Imperial College London, legally the Imperial College of Science, Technology and Medicine, is a public research university in London. Imperial grew out of Prince Albert's vision for an area of culture, including the Royal Albert Hall, Victoria & Albert Museum, Natural History Museum, and several Royal Colleges. In 1907, Imperial College was established by royal charter, unifying the Royal College of Science, Royal School of Mines, and City and Guilds of London Institute. In 1988, the Imperial College School of Medicine was formed by merging with St Mary's Hospital Medical School. In 2004, Queen Elizabeth II opened the Imperial College Business School.Imperial focuses exclusively on science, technology, medicine, and business, although students can take humanities courses through their "Horizons" programme. The main campus is located in South Kensington, and there is an innovation campus in White City. Facilities also include teaching hospitals throughout London, and a research field station at Silwood Park. The college was formerly a member of the University of London, becoming independent on its centenary. The college is a major centre for medical teaching and research and together with Imperial College Healthcare NHS Trust forms an academic health science centre. Imperial has a highly international community with more than 59% of students from outside the UK, and 140 countries represented on campus.
The widely accepted definition of grid-forming (GFM) inverter states that it should behave as a (nearly) constant voltage source behind an impedance by maintaining a (nearly) constant internal voltage phasor in the sub-transient to transient time frame. Some system operators further mandate permissible ranges for this effective impedance. However, these specifications do not clearly define the location of the internal voltage source, and no systematic method exists to quantify its effective impedance for a black-box GFM model. To address this, we first compare the transient responses of an ideal voltage source and a GFM to show that an idealistic GFM maintains a (nearly) constant voltage across the filter capacitor, rather than at the inverter switches. Then we propose a systematic method to quantify the effective impedance of a GFM from its black-box model using frequency-domain admittance plots. Using standard PSCAD GFM models developed by NLR (formerly NREL), we demonstrate that the GFM’s equivalent impedance model captures the sub-transient response and static voltage stability limit accurately. Further, replacing the GFM with the proposed equivalent circuit model in the modified IEEE-39 bus system is shown to reproduce the small-signal stability characteristics with reasonable accuracy.
Power systems are being reshaped by decarbonization, digitalization, and high shares of renewables. At the same time, increasingly severe extreme conditions expose the limits of traditional reliability frameworks, calling for risk-aware, resilience-oriented approaches to address high-impact, low-probability (HILP) events. In this context, this paper presents a comprehensive overview of the foundations of power system resilience. It revisits the transition from reliability to resilience, formalizes key concepts and metrics, and introduces advanced approaches for resilience assessment, including fragility-based modeling, cascading failure analysis, and tail-risk indicators. The paper further examines resilience-oriented investment planning, operational strategies across all event phases, and the role of distributed energy resources, microgrids, and cybersecurity. The analysis highlights that resilience extends reliability by focusing on extreme conditions, fundamentally reshaping decision-making and requiring coordinated strategies across infrastructure, operation, and governance.
Network congestion often hinders the deployment of reserves needed to balance forecast errors during real-time operations. A pertinent idea to tackle this challenge involves adding deployment scenarios of spatial distributions of forecast errors as contingencies to the day-ahead problem. However, current approaches disregard the effect of grid characteristics and the day-ahead schedule on the induced congestion and, consequently, reserve deliverability. In this work, we formulate a two-stage adaptive robust optimization problem to jointly consider interactions between day-ahead and real-time operations and forecast errors. Using a column-and-constraint algorithm, we iteratively construct deployment scenarios by finding the worst-case forecast error for reserve deliverability. Simulations on the RTS-GMLC system show that adding these scenarios to the day-ahead problem significantly reduces the frequency of congestion-driven reserve undeliverability. Notably, the choice and number of scenarios dynamically adapts to the day-ahead schedule.
Managing municipal solid waste in rapidly urbanizing Sub-Saharan Africa remains challenging due to dispersed informal dumping and limited high-resolution datasets for spatial monitoring. We present an open-access deep learning model for automated detection of openly dumped dispersed MSW (ODD-MSW) via crowdsourced UAV imagery, trained and evaluated across 29 regions in 10 Sub-Saharan African countries, encompassing diverse environmental contexts. A deep learning model trained on manually annotated image tiles achieved an overall accuracy of 92.87% (F1 = 0.927) in detecting ODD-MSW across all study regions. Applied without any site-specific retraining to 25 independent OpenAerialMap scenes from 21 cities in 19 countries on five continents, the frozen model retained an F1 score of 0.867, providing direct evidence of transferability beyond the training domain. Predicted distributions reveal heterogeneous accumulation patterns, ranging from localized hotspots - often along waterways, where waste can exacerbate flood and public health risks - to more dispersed litter across urban areas. Waste accumulation is most strongly associated with population density (Spearman rho = 0.674, p < 0.001 ) and indicators of lack of local infrastructure access ( rho = 0.700, p < 0.001 ), whereas its relationship with broader measures of regional development is weaker and not statistically significant ( rho = 0.215, p = 0.26 ), highlighting the importance of fine-scale data for understanding localized waste dynamics. By releasing the model, this study provides a ready-to-use tool for UAV imagery collected by municipalities and local mapping communities, enabling ODD-MSW monitoring without extensive technical expertise. This approach empowers local practitioners to convert UAV imagery into actionable insights, supporting targeted interventions and improved municipal solid waste management across Sub-Saharan Africa.
We extend earlier international efforts to optimise hexahedral-based spectral element methods on GPUs and vectorised CPUs to mixed element meshes additionally involving prismatic, pyramidic, and tetrahedral shapes using tensorial expansions. We demonstrate that common finite element operators (such as the mass and Helmholtz matrices) benefit from alternative implementation strategies depending on the element shape, choice of polynomial order, and system architecture in order to achieve optimal performance. In addition, we introduce a new approach/interpretation to efficiently evaluate more complex operations involving inner products with the derivative of the expansions as part of the integrand such as the stiffness matrix. This approach seeks to maximise operations using the collocation properties of the nodal tensorial expansion associated with classical quadrature rules. Our GPU performance tests demonstrate that the throughput of the Helmholtz operator on tetrahedral elements is at most 2.5 times slower than on hexahedral elements, despite tetrahedra having a factor of six greater quadrature points per degrees of freedom.
