various rounds of interaction with the research community and user groups at large. The proposal covers a set of scenarios exploring high levels of climate change (to explore high-end climate risks), medium levels of climate change (anchored to current policy), and low levels of climate change (aligned with current international agreements). These scenarios follow very different trajectories in terms of emissions, with some likely to experience peaks and subsequent declines in greenhouse gas concentrations in this century. An important innovation is that most scenarios are intended to be run, if possible, in emission-driven mode, providing a better representation of the Earth system uncertainty space. The proposal also includes plans for long-term extensions (up to 2500 AD) to study long-term impacts, climate change-related processes on long timescales, and (ir)reversibility. This proposal forms the basis for further implementation of the framework in terms of the derivation of emissions and land use pathways for use by Earth system models and additional variants for adaptation and mitigation studies.
This paper develops an efficient multi-period energy storage system planning (MPEP) framework for renewable-dominated power systems under both normal and severe weather conditions, aiming to enhance investment efficiency and operational security. First, a distributionally robust optimization (DRO)-based MPEP model is formulated that captures the long-term evolution of installed renewable capacity and load demand across the planning horizon, as well as different hurricane tracks and intensities, thereby reducing operational risks and overall costs. Second, to address the modeling difficulties arising from the inherent non-smooth and discontinuous characteristics of wind generation within the DRO-MPEP framework, a year-indexed, wind-speed-driven Wasserstein ambiguity set is constructed, which explicitly captures turbine cut-in and cut-out behavior and yields physically consistent distance metrics. Meanwhile, a moment-based ambiguity set is constructed for characterizing transmission line fault uncertainty during hurricanes. Furthermore, an efficient DRO solution framework is proposed to deal with the large-scale MPEP model, incorporating two acceleration techniques—topology-based scenario pre-screening and low-impact scenario filtering—to control scenario scale and reduce computational burden. Finally, numerical experiments benchmarked against previous models and algorithms demonstrate the effectiveness and superiority of the proposed method.
The rapid expansion of artificial intelligence (AI) infrastructure is driving unprecedented growth in electricity demand from data centers. Traditional power-system planning treats large computing facilities as inflexible peak loads, leading to costly infrastructure upgrades and long delays in grid interconnection. Recent work has shown that AI clusters can reduce electricity consumption during peak demand through software-based workload orchestration. This article explores how modern GPU-based AI data centers can operate as grid-interactive assets that respond dynamically to power system conditions. We describe an architecture integrating grid signals, workload scheduling, and power telemetry for fine-grained cluster power control. Experimental results from a real-world deployment on a 130 kW GPU cluster demonstrate multiple forms of flexibility, including rapid load reduction, sustained curtailment, and carbon-aware operation while preserving service levels for priority jobs. We further demonstrate performance-aware load shifting across geographically distributed clusters, enabling workloads to migrate toward regions with lower grid stress. Together, these capabilities transform AI infrastructure from static electricity consumers into flexible resources that support grid reliability, accelerate interconnection, and improve computing sustainability.
