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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 paper discusses the modeling and the simulation of the limiters of proportional integral (PI) controllers included in voltage-sourced converters. Seven PI controller models are considered. These include windup and anti-windup limiters, back calculation with different gains and delayed feedback and the model recommended by the IEEE standard 421.5-2016. The dynamic behavior and numerical issues arising for each PI implementation are thoroughly compared by means of a detailed all-island dynamic model of the Irish system including the HVDC East–West Interconnector and a simplified dynamic model of the Great Britain system; and the Nordic system with inclusion of a STATCOM device. Recommendations on the computer-based implementation of each model are provided in the concluding remarks. [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]

Segmenting anatomical structures in medical images has been successfully addressed with deep learning methods for a range of applications. However, this success is heavily dependent on the quality of the image that is being segmented. A commonly neglected point in the medical image analysis community is the vast amount of clinical images that have severe image artefacts due to organ motion, movement of the patient and/or image acquisition related issues. In this paper, we discuss the implications of image motion artefacts on cardiac MR segmentation and compare a variety of approaches for jointly correcting for artefacts and segmenting the cardiac cavity. The method is based on our recently developed joint artefact detection and reconstruction method, which reconstructs high quality MR images from k-space using a joint loss function and essentially converts the artefact correction task to an under-sampled image reconstruction task by enforcing a data consistency term. In this paper, we propose to use a segmentation network coupled with this in an end-to-end framework. Our training optimises three different tasks: 1) image artefact detection, 2) artefact correction and 3) image segmentation. We train the reconstruction network to automatically correct for motion-related artefacts using synthetically corrupted cardiac MR k-space data and uncorrected reconstructed images. Using a test set of 500 2D+time cine MR acquisitions from the UK Biobank data set, we achieve demonstrably good image quality and high segmentation accuracy in the presence of synthetic motion artefacts. We showcase better performance compared to various image correction architectures. [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]

Phase imbalance in the U.K. and European low-voltage (415 V, LV) distribution networks causes additional energy losses. A key barrier against understanding the imbalance-induced energy losses is the absence of high-resolution time-series data for LV networks. It remains a challenge to estimate imbalance-induced energy losses in LV networks that only have the yearly average currents of the three phases. To address this insufficient data challenge, this paper proposes a new customized statistical approach, named as the clustering, classification, and range estimation (CCRE) approach. It finds a match between the network with only the yearly average phase currents (the data-scarce network) and a cluster of networks with time series of phase current data (data-rich networks). Then, CCRE performs a range estimation of the imbalance-induced energy loss for the cluster of data-rich networks that resemble the data-scarce network. Chebyshev's inequality is applied to narrow down this range, which represents the confidence interval of the imbalance-induced energy loss for the data-scarce network. Case studies reveal that, given such a few data from the data-scarce networks, more than 80% of these networks are classified to the correct clusters and the confidence of the imbalance-induced energy loss estimation is 89%. [ABSTRACT FROM AUTHOR]

This paper empirically explores the organizational processes at the onset of disruptions and the factors that determine different configurations of responses. It examines how processes of response, both before and in the aftermath of a disruption, support the building and development of organizational resilience. Using case study data from three U.K. based organizations, this paper makes the following three contributions. First, it identifies the common elements involved in decision making at the onset of a disruption, and explains the iterative stages and processes that led to the development of resilience. It explains the criticality and relationships between the elements of detection, activation, and response. Second, this paper explains why responses vary from one situation to another, by identifying two dimensions that determine the configurations of organizational resilience, namely Preparedness and Adaption. Third, this paper presents the resilience configurations matrix that gives rise to and establishes four distinct types of organizational configurations, which are Process Based, Resourceful, At High Risk, and Resilience Focused. This paper concludes by discussing the implications for theory and practice of resilience. [ABSTRACT FROM AUTHOR]

The undergoing transition from conventional to converter-interfaced renewable generation leads to significant challenges in maintaining frequency stability due to declining system inertia. In this paper, a novel control framework for Synthetic Inertia (SI) provision from Wind Turbines (WTs) is proposed, which eliminates the secondary frequency dip and allows the dynamics of SI from WTs to be analytically integrated into the system frequency dynamics. Furthermore, analytical system frequency constraints with SI provision from WTs are developed and incorporated into a stochastic system scheduling model, which enables the provision of SI from WTs to be dynamically optimized on a system level. Several case studies are carried out on a Great Britain 2030 power system with different penetration levels of wind generation and inclusion of frequency response requirements in order to assess the performance of the proposed model and analyze the influence of the improved SI control scheme on the potential secondary frequency dip. The results demonstrate that the inclusion of SI provision from WTs into Unit Commitment (UC) can drastically impact the overall system costs. [ABSTRACT FROM AUTHOR]

In this paper, an efficient and accurate imaging algorithm is presented for Ground-Based Synthetic Aperture Radar (GB-SAR) or other radar systems that could be formed by a physical or synthetic linear aperture. The imaging algorithm is based on the fractional Fourier transform (FrFT) for the azimuth compression. A mathematical framework is derived according to the projection of a sample reflectivity image onto the pseudopolar coordinate, and its implementation was presented. With the data acquisition geometry and the pseudopolar imaging coordinate, the phase of a point target can be expressed as a quadratic phase exponential. It makes that only 1-D FrFT is needed for the azimuth compression of the time-domain backscatter data for the GB-SAR imaging problem. By further research, the optimal transformation order that represents the spatial frequency changes by the FrFT was given subsequently. Taking advantage of this optimal representation, the proposed approach avoids the large calculation that occurs in the time-domain back projection (TDBP). Comparing to the far-field pseudopolar format algorithm (FPFA), the accuracy of the proposed algorithm is much improved. Meanwhile, the proposed approach holds almost the same computational cost and complexity as the FPFA. The proposed approach keeps the advantages of the imaging quality of the TDBP and the computational cost of the FPFA that are two important aspects of the GB-SAR applications. Both the numerical simulation and the field GB-SAR experiment show that the algorithm is more suitable for the high-precision GB-SAR imaging, especially for the near field. [ABSTRACT FROM AUTHOR]

The reduced level of system inertia in low-carbon power grids increases the need for alternative frequency services. However, simultaneously optimizing the provision of these services in the scheduling process, subject to significant uncertainty, is a complex task given the challenge of linking the steady-state optimization with frequency dynamics. This paper proposes a novel frequency-constrained stochastic unit commitment model which, for the first time, co-optimizes energy production along with the provision of synchronized and synthetic inertia, enhanced frequency response, primary frequency response and a dynamically-reduced largest power infeed. The contribution of load damping is modeled through a linear inner approximation. The effectiveness of the proposed model is demonstrated through several case studies for Great Britain's 2030 power system, which highlight the synergies and conflicts among alternative frequency services, as well as the significant economic savings and carbon reduction achieved by simultaneously optimizing all these services. [ABSTRACT FROM AUTHOR]

Conservation voltage reduction (CVR) has been traditionally applied adopting moderate settings at primary substations and when distributed generation was uncommon. However, as new infrastructure is deployed across European-style medium voltage (MV) and low voltage (LV) networks, driven by increasing photovoltaic (PV) penetration levels, the opportunity arises to develop more advanced CVR schemes. This paper proposes a centralized, three-phase AC optimal power flow (OPF)-based CVR scheme that, using monitoring, on-load tap changers and capacitors across MV and LV, actively manages voltages to minimize energy consumption, even with high PV penetration, while considering MV–LV constraints. To tackle scalability issues brought by discrete variables, a two-stage approach is proposed to solve the OPF as a non-linear programming problem (relaxing integer variables). A process that continuously checks customer voltages is adopted to trigger the optimization only when needed. Moreover, CVR benefits are not only quantified at a network level but also for customers, providing useful insights to policy makers. The proposed control is assessed using a realistic, unbalanced U.K. residential MV–LV network (2,400+ customers) with high PV penetration, and 1-min resolution time-varying profiles and load models. Results demonstrate that the proposed control effectively coordinates voltage regulation in MV and LV levels throughout the day, minimizing energy imports for all customers. [ABSTRACT FROM AUTHOR]

The prediction of combined sewer overflow (CSO) operation in urban environments presents a challenging task for water utilities. The operation of CSOs (most often in heavy rainfall conditions) prevents houses and businesses from flooding. However, sometimes, CSOs do not operate as they should, potentially bringing environmental pollution risks. Therefore, CSOs should be appropriately managed by water utilities, highlighting the need for adapted decision support systems. This paper proposes an automated CSO predictive model construction methodology using field monitoring data, as a substitute for the commonly established hydrological-hydraulic modeling approach for time-series prediction of CSO statuses. It is a systematic methodology factoring in all monitored field variables to construct time-series dependencies for CSO statuses. The model construction process is largely automated with little human intervention, and the pertinent variables together with their associated time lags for every CSO are holistically and automatically generated. A fast least absolute shrinkage and selection operator solution generating scheme is proposed to expedite the model construction process, where matrix inversions are effectively eliminated. The whole algorithm works in a stepwise manner, invoking either an incremental or decremental movement for including or excluding one model regressor into, or from, the predictive model at every step. The computational complexity is thereby analyzed with the pseudo code provided. Actual experimental results from both single-step ahead (i.e., 15 min) and multistep ahead predictions are finally produced and analyzed on a U.K. pilot area with various types of monitoring data made available, demonstrating the efficiency and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Cellular network deployment with three sectors per site and a frequency reuse factor of one is key to supporting high data rate applications in LTE Advanced Networks. However, such configurations result in increased inter-cell interference. In this paper we model the effects of inter-cell interference on an LTE-Advanced network at two carrier frequencies. We consider two urban environments and four cell ranges. The effects of inter-cell interference are separated into inter-site interference and inter-sector interference. A site-specific 3D ray-tracing tool is used to model the communication channel between the base stations (serving and interfering links) and the user equipment terminals. The inter-site interference is first modelled in terms of its dominant interference proportion and then validated using previously reported field measurements recorded in London (U.K.). Secondly, the inter-site and inter-sector interference components are modelled as a function of user equipment relative position from the serving base stations. Our approach is compatible with the standardized ITU-R model and our proposal can easily be added to these international models. Our model requires no system layout for the interferers and reduces the required channel generation time by a factor of 12 compared with the system layout method. [ABSTRACT FROM AUTHOR]

In the Great Britain power system, reduced system inertia (particularly during low demand conditions) and larger possible infeed loss would make grid frequency regulation extremely challenging in future. Traditional primary frequency response could be insufficient to limit the frequency variation within acceptable range. This paper shows that thermostatically controlled loads (TCLs) (domestic refrigerators) can be controlled without real-time communication and in a nondisruptive way to collectively enhance the network frequency response. The aggregated power consumption of TCLs, distributed across the system, could be controlled as a ‘linear’ function of the locally measured frequency and its rate of change. Alternatively, their aggregated consumption could be made to follow a ‘pre-set’ power profile depending on the estimated infeed loss. A novel technique for accurate estimation of infeed loss and consequent postfault TCL power reduction is also proposed. The effectiveness of the two TCL control strategies is compared for primary and secondary frequency response through a case study on a 36 busbar reduced equivalent of the Great Britain power system. The effect of spatial variation of transient frequencies and the time delays in frequency measurement and filtering are considered to show how the TCLs can realistically provide rapid frequency response. [ABSTRACT FROM AUTHOR]

Probabilistic flood mapping offers flood managers, decision makers, insurance agencies, and humanitarian relief organizations a useful characterization of uncertainty in flood mapping delineation. Probabilistic flood maps are also of high interest for data assimilation into numerical models. The direct assimilation of probabilistic flood maps into hydrodynamic models would be beneficial because it would eliminate the intermediate step of having to extract water levels first. This paper introduces a probabilistic flood mapping procedure based on synthetic aperture radar (SAR) data. Given a SAR image of backscatter values, we construct a total histogram of backscatter values and decompose this histogram into probability distribution functions of backscatter values associated with flooded (open water) and non-flooded pixels, respectively. These distributions are then used to estimate, for each pixel, its probability of being flooded. The new approach improves on binary SAR-based flood mapping procedures, which do not inform on the uncertainty in the pixel state. The proposed approach is tested using four SAR images from two floodplains, i.e., the Severn River (U.K.) and the Red River (U.S.). In all four test cases, reliability diagrams, with error values ranging from 0.04 to 0.23, indicate a good agreement between the SAR-derived probabilistic flood map and an independently available validation map, which is obtained from aerial photography. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel multi-resolution clustering (MRC) method that for the first time classifies end customers directly from massive, volatile and uncertain smart metering data. It will firstly extract spectral features of load profiles by multi-resolution analysis (MRA), and then cluster and classify these features in the spectral-domain instead of time-domain. The key advantage is that the proposed method will allow a dynamic load profiling to be flexibly re-constructed from each spectral level. MRC addresses three key limitations in time-series based load profiling: i) large sample size: sample size is reduced by a novel two-stage clustering, which firstly clusters each customers' massive daily profiles into several Gaussian mixture models (GMMs) and then clusters all GMMs; ii) volatility: it avoids the interferences between different load features (e.g. magnitude, overall trend, spikes) by decomposing them onto different resolution levels, and then clustering separately; iii) uncertainties: as the GMM can give a probabilistic cluster membership instead of a deterministic one, an additive classification model based on the posterior probability is proposed to reflect the uncertainty between days. The proposed method is implemented on 6369 smart metered customers from Ireland, and compared with the load profiles used by the U.K. industry and traditional K-means clustering. The results show that the developed MRC outperformed the traditional methods in its ability in profiling load for big, volatile and uncertain smart metering data. [ABSTRACT FROM PUBLISHER]

This paper presents a fault tree analysis (FTA) method for the unavailability of supply of gas networks. The method is based on the automatic generation of fault trees, which estimate the probability of disruption of the gas delivery from terminals/storages to the demand nodes. Moreover, it allows probabilistic analyses of the availability of gas supply to individual demand nodes and to the overall gas network. To assess the importance of each network component, the risk achievement worth and the risk reduction worth importance measures are utilized. The developed method can identify weakness in the gas network and can guide expansion planning and maintenance scheduling activities. Furthermore, the developed framework is enriched by steady-state analysis of the gas network operations performed using a physical flow/pressure model. The impact of a component failure on the gas supply interruption at different demand nodes is assessed by the physical model in retrospective to the FTA results. The framework is exemplified with reference to the reduced gas network of Great Britain. The results provide insights to support a robust reliability assessment of the gas network. Moreover, the probability mapping of the most important components in the gas network informs optimal strategies for maintenance scheduling as well for prioritizing improvements of the gas network aimed at significant risk reduction. Remarkably, the failure of components identified as most important according to the FTA importance measures may not have significant effects on the gas supply. A discussion on the range of applicability and limitations of the two methodologies is provided. [ABSTRACT FROM AUTHOR]

Using their fast power reduction capabilities, the induction motor variable speed drives can successfully participate in the power system primary frequency control. A new control strategy for fast frequency support from the diode front-end induction motor variable speed drive is presented in this paper. By this, practical restrictions are considered. In this context, an existing conventional scheme presented in literature is firstly modified by deploying an estimated motor's power losses for a more exactly control design. Next, a novel control scheme is proposed. It attains the fastest frequency support from the drive and simultaneously restricts its minimum power consumption to a given positive value to prevent regeneration mode during the frequency support process. The frequency of applied voltage to the motor is adjusted through a proportional-integrator compensator with deviation of the drive's consumption power from its reference power as input. The reference power is synthesized by permanent and temporary components, which are determined based on deviation and time derivative of the power system's frequency, respectively. The effectiveness of the proposed smart drive for the primary frequency control is investigated using the New England 39 bus and a more realistic Great Britain 36 zone test networks. It improves system frequency response in terms of the rate of change of frequency and the frequency nadir. Furthermore, the new control scheme can contribute to quickly damping inter-area oscillations in multi-machine power systems. [ABSTRACT FROM AUTHOR]

Currently available risk prediction methods are limited in their ability to deal with complex, heterogeneous, and longitudinal data such as that available in primary care records, or in their ability to deal with multiple competing risks. This paper develops a novel deep learning approach that is able to successfully address current limitations of standard statistical approaches such as landmarking and joint modeling. Our approach, which we call Dynamic-DeepHit, flexibly incorporates the available longitudinal data comprising various repeated measurements (rather than only the last available measurements) in order to issue dynamically updated survival predictions for one or multiple competing risk(s). Dynamic-DeepHit learns the time-to-event distributions without the need to make any assumptions about the underlying stochastic models for the longitudinal and the time-to-event processes. Thus, unlike existing works in statistics, our method is able to learn data-driven associations between the longitudinal data and the various associated risks without underlying model specifications. We demonstrate the power of our approach by applying it to a real-world longitudinal dataset from the U.K. Cystic Fibrosis Registry, which includes a heterogeneous cohort of 5883 adult patients with annual follow-ups between 2009 to 2015. The results show that Dynamic-DeepHit provides a drastic improvement in discriminating individual risks of different forms of failures due to cystic fibrosis. Furthermore, our analysis utilizes post-processing statistics that provide clinical insight by measuring the influence of each covariate on risk predictions and the temporal importance of longitudinal measurements, thereby enabling us to identify covariates that are influential for different competing risks. [ABSTRACT FROM AUTHOR]

Hybrid energy storage system (HESS) based on Li-ion and supercapacitor (SC) can play a potential role to stabilize the grid by providing the fast frequency ancillary services. The SC helps to reduce the battery charge/discharge stress and, hence, assists to extend the battery lifespan. The power sharing method (PSM) is the heart in control part to improve the HESS performance and reduce the battery stress. This paper proposes a hybrid PSM and investigates its impact on battery life extension along with its relation to the system design and regulation signal. The performance of hybrid PSM is compared with three other PSMs (low-pass filter, first and second rule based) in a 10 MW/10 MWh full-active parallel HESS for frequency regulation service in two networks: U.K. (national grid) and USA (PJM). Considering maximum possible battery lifetime upto 25 years, result shows that the hybrid PSM approach allows a degree of the better performance for both grid while the sharing of SC is kept maximum 2.0% and 2.5% (for U.S. and U.K. grid, respectively) of the HESS capacity. This study also analyses the impact of PSM on shared capacity and design of HESS for different grid regulation signals. [ABSTRACT FROM AUTHOR]

Electricity demand forecasts are needed for decisions regarding generation dispatch for lead times as short as just a few minutes. Imbalance between generation and demand causes deviation of the system frequency from its target, which in Great Britain is 50 Hz. This, in turn, causes a change in demand, due largely to motor loads. For Great Britain, the change is estimated to be 2.5% of demand per 1 Hz of frequency deviation from its target. This can be used to calculate the demand that would have occurred if frequency had been at 50 Hz. Modeling and forecasting the resulting frequency-corrected demand provides a better basis for dispatching generation. This paper evaluates methods for forecasting frequency-corrected demand up to 10 min ahead. We introduce an exponential smoothing model that, like the system operator's proposed Kalman filter approach, jointly models frequency and demand. We also evaluate a set of univariate methods applied directly to the series of frequency-corrected demand. These methods have not previously been considered for lead times less than 10 min. In our empirical analysis, the best results were produced by a seasonal exponential smoothing method applied directly to the series of frequency-corrected demand. [ABSTRACT FROM AUTHOR]

This paper presents a comparison of two forms of cable connection of a distant offshore wind farm to a transmission system: AC and HVDC. The requirements of relevant industry standards in Great Britain (GB) that drive a connection design and, hence, its costs are highlighted along with an analysis of the ways in which AC cable connections might be made to comply while facilitating the export of active power. Dynamic studies investigating responses to grid-side short-circuit faults show that, in the particular scenarios studied, an AC connection of a wind farm in place of a large synchronous generator is marginally detrimental while an HVDC connection is beneficial. A comparison of costs shows that the crossover distance at which HVDC is cheaper than AC for wind farms of different sizes occurs at longer distances than have hitherto commonly been assumed, and AC connections benefit from reactive compensation not only at the point of common coupling and wind farm end, but also at the connection midpoint. [ABSTRACT FROM AUTHOR]

Phase imbalance widely exists in the UK's low voltage (415 V, LV) distribution networks. The imbalances not only lead to insufficient use of LV network assets but also cause energy losses. They lead to hundreds of millions of British pounds each year in the UK. The cost-benefit analyses of phase balancing solutions remained an unresolved question for the majority of the LV networks. The main challenge is data-scarcity – these networks only have peak current and total energy consumption that are collected once a year. To perform a cost-benefit analysis of phase balancing for data-scarce LV networks, this paper develops a customized cluster-wise Gaussian process regression (CGPR) approach. The approach estimates the total cost of phase imbalance for any data-scarce LV network by extracting knowledge from a set of representative data-rich LV networks and extrapolating the knowledge to any data-scarce network. The imbalance-induced cost is then translated into the benefit from phase balancing and this is compared against the costs of phase balancing solutions, e.g., deploying phase balancers. The developed CGPR approach assists distribution network operators (DNOs) to evaluate the cost-benefit of phase balancing solutions for data-scarce networks without the need to invest in additional monitoring devices. [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]

The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding cannot detect and classify complex deformation signals reliably making providing usable information to a broad range of nonexpert stakeholders a challenge. Here, we explore the applicability of deep learning approaches by adapting a pretrained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. For our proof-of-concept, we focus on the U.K. where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides, and tunneling. The sparsity of measurement points and the presence of spike noise make this a challenging application for deep learning networks, which involve calculations of the spatial convolution between images. Moreover, insufficient ground truth data exist to construct a balanced training data set, and the deformation signals are slower and more localized than in previous applications. We propose three enhancement methods to tackle these problems: 1) spatial interpolation with modified matrix completion; 2) a synthetic training data set based on the characteristics of the real U.K. velocity map; and 3) enhanced overwrapping techniques. Using velocity maps spanning 2015–2019, our framework detects several areas of coal mining subsidence, uplift due to dewatering, slate quarries, landslides, and tunnel engineering works. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems. [ABSTRACT FROM AUTHOR]

This article details with the use of objective sizing techniques for a novel design of a residential solid oxide fuel cell (SOFC) combined cooling, heating, and electrical power system for the U.K. market. The aim of the research is to determine the objective sizing of key parameters relating to the cooling, heating, and power supply and demand, namely the number of cells in the SOFC, the effectiveness of the heat exchangers, and the coefficient of performance of the absorption chillers. These parameters are determined taking into account the aspects of efficiency, economic, and environmental impacts through use of the entropy-weighting approach and gray relationship analysis. The combination of these two approaches will help designers maximize efficiency of energy utilization and minimize emissions and costs of the system being examined. It is envisaged that electrical demand would be met by the fuel cell (FC) stacks, while the most efficient use is made of heat that is generated by the FC through waste heat recovery to satisfy domestic hot water, freezers, space heating, and space cooling. The demand of the conventional electric freezers is innovatively designed to be fulfilled by heat exchangers and absorption chillers to further increase the efficiency of heat energy use. Due to the energy demand characteristics of the U.K. domestic sector, the proposed system structure, objective sizing values, and operation control strategies—supported by MATLAB/Simulink software—are suited to the residential energy demands of a single household. [ABSTRACT FROM AUTHOR]

Software-defined radio (SDR) instruments can be used to replace bulky and expensive spectrum analyzers for RF field measurements. Using the commercial off-the-shelf (COTS) equipment, a low-cost, portable SDR instrument for measuring RF propagation has been created and tested. In the U.K., parts of the very high-frequency (VHF) spectrum have been repurposed for the use of Internet-of-Things devices; the instrument developed here is designed to meet the use case of performing an urban propagation study in VHF and UHF short range devices bands in a fast and low-cost manner. Design of the hardware and software is discussed, as well as the calibration of the instrument. The results of a test propagation study are given for the completed instrument. It is shown that the SDR instrument is capable of performing the study to a high degree of agreement with a commercial spectrum analyzer, thus validating the approach. The readings of the received power taken by the instrument are shown to agree with the readings taken at the same locations with a commercial spectrum analyzer to within an average of 1.4 dB at 71 MHz and 1.1 dB at 869.525 MHz. From the measurements taken, log-distance models were able to be produced with a path-loss exponent of 2.44 and a log-normal shadowing standard deviation of 8.5 dB at 71 MHz, and a path-loss exponent of 4.06 and a log-normal shadowing standard deviation of 8.8 dB at 869.525 MHz. [ABSTRACT FROM AUTHOR]

This article proposes a meta-heuristic optimization-based two-stage residential load pattern clustering (LPC) approach to address two main issues that exist in the most current LPC methods: 1) unreasonable typical load pattern (TLP) extraction; 2) a good clustering should achieve a good balance between the compactness and separation of the formed clusters. However, few clustering algorithms integrate both of these two aspects into the objective function of clustering for consideration. In the first stage, an adaptive density-based spatial clustering of applications with noise (DBSCAN) is proposed to automatically detect the uncommon load curves and obtain the TLP of each individual customer. In the second stage, LPC is formulated as an optimization problem in which clustering validity index (CVI) considering both compactness and separation is used as the objective function. Gravitational search algorithm (GSA) is adopted to solve this optimization problem. Four different CVIs are investigated to find the most appropriate one for LPC. A comparative case study using the real load data from 208 households from the U.K. verified the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The ever-increasing complexity of intelligent electronic devices (IED) for the protection of electrical equipment leads to significant research challenges in evaluating their safety integrity level (SIL). The development of new technologies, components, operation, and maintenance requirements are factors that increase uncertainties and risks associated with the safety, integrity, and reliability of the IEDs. Based on both “the U.K. guidance on assessing the safety integrity of electrical supply protection” and Markov processes, this research proposes a methodology to assess and verify the integrity of protection schemes in industrial and commercial power systems. The average probability of failure on demand (PFDavg) and SIL level, are used to quantify the reliability of protection arrangements for radial distribution systems in accordance with the safety integrity foundations of IEC 61508 and IEC 61511 standards. The methodology is implemented with the IEEE 242-2001 case study, with illustrative results. [ABSTRACT FROM AUTHOR]

Meeting the challenge of realizing fusion power production will require considerable and increasing investment in facilities for testing and development of fusion technology. Particularly important will be testing of components destined for the harsh in-vessel environment of the reactor. To help address this need, the U.K. Government is investing in major new fusion technology facilities, which will offer integrated laboratories covering the complete development life cycle from materials to manufacturing processes and load testing of components. A major part of these facilities shall be a test device named CHIMERA (combined heating and magnetic research apparatus), offering testing under fusion-relevant loads for meter-scale in-vessel component mock-ups. Among the major challenges addressed are electromagnetic loads, high heat flux (HHF), and proving complex and high-risk manufacturing. The ability to test technology in magnetic fields will be unparalleled and could prove vital for breeding blanket designs featuring a ferromagnetic structural material or a liquid metal breeder. The CHIMERA magnet system uses a split-pair NbTi superconducting magnet, combined with a vertical-axis pulsed resistive solenoid to simulate plasma disruptions. Furthermore, in order to provide semi-integrated testing including possible synergistic effects, CHIMERA will enable tests of resilience against magnetic and thermal loads in combination. The heating systems will deliver at least 0.5 MW/m2 at the module surface, HHF in localized areas, and power for volumetric heating of a module. This article introduces the CHIMERA device, reports the motivation and technical basis, describes the system specification, and outlines the future plan. [ABSTRACT FROM AUTHOR]

There is a reduction in the percentage penetration of synchronous machines within the Great Britain (GB) power system leading to a decrease in inertia and an increase in the system rate of change of frequency (RoCoF) resulting from power imbalances. This raises the challenge of containing frequency deviations to within the relevant operational limits. As a result, steps need to be taken by the system operator to manage the risk to system security. In order to better understand this risk, this article presents the challenge in light of the changing energy landscape and the current and future frequency response services available to contain frequency deviations. Although the current GB frequency response services may be capable of containing most events within frequency limits, in low-inertia scenarios, these responses alone are not capable of containing excursions within practical RoCoF limits. Consequently, further action must be taken to ensure system security. The system operator currently employs an interim solution of limiting the largest loss risk, depending on system inertia and the RoCoF limit. While this is suitable in the short term, it is unlikely that this option will be cost-effective in the future. [ABSTRACT FROM AUTHOR]

The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artifacts, such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images; however, this procedure is strongly operator-dependent, cumbersome, and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully automated, and learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation; 2) inter-slice motion detection; 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method—integrating both regression and structured classification models—to extract landmarks and probabilistic segmentation maps from both long- and short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank and on 100 cases from the UK Digital Heart Project and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g., on UK Biobank, sensitivity and specificity, respectively, 88% and 99% for heart coverage estimation and 85% and 95% for motion detection), allowing their exclusion from the analyzed dataset or the triggering of a new acquisition. [ABSTRACT FROM AUTHOR]

The rise of distributed energy resources (DERs) can enhance the efficiency of system operations by providing flexibility services to the different agents involved, but they also pose a major resource allocation problem. This study considers three different agents procuring DER services: distribution system operators (DSOs) for local congestion management, transmission system operators (TSOs) for system-wide reserve deployment, and retailers for hedging against network usage tariffs based upon peak-load pricing. A variety of market mechanisms are identified to co-ordinate these needs, and three schemes are developed in detail. These are separate markets for each agent, co-ordinated Shapley value allocations for TSO and DSO, and a co-ordinated mechanism including retailers. These designs are evaluated on a realistic distribution network in Britain for two operational days. The results show a more efficient dispatch from the TSO–DSO co-ordinated procurement over independent sequential procurements. However, the inclusion of retailers in the joint dispatch is surprisingly less attractive due to the lack of improvement in social welfare and the undesirable impacts on the DSO. [ABSTRACT FROM AUTHOR]