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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]

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]

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]

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]

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]

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]

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]

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 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 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]

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]

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]

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]

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]

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]

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]