Demonstrating Almost Linear Time Complexity of Bus Admittance Matrix-Based Distribution Network Power Flow: An Empirical Approach

The bus admittance matrix is central to many power system simulation algorithms, but the link between problem size and computation time (i.e., the time complexity) using modern sparse solvers is not fully understood. It has recently been suggested that some popular algorithms used in distribution system power flow analysis have cubic complexity, based on properties of dense matrix numerical algorithms; a tighter theoretical estimate of complexity using sparse solvers is not immediately forthcoming due to these solvers' problem-dependent behaviour. To address this, the time complexity of admittance matrix-based distribution power flow is considered empirically across a library of 75 networks, ranging in size from 50 to 300,000 nodes. Results across four admittance matrix-based methods suggest complexity coefficient values between 1.04 and 1.12, indicating complexity that is instead almost linear. The proposed empirical approach is suggested as a convenient and practical way of benchmarking the scalability of power flow algorithms

Hybrid European MV-LV Network Models for Smart Distribution Network Modelling

A pair of European-style, integrated MV-LV circuits are presented, created by combining generic MV and real LV networks. The two models have 86,000 and 113,000 nodes, and are made readily available for download in the OpenDSS file format. Primary substation tap change controls and MV-LV feeders are represented as three-phase unbalanced distribution network models, capturing the coupling of voltages at the MV level. The assumptions made in constructing the models are outlined, including a preconditioning step that reduces the number of nodes by more than five times without affecting the solution. Two flexibility-based case studies are presented, with TSO-DSO and peer-peer-based smart controls considered. The demonstration of the heterogeneous nature of these systems is corroborated by the analysis of measured LV voltage data. The models are intended to aid the development of algorithms for maximising the benefits of smart devices within the context of whole energy systems

Smart Energy Network Digital Twins: Findings from a UK-Based Demonstrator Project

Digital Twins promise to deliver a step-change in distribution system operations and planning, but there are few real-world examples that explore the challenges of combining imperfect model and measurement data, and then use these as the basis for subsequent analysis. In this work we propose a Digital Twin framework for electrical distribution systems and implement that framework on the Smart Energy Network Demonstrator microgrid in the UK. The data and software implementation are made available open-source, and consist of a network model, power meter measurements, and unbalanced power flow-based algorithms. Measurement and network uncertainties are shown to have a substantial impact on the quality of Digital Twin outputs. The potential benefits of a dynamic export limit and voltage control are estimated using the Digital Twin, using simulated measurements to address data quality challenges, with results showing curtailment for an exemplar day could be reduced by 56%. Power meter data and a network model are shown to be necessary for developing algorithms that enable decision-making that is robust to real-world uncertainties, with possibilities and challenges of Digital Twin development clearly demonstrated

Hourly historical and near-future weather and climate variables for energy system modelling

Energy systems are becoming increasingly exposed to the impacts of weather and climate due to the uptake of renewable generation and the electrification of the heat and transport sectors. The need for high-quality meteorological data to manage present and near-future risks is urgent. This paper provides a comprehensive set of multi-decadal, time series of hourly meteorological variables and weather-dependent power system components for use in the energy systems modelling community. Despite the growing interest in the impacts of climate variability and climate change on energy systems over the last decade, it remains rare for multi-decadal simulations of meteorological data to be used within detailed simulations. This is partly due to computational constraints, but also due to technical barriers limiting the use of meteorological data by non-specialists. This paper presents a new European-level dataset which can be used to investigate the impacts of climate variability and climate change on multiple aspects of near-future energy systems. The datasets correspond to a suite of well-documented, easy-to-use, self-consistent, hourly- and nationally aggregated, and sub-national time series for 2 m temperature, 10 m wind speed, 100 m wind speed, surface solar irradiance, wind power capacity factor, solar power factor, and degree days spanning over 30 European countries. This dataset is available for the historical period 1950–2020 and is accessible from https://doi.org/10.17864/1947.000321 (Bloomfield and Brayshaw, 2021a). As well as this a companion dataset is created where the ERA5 reanalysis is adjusted to represent the impacts of near-term climate change (centred on the year 2035) based on five high-resolution climate model simulations. These data are available for a 70-year period for central and northern Europe. The data are accessible from https://doi.org/10.17864/1947.000331 (Bloomfield and Brayshaw, 2021b). To the authors’ knowledge, this is the first time a comprehensive set of high-quality hourly time series relating to future climate projections has been published, which is specifically designed to support the energy sector. The purpose of this paper is to detail the methods required for processing the climate model data and illustrate the importance of accounting for climate variability and climate change within energy system modelling from the sub-national to European scale. While this study is therefore not intended to be an exhaustive analysis of climate impacts, it is hoped that publishing these data will promote greater use of climate data within energy system modelling.</p

β-Ga₂O₃ in Power Electronics Converters:Opportunities &amp; Challenges

In this work, the possibility of using different generations of β-Ga2O3 as an ultra-wide-bandgap power semiconductor device for high power converter applications is explored. The competitiveness of β-Ga2O3 for power converters in still not well quantified, for which the major determining factors are the on-state resistance, RON, reverse blocking voltage, VBR, and the thermal resistance, Rth. We have used the best reported device specifications from literature, both in terms of reports of experimental measurements and potential demonstrated by computer-aided designs, to study power converter performance for different device generations. Modular multilevel converter-based voltage source converters are identified as a topology with significant potential to exploit these device characteristics. The performance of MVDC &amp; HVDC converters based on this topology have been analysed, focusing on system level power losses and case temperature rise at the device level. Comparisons of these β-Ga2O3 devices are made against contemporary SiC-FET and Si-IGBTs. The results have indicated that although the early β-Ga2O3 devices are not competitive to incumbent Si-IGBT and SiC-FET modules, the latest experimental measurements on NiOX / β-Ga2O3 and β-Ga2O3 /diamond significantly surpass the performance of incumbent modules. Furthermore, parameters derived from semiconductor-level simulations indicate that the β-Ga2O3 /diamond in superjunction structures delivers even superior performance in these power converters

Prehospital randomised assessment of a mechanical compression device in cardiac arrest (PaRAMeDIC) trial protocol

Background Survival after out-of-hospital cardiac arrest is closely linked to the quality of CPR, but in real life, resuscitation during pre-hospital care and ambulance transport is often suboptimal. Mechanical chest compression devices deliver consistent chest compressions, are not prone to fatigue and could potentially overcome some of the limitations of manual chest compression. However, there is no high-quality evidence that they improve clinical outcomes, or that they are cost effective. The Pre-hospital Randomised Assessment of a Mechanical Compression Device In Cardiac Arrest (PARAMEDIC) trial is a pragmatic cluster randomised study of the LUCAS-2 device in adult patients with non-traumatic out-of-hospital cardiac arrest. Methods The primary objective of this trial is to evaluate the effect of chest compression using LUCAS-2 on mortality at 30 days post out-of-hospital cardiac arrest, compared with manual chest compression. Secondary objectives of the study are to evaluate the effects of LUCAS-2 on survival to 12 months, cognitive and quality of life outcomes and cost-effectiveness. Methods: Ambulance service vehicles will be randomised to either manual compression (control) or LUCAS arms. Adult patients in out-of-hospital cardiac arrest, attended by a trial vehicle will be eligible for inclusion. Patients with traumatic cardiac arrest or who are pregnant will be excluded. The trial will recruit approximately 4000 patients from England, Wales and Scotland. A waiver of initial consent has been approved by the Research Ethics Committees. Consent will be sought from survivors for participation in the follow-up phase. Conclusion The trial will assess the clinical and cost effectiveness of the LUCAS-2 mechanical chest compression device. Trial Registration: The trial is registered on the International Standard Randomised Controlled Trial Number Registry (ISRCTN08233942)

Comparative analysis of services from soft open points using cost–benefit analysis

Soft Open Points (SOPs) are power electronic-based devices which can replace Normally Open Points (NOPs) in distribution networks. They can improve network performance by enabling controllable power transfer between adjacent feeders. This flexible meshing can provide a wide range of services, including loss reduction, reduced renewables curtailment, improved reliability, reinforcement deferral, or enabling flexibility services. This paper proposes a novel framework, based on the Cost–Benefit Analysis methodology, to quantify and compare the cost-effectiveness of SOPs for providing each of these five value streams. The framework includes the development of mathematical models that encapsulate the key variables that drive competitive SOP use cases, as well as providing detailed analysis to determine quantitative estimates for each of the parameters. Results suggest that, whilst all services could be cost-effective, that reinforcement deferral and reduced DG curtailment are most likely to find wide usage. It is also suggested that the fast response time of SOPs as compared to conventional NOPs is unlikely to be a viable value proposition for improving reliability via conventional loss of load metrics such as energy not supplied. A detailed case study demonstrates that in marginal cases, where a SOP has a similar system net benefit compared to Business-as-Usual, that all services need to be considered rather than just single value streams in isolation. It is concluded from the research that there are multiple potential competitive applications for SOPs in future distribution networks.</p