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.
Measurements of 2-furfuraldehyde (2-FAL) in oil are routinely used to predict paper condition in mineral oil filled transformers. Since the level of 2-FAL in oil is influenced by moisture, the hydrophobicity of the oil, aging conditions and the presence of other materials, adjustments may be required before this methodology can be applied to ester filled transformers. The variables that influence the relationship between 2-FAL and degree of polymerization (DP) were investigated through thermal aging of oil/paper systems across four transformer insulating liquids; a synthetic ester (SE), a natural ester (NE), an ester based biofluid (BF) and a mineral oil (MO). Whilst a copper catalyst had a negligible effect, more 2-FAL was found in SE/paper systems and less in NE/paper systems compared with MO/paper systems. 2-FAL levels were depleted following long-term storage whilst the use of wetter paper had the opposite effect. With minor adjustments, correlations between 2-FAL and DP established for mineral oil filled transformers can be applied to ester filled plant.
The increasing complexity and dynamic nature of regulatory frameworks in energy systems demand efficient, accurate, and timely compliance reporting. Traditional manual processes are no longer sufficient to meet these evolving requirements, necessitating the development of automated solutions. This paper presents a comprehensive framework for AI-enabled compliance automation designed to facilitate real-time regulatory reporting in energy infrastructures. By integrating machine learning and natural language processing, the framework interprets complex regulatory texts, processes heterogeneous operational data streams, and generates compliant reports with minimal latency. The proposed model architecture emphasizes robust data acquisition, preprocessing, scalable AI workflows, and real-time data processing mechanisms, ensuring adaptability to regulatory changes and operational demands. The findings demonstrate that AI-driven automation enhances reporting accuracy, reduces operational burdens, and strengthens transparency and accountability within energy markets. The study also identifies future research directions, including model interpretability, cybersecurity, and multi-jurisdictional compliance, underscoring the critical role of AI in advancing sustainable and resilient energy system governance.
As climate change intensifies, extreme weather events increasingly threaten communities and infrastructure. These shocks can act as a form of creative destruction, generating opportunities for sustainable reconstruction, while also reshaping risk perceptions and influencing investment in green technologies. This paper empirically examines how natural disasters and weather extremes affect the diffusion of green buildings across the United States. Using a panel data set of buildings certified under the U.S. Green Building Council's Leadership in Energy and Environmental Design (LEED) program at the city level from 2000 to 2020, we find that major flooding events increase the number of LEED-certified buildings within three to six years. Hurricanes initially reduce green building development, followed by a rebound in later years. We also find that extreme hot days are associated with increased green building adoption, whereas the effects of cold days and moderately hot days are limited and less consistent. These response patterns are driven primarily by private buildings rather than public properties. Overall, our findings highlight the heterogeneous and dynamic ways in which climate shocks shape green building adoption and provide insights for policymakers and planners seeking to promote sustainable urban development in a changing climate.
National Grid Electricity Transmission (NGET) has committed to deliver electricity safely, reliably and efficiently to custom ers and communities while working towards a clean energy future. NGET has developed a new way of connecting customers to the electricity transmission network quicker and more cheaply than before. Demand to connect has soared in the past year, and Grid Parks are an innovation that allows NGET to connect several customers to the same connection point. The first Grid Park at Sundon substation in Bedfordshire has been built to facilitate the connection of three customers at once to a new supergrid transformer, via a new 33kV substation. This Grid Park substation has been commissioned, and two customers have been successfully connected. This gives the capacity these customers require, using NGET resources on a single project, rather than alternatively repeating the same process three times giving the most economic and efficient means of connecting. National Grid (UK) electricity transmission system assets are protected in accordance with the requirements of the National Grid Protection Application and Setting Policy-PS(T)010. This policy does not include connection assets at 33kV level. Therefore, a policy-PS(T)-137 was initially developed outlining the protection application and settings to be applied for single connections to 13kV tertiary windings of Supergrid Transformers (SGT) with the user connected via a 13/33kV transformer. As the Grid Park connection concept emerged, there was a need to extend the policy of protection application to Grid Park substations. This paper will present a pragmatic approach for the protection and control philosophy for Grid Park connections which has been adopted by National Grid. This enables the connection of upto three users to a 33kV bar via a 400/33kV, 150MVA SGT. Each user has a maximum capacity of 49. 99MVA. This paper will describe the complexity of design, protection application, grid code compliance harmonic considerations and commissioning aspects. Sundon GRID PARK SUBSTATION aerial view is shown in the full paper.
