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With low voltage (LV) distribution networks increasingly being re-purposed beyond their original design specifications to accommodate low carbon technologies, the ability to accurately calculate their actual spare capacity is critical. Traditionally, within the Great Britain (GB) power system, there has been limited monitoring of LV distribution networks, making this difficult. This paper proposes a method for estimating spare capacity of unmonitored LV networks using demand data from customer Smart Meters. In particular, the proposed method infers existing LV network capacity, as well as losses, across scenarios where only a limited number of customers have Smart Meters installed. Typical daily load profiles across customers with Smart Meters are learned using a Dirichlet sampled Gaussian mixture model (GMM). Learned profiles are then applied to all unmetered customers to estimate network parameters. Method accuracy is assessed by comparing estimations with simulated, fully observed, LV network models. The method is also compared to benchmark models for establishing unobserved demand profiles. Overall, results in the paper show that the proposed method outperforms benchmark models in terms of accurately assessing substation headroom, particularly in scenarios where only 10–50% of customers have Smart Meters installed. [ABSTRACT FROM AUTHOR]

The reduced inertia levels in low-carbon power grids necessitate explicit constraints to limit frequency’s nadir and rate of change during scheduling. This can result in significant curtailment of renewable energy due to the minimum generation of thermal plants that are needed to provide frequency response (FR) and inertia. Additional consideration of fast FR, a dynamically reduced largest loss and under frequency load shedding (UFLS) allows frequency security to be achieved more cost effectively. This paper derives a novel constraint from the swing equation to contain the frequency nadir using all of these services. The expected cost of UFLS is found probabilistically to facilitate its comparison to the other frequency services. We demonstrate that this constraint can be accurately and conservatively approximated for moderate UFLS levels with a second order cone (SOC), resulting in highly tractable convex problems. Case studies performed on a Great Britain 2030 system demonstrate that UFLS as an option to contain single plant outages can reduce annual operational costs by up to $\pounds $ 559 m, 52% of frequency security costs. The sensitivity of this value to wind penetration, abundance of alternative frequency services, UFLS amount and cost is explored. [ABSTRACT FROM AUTHOR]

The foreseen development of large-scale Offshore Windfarms (OWFs) further from the shore dictates that the OWF transmission system must be optimally designed based on Dynamic Thermal Rating (DTR) in order to fully utilize the intermittent nature of the wind and to keep the offshore wind cost-competitive. In this paper, a comprehensive, DTR-based, two-stage stochastic model is presented, which has been developed for investment decision support for OWF size and HVAC transmission systems. Complex DTR models for all the critical HV components are made fit for the mixed-integer linear programming problem, while accounting for the stochasticity in wind generation and component availability. The main decisions incorporate the discrete size of OWFs, HV subsea export cable cross-sections and ratings for transformers and shunt reactors. For validation, an actual testcase OWF off the east coast of U.K. has been used. Results indicate that DTR-based iterative design of OWF and its transmission components can significantly improve the business case, even though transmission efficiency and energy delivered are not maximum for the optimal design [ABSTRACT FROM AUTHOR]

DC technology has gained considerable interest in the medium voltage applications due to the benefits over the AC counterpart. However, to utilize the full capacity of this development, the selection of a suitable power electronic converter topology is a key aspect. From the pool of voltage source converters (VSC's), it is unclear which topology is suitable for multi-megawatt applications at medium voltage dc (MVdc) levels. To address this, the paper proposes a selection guideline based on reliability and optimum redundancy levels of VSCs for MVdc applications. This will be combined with other functional factors such as operational efficiency and return-on-investment. Three candidate multi-level topologies namely three-level neutral point clamped converter (3L-NPC), modular multi-level converter (MMC) and cascaded 3L-NPC (which is being used for the first MVdc link in the U.K.) have been evaluated over two-level-VSC from $\pm$ 10 kV to $\pm$ 50 kV. Results show that with the increase of MVdc voltage level MMC shows better performance whereas at low MVdc voltage levels 3L-NPC is the prominent topology. [ABSTRACT FROM AUTHOR]

In Part I of this paper we have introduced the closed-form conditions for guaranteeing regional frequency stability in a power system. Here we propose a methodology to represent these conditions in the form of linear constraints and demonstrate their applicability by implementing them in a generation-scheduling model. This model simultaneously optimises energy production and ancillary services for maintaining frequency stability in the event of a generation outage, by solving a frequency-secured Stochastic Unit Commitment (SUC). We consider the Great Britain system, characterised by two regions that create a non-uniform distribution of inertia: England in the South, where most of the load is located, and Scotland in the North, containing significant wind resources. Through several case studies, it is shown that inertia and frequency response cannot be considered as system-wide magnitudes in power systems that exhibit inter-area oscillations in frequency, as their location in a particular region is key to guarantee stability. In addition, securing against a medium-sized loss in the low-inertia region proves to cause significant wind curtailment, which could be alleviated through reinforced transmission corridors. In this context, the proposed constraints allow to find the optimal volume of ancillary services to be procured in each region. [ABSTRACT FROM AUTHOR]

We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly provided and then incorporated into the training process. By using the differentiable properties of persistent homology, a concept used in topological data analysis, we can specify the desired topology of segmented objects in terms of their Betti numbers and then drive the proposed segmentations to contain the specified topological features. Importantly this process does not require any ground-truth labels, just prior knowledge of the topology of the structure being segmented. We demonstrate our approach in four experiments: one on MNIST image denoising and digit recognition, one on left ventricular myocardium segmentation from magnetic resonance imaging data from the UK Biobank, one on the ACDC public challenge dataset and one on placenta segmentation from 3-D ultrasound. We find that embedding explicit prior knowledge in neural network segmentation tasks is most beneficial when the segmentation task is especially challenging and that it can be used in either a semi-supervised or post-processing context to extract a useful training gradient from images without pixelwise labels. [ABSTRACT FROM AUTHOR]

The Under-Frequency Load Shedding (UFLS) scheme is an emergency measure in place to prevent system collapse in the event of a large generation deficit relative to demand at any moment in time. However, the effectiveness of traditional UFLS schemes may be compromised as network power flows change due to the growth of distributed generation (DG) and a reduction in system inertia. This study has shown some of the issues affecting the UFLS protection system with rising installed capacities of DG in the Great Britain power system. Simulation results show how the effectiveness of the scheme is challenged with current and future expected DG installed capacities and the risk of over shedding demand due to the scheme’s current settings is evaluated. Many proposed improvements to traditional load shedding schemes are based on upgrades to network monitoring and communication systems, which are not yet fully available. This study evaluates more readily available solutions, including relocation of under-frequency relays and reducing the time delay of the scheme, which may provide interim improvements to the UFLS scheme, based on a case study in GB. [ABSTRACT FROM AUTHOR]

The recent advances in Internet of Things (IoT), computational analytics, processing power, and assimilation of Big Data (BD) are playing an important role in revolutionizing maintenance and operations regimes within the wider facilities management (FM) sector. The BD offers the potential for the FM to obtain valuable insights from a large amount of heterogeneous data collected through various sources and IoT allows for the integration of sensors. The aim of this article is to extend the exploratory studies conducted on Big Data analytics (BDA) implementation and empirically test and categorize the associated drivers and challenges. Using exploratory factor analysis (EFA), the researchers aim to bridge the current knowledge gap and highlight the principal factors affecting the BDA implementation. Questionnaires detailing 26 variables are sent to the FM organization in the U.K. who are in the process or have already implemented BDA initiatives within their FM operations. Fifty-two valid responses are analyzed by conducting EFA. The findings suggest that driven by market competition and ambitious sustainability goals, the industry is moving to holistically integrate analytics into its decision making. However, data quality, technological barriers, inadequate preparedness, data management, and governance issues and skill gaps are posing to be significant barriers to the fulfillment of expected opportunities. The findings of this study have important implications for FM businesses that are evaluating the potential of the BDA and IoT applications for their operations. Most importantly, it addresses the role of the BD maturity in FM organizations and its implications for perception of drivers. [ABSTRACT FROM AUTHOR]

Recent long term planning studies have demonstrated the important role of variable renewables (VRE) in decarbonising our energy system. However, cost-optimising models do not capture the visual impact of VREs on the landscape which can act to undermine their public acceptability. Here, we use crowd-sourced scenicness data to derive spatially explicit wind energy capacity potentials for three scenarios of public sensitivity to this visual impact. We then use these scenarios in a cost-optimising model of Great Britain's power system to assess their impact on the cost and design of the electricity system in 2050. Our results show that total system costs can increase by up to 14.2% when public sensitivity to visual impact is high compared to low. It is thus essential for policy makers to consider these cost implications and to find mechanisms to ameliorate the visual impact of onshore wind in local communities. [ABSTRACT FROM AUTHOR]

In recent years, trees in European countries have been increasingly endangered by emerging infectious diseases (EIDs). In the United Kingdom (U.K.), this has been observed to affect whole woodlands and forests, threatening the existence of some types of trees. Although quarantine measures have been taken to limit the spreading of such diseases, this has not yet been effectively controlled leading to millions of trees affected by EIDs. Ground-penetrating radar (GPR) has proven effective in identifying critical features on diseased trees for detection of EIDs spread. However, the irregular shape of tree trunks and their complex internal structure represent real challenges for conventional GPR measurements and data processing methodologies. In this research, a dual-polarized GPR system is used to detect internal decay in tree trunks using novel signal processing methodologies. A polarimetric correlation filter based on Bragg scattering on a 3-D Pauli feature vector and an arc-shaped Kirchhoff migration are discussed in detail. The proposed polarimetric correlation filter is utilized to enhance the signal-to-noise ratio (SNR) of B-scans due to bark and tree trunk high-loss properties of tree trunks. Meanwhile, an arc-shaped Kirchhoff migration algorithm is performed to counteract the influence of the bark irregularity. The proposed data processing framework is successfully validated with measurements on a real tree trunk, where cross sections were subsequently cut for comparison purposes. Outcomes from the proposed methodology demonstrate a high consistency with the features observed on the tree trunk cross sections, indicating the reliability of the proposed detection scheme for assessing tree-decay associated with EIDs. [ABSTRACT FROM AUTHOR]

In August 2020, students took to the streets of London in a mass protest over their A-level results (that determine entry to university), chanting “F*ck the algorithm.” Their A-level grades had been determined by an algorithm that depended on past school performance, a system of teacher ratings, and for smaller student groups, the use of Centre Assessed Grades. Commendably Ofqual (the U.K. Government Office of Qualifications and Examinations Regulation) were transparent in their use of the algorithm so that affected stakeholders were informed of the algorithm’s existence and the key details of how it was developed. The design of the data set produced an algorithm with biased outputs, which hardcoded preexisting educational, policy, and societal biases. Due to the large numbers of students, the single “results day” output and the wider interests of schools, teachers, parents, and universities, the unfairness of the outcome was magnified. Add to this that the students were, by definition, academically proficient individuals, many of whom derived from privileged and supportive backgrounds, which meant that they had the awareness and capacity to challenge. Hence, the impact of this biased algorithm was not received silently and so it was hard to deny, deflect, and dismiss. However, this is not always the case, and many systems are deployed with similar biases and harmful outcomes but the impact is dissipated over time and individuals, and therefore not so immediately obvious and easy to challenge. This is particularly the case when the people impacted do not have the advantages that enabled the A-level students to challenge. [ABSTRACT FROM AUTHOR]

This letter analyzes the difficulty of estimating power system inertia under ambient conditions using the Center-of-Inertia (CoI) system model. We show that the main obstacle to doing this is a difficulty in detecting a peak in the Power Spectral Density (PSD) of the frequency trace. This is due to a combination of two factors: (i) the Ornstein-Uhlenbeck (OU) process with a high mean-reversion time, which models the load disturbance and is mathematically equivalent to a low-pass filter with a high time constant; (ii) the CoI dominant mode is highly damped. This observation also explains why it is possible to estimate system inertia under ambient conditions using wide-area PMU measurements by exploiting information about inter-area oscillations which have lower damping than the dominant mode of the CoI model. We validated those findings by using the PSD of the actual 2-hour frequency trace for Great Britain from 00:00 to 02:00 of January 01, 2019. [ABSTRACT FROM AUTHOR]

The noise equivalent delta temperature (NEDT) determines the radiometric resolution of a radiometer. Determining NEDT is indispensable for assessing the in-orbit radiometer performance and quantifying uncertainty propagation from radiance to climate data records. Agencies of EUMETSAT, UK MetOffice, and National Oceanic and Atmospheric Administration (NOAA) have developed their own algorithms for calculating and monitoring NEDT of in-orbit microwave radiometers. These algorithms are an essential means for monitoring NEDT and hardware health. While remarkable accomplishments have been made, there appears to be room for improvement. The investigation is needed for the NEDT underestimate found at channels like G-band that has pronounced $1/f$ noise. Also, it is necessary to explain the inconsistency in calculated NEDTs of different algorithms. We have reviewed the theoretical basis for determining NEDT and developed an improved algorithm of clear physics and mathematics. We have developed methods for handling error sources that can result in either negative or positive biases with an overall underestimate of NEDT. We have done comparison and validation with the prelaunch thermal vacuum chamber (TVAC) test, in-orbit data, and simulation. The new algorithm significantly improves the estimate of NEDT, including the G-band and advances the understanding of algorithm structures and physical foundations. It can accurately monitor in-orbit NEDT and facilitate quantifying the associated uncertainty propagation through science products. [ABSTRACT FROM AUTHOR]

Due to decarbonisation and decentralisation of energy sectors, the rise of distributed generation (DG) will modify generation and demand patterns at grid supply points (GSPs), where the interface between distribution and transmission systems takes place. With the increasing penetration of such devices, methodologies able to evaluate its contribution to both local distribution network and transmission system become crucial. This article proposes an analytical method to evaluate the DG locational incremental contribution (LIC) to the interface with the transmission grid. Accordingly, the original model of the U.K. engineering recommendation P2 is enhanced by studying the impacts from high-voltage distribution networks (i.e., ≤ 132 kV) under normal and contingent conditions. This approach enables a more accurate network security assessment, especially when considering contingencies such as distribution system faults. To illustrate the proposed method, the original P2 model is compared against different enhanced approaches on three basic distribution networks, followed by a case study and a sensitivity analysis on a revised IEEE 14-bus GSP system. The proposed LIC method produces results that assess a wider range of conditions including DG penetration, concentration, and system reliability. Furthermore, it provides an increased DG visibility for transmission planning and operation. [ABSTRACT FROM AUTHOR]

Data-driven condition monitoring reduces downtime of wind turbines and increases reliability. Wind turbine operation and maintenance (O&M) cost is a significant factor that calls for automated fault detection systems in wind turbines. In this manuscript, the anomaly detection problem for wind turbine gearbox is formulated based on adaptive threshold and twin support vector machine (TWSVM). In this work, SCADA data from wind farms located in the U.K. is considered with samples from twelve months before failure, and from one month before failure. Gearbox oil and bearing temperatures are used as two univariate time-series for analyzing adaptive threshold. The effectiveness of the proposed method is compared with standard classifiers like support vector machines (SVM), k-nearest neighbors (KNN), multi-layer perceptron neural network (MLPNN), and decision tree (DT). Anomaly detection of wind turbine gearbox using TWSVM and adaptive threshold results in an accurate performance, thus increasing the reliability. The missed failure and false positive rate that indicate the proposed methodology’s ability is also investigated to discriminate between false alarms, and comparison with previous studies shows superior performance. [ABSTRACT FROM AUTHOR]

Distribution companies are increasingly turning to active network management to improve how networks perform with respect to different metrics or objectives. This includes Conservation Voltage Reduction (CVR) that seeks to minimize energy usage of consumers (mostly connected to low voltage, LV, levels) by reducing their supply voltage. However, if medium voltage (MV) levels have to be reduced to achieve CVR, it can negatively affect the energy losses in MV networks. In this study, we explore the potential trade-off that may exist between reducing energy consumption of LV networks and the potential increase in energy losses in MV networks. A Pareto Particle Swarm Optimization (PPSO) is used to actively control voltage devices, including on-load tap changer-fitted LV transformers and MV capacitors. Using three realistically modelled radially operated UK MV/LV networks serving 37,000+ customers (94% residential, 6% commercial and industrial), our results show that a trade-off does exist, but it can be reduced with increasing network control flexibility, particularly with controllable LV transformers. Furthermore, for these networks the reduction in energy consumption due to CVR can be 20 times larger than the increase seen in MV energy losses. These insights are useful for distribution companies in assessing the overall benefits of CVR with active voltage management despite small increases in MV energy losses. [ABSTRACT FROM AUTHOR]

Capital cost is a practical limitation in distributed energy resources (DER) system sizing and the optimal size may lead to high capital investment. This calls for the development of a framework that provides alternate system sizing with the ability to address capital cost constraints. A framework for sizing microgrid DER considering capital cost constraints and inclusion of manufacturer catalog ratings is introduced in this article. This framework uses a system-wide, composite, cost metric that combines capital costs and operating costs per month. This per-month optimization results are transformed to evaluate the feasible DER ratings and annual savings possible with imposed capital and operating cost constraints. The formulated framework is demonstrated using two microgrid case studies in peak shaving applications at industrial facilities located in the U.K. and USA. This article also introduces development of hardware-in-loop (HIL) system as a tool to validate microgrid sizing results demonstrating that real-time operation can provide the savings projected by the optimized assets. The HIL simulation, with a microgrid controller and the plant sized per the optimization verifies the annual savings. This HIL confirmed that the sizing did not result in violations of state of charge limits of battery energy storage. Traditional optimal sizing approaches do not verify for violations and can result in suboptimal or oversized designs. [ABSTRACT FROM AUTHOR]

Globally gridded sea surface temperatures (SSTs) provide key data for long-term monitoring of the stability of satellite data. Despite apparent limitations, accurate hyperspectral data can provide useful independent information to critique the stability of global SST products on the annual-to-decadal time scale. We compared data from atmospheric infrared sounder (AIRS) on EOS Aqua and Crosstrack Interferometer Sounder (CrIS) on SNPP with the SST products from NOAA/NESDIS [real time global (RTG)], the Canadian Meteorological Centre (CMC), and the U.K. Met Office [Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA)]. For the 2013–2019 period, the overall standard deviation of the difference between AIRS and the RTG was 0.55 K, with an increasing trend over time. In contrast, the standard deviation of the difference between the AIRS, the CMC, and the OSTIA dropped steadily to below 0.4 K, a level previously seen only in SST products relative to independent buoy data. Unexplained biases between the observed and the gridded SSTs at the 100-mK level are consistent with the already existing estimates of the AIRS and CrIS absolute calibration accuracy. However, the AIRS and CrIS observations both show artifacts in all three SST products, increasing with distance from the equator, with the CMC artifacts being the smallest. Even with the CMC, a trend of 4 mK per year relative to AIRS and CrIS was observed between 2013 and 2019 for the 30S–30N oceans. Investigation of the underlying causes of the observed discrepancies requires further work. [ABSTRACT FROM AUTHOR]

Any reliable model for scheduling optical space-to-ground communication must factor in cloud cover conditions due to attenuation of the laser beam by water droplets in the clouds. In this article, we provide two alternative models of uncertainty for cloud cover predictions: a robust optimization model with a polyhedral uncertainty set and a distributionally robust optimization model with a moment-based ambiguity set. We computationally analyze their performance over a realistic communication system with one satellite and a network of ground stations located in the U.K. The models are solved to schedule satellite operations for six months utilizing cloud cover predictions from official weather forecasts. We found that the presented formulations with the treatment of uncertainty outperform in the long-term models, in which uncertainty is ignored. Both treatments of uncertainty exhibit similar performance. Nonetheless, the novel variant with the polyhedral uncertainty set is considerably faster to solve. [ABSTRACT FROM AUTHOR]