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This paper presents an analysis of the August 9th 2019 GB transmission system frequency event which saw 1.1 million customers disconnected from the electricity system. Using publicly available data and dynamic system simulation, a detailed event timeline is presented that very closely matches the observed system response on the day. This allows for an independent assessment of system operation and response services during the incident, and of various uncertainties around what happened. A number of alternative scenarios are modelled that show how better response provision or better adherence of distributed generation to the latest protection setting guidance could have avoided the disconnection of demand. A thorough discussion of the key lessons that can be learned from the system event is given. This identifies issues for multiple stakeholders to address including the Electricity System Operator, Distribution Network Operators, generator owners and regulators.

This paper presents a feasibility study of an offshore wind farm (OWF) HVDC integration system using high-temperature superconductor (HTS) DC cables. A representative 100kV/2GW point-to-point OWF HVDC system is proposed including HTS DC cables and two converter stations using modular multilevel converters (MMCs). To be compatible with the high current rating of the HTS cables, each of the offshore and onshore converter stations consists of three MMCs in parallel. To study the interaction between HTS DC cables and MMCs, a multiple lumped -section model of a HTS DC cable considering electrical and thermal functionality is developed. This paper provides a critical assessment of the proposed HVDC-HTS system, with emphasis on the performance under fast DC fault transients. Detailed simulations presented in this paper reveal that the HVDC-HTS system provides effective current limiting against DC cable short circuit faults.

The battery electric vehicle (EV) market is in a state of continuing rapid evolution, both in terms of the battery capacities and charger power ratings that vehicle manufacturers are bringing to the market and the increasingly widespread penetration of charging infrastructure. As a result, individuals’ charging behaviours may change substantially and be different from what has been expected or observed to date. This could have a significant effect on the resulting electrical demand from EV charging. Aside from these technical parameters, the demographics of the population served by any given network – and how that might affect their travel habits, particularly car use – must be considered. In this paper, statistical analysis of a large travel survey dataset is carried out to support the hypothesis that car use is likely to vary according to population demographics. Car-based travel diaries disaggregated on key demographic traits of the drivers are assigned to vehicles in the network according to Census data pertaining to those same demographic traits. Charging schedules are derived from these travel diaries for different battery capacities, charger power ratings and level of access to charging to investigate the likely effect of changing these parameters on the resulting charging behaviour and electricity demand. It is found that out of the key emerging patterns identified in the evolving EV market, increasing battery capacities and the establishment of more widespread charging opportunities may reduce the peak demand from EV charging or shift it to a time less likely to coincide with peak domestic demand, hence making it easier for the network to cope with increasing penetrations of EVs. On the other hand, increasing charging power may increase the peak and bring it closer to a time where it is more likely to coincide with peak domestic demand.

A model of the European electricity system is used to analyse the impact of increasing interconnection levels between Great Britain and its European neighbours. Scenarios are assessed for 2020, 2025 and 2030. Several policy questions are highlighted around the assessment criteria for approving interconnector projects and the viability of existing investment and remuneration models. Results show that a GB specific carbon tax in 2020 contributes to a relatively high cost of energy and therefore high imports into GB. Increasing GB interconnection capacity in 2020 from 5 GW to 8.4 GW facilitates additional imports and brings down the annual cost of electricity for GB consumers by €639m. Analysis for 2025 and 2030 shows that additional interconnectors, the removal of the additional GB carbon tax and changes to the generation background lead to GB experiencing increased interconnector exports and reduced interconnector utilisation. This lessens or reverses the impact of new GB interconnection on GB consumer prices. It also leads to a significant reduction in revenue potential for future interconnectors. Carbon emission analysis indicates that inconsistencies in carbon pricing across different countries, like the extra GB price support, can lead to perverse outcomes with reduced GB emissions but increased total European emissions.

The liberalisation of electricity markets and increasing penetration of renewable generation are encouraging trading opportunities to be identified and developed. In Europe, the importance of interconnection is well recognised. Interconnections are particularly important for islanded nations, like Great Britain, which provides access to generation and demand across the national boundaries via sub-sea HVDC cables while providing means to share reserves enhancing the security of supply and reducing overall costs. There are plans in place to significantly increase the current 5 GW of interconnection capacity between GB and neighbouring European countries. The paper investigates the expected impacts that a large increase in interconnection capacity could have on key electricity market parameters such as marginal prices, carbon emissions and the nature of utilisation of existing and future GB interconnections. Several scenarios have been considered to model future uncertainty for the years 2020 and 2025.

This study examines the heterogeneity of UK residential heat demand and how this diversity, along with social demographic and dwelling characteristic diversity, impacts the value case for heat pumps (HPs). The marginal abatement cost (MAC) of HPs is highly sensitive to the level of heat demand and technology assumptions. Care must be taken when interpreting the results from models with a high degree of aggregation. For similar dwellings, heat demand typically becomes lower for demographic groups that have higher levels of deprivation. For similar dwellings and demographics, households using natural gas typically have double the end-use heat demands of households with electric storage heaters. Therefore, if access to heating with similar costs to that of natural gas-fired heating is gained, the direct rebound effect suggests that the heat demands of households heated with electric storage heaters could double, particularly for households that have relatively high proportions of energy expenditure. Heating technology and building efficiency support mechanisms need to simultaneously address the wider goal of decarbonisation while reducing fuel poverty, and to incorporate measures of demand diversity into future assessment of heat policy that recognises how this rebound may oppose decarbonisation efforts but enable improvements in comfort, welfare and health standards.

This paper presents an investigation of the potential for coordinated charging of electric vehicles to i) reduce the CO2 emissions associated with their charging by selectively charging when grid carbon intensity (gCO2/kWh) is low and ii) absorb excess wind generation in times when it would otherwise be curtailed. A method of scheduling charge events that seeks the minimum carbon intensity of charging while respecting EV and network constraints is presented via a time-coupled linearised optimal power flow formulation, based on plugging-in periods derived from a large travel dataset. Schedules are derived using real half-hourly grid intensity data; if charging in a particular event can be done entirely through use of renewable energy that would otherwise have been curtailed, its carbon intensity is zero. It was found that if ‘dumb’ charged from the current UK mainland (GB) grid, average emissions related to electric vehicle (EV) charging are in the range 35–56 gCO2/km; this can be reduced to 28–40 gCO2/km by controlled charging – approximately 20–30% of the tailpipe emissions of an average new petrol or diesel car sold in Europe. There is potential for EVs to absorb excess wind generation; based on the modelled charging behaviour, 500,000 EVs (20% of Scotland's current car fleet) could absorb around three quarters of curtailment at Scotland's largest onshore wind farm.

‘Destination charging’ – in which drivers charge their battery electric vehicles (EVs) while parked at amenities such as supermarkets, shopping centres, gyms and cinemas – has the potential to accelerate EV uptake. This study presents a Monte Carlo-based method for the characterisation of EV destination charging at these locations based on smartphone users' anonymised positional data captured in the Google Maps Popular Times feature. Unlike the use of household and travel surveys, from which most academic works on the subject are based, these data represent individuals' actual movements rather than how they might recall or divulge them. Through a fleet EV charging approach proposed in this study, likely electrical demand profiles for EV destination charging at different amenities are presented. Use of the method is presented first for a generic characterisation of EV charging in the car parks of gyms, based on a sample of over 2000 gyms in around major UK cities, and second for a specific characterisation of hypothetical EV charging infrastructure installed at a large UK shopping centre to investigate the impact of varying the grid and converter capacity on the expected charging demand and level of service provision to the vehicles charging there.

An approach is demonstrated to visualise overhead line failure rates and estimated wind power output during extreme wind events on transmission networks. Reanalysis data is combined with network data and line failure models to illustrate spatially resolved line failure probability with data corrected for asset altitude and exposure. Wind output is estimated using a corrected power curve to account for high speed shutdown with wind speed corrected for altitude. Case studies demonstrate these methods' application on representations of real networks of different scales. The proposed methods allow users to determine at-risk regions of overhead line networks and to estimate the impact on wind power output. Such techniques could equally be applied to forecasted weather conditions to aid in resilience planning. The methods are shown to be particularly sensitive to the weather data used, especially when modelling risk on overhead lines, but are still shown to be useful as an indicative representation of system risk. The techniques also provide a more robust method of representing weather-related failure rates on lines considerate of the altitude, voltage level, and their varying exposure to weather conditions than current techniques typically provide, which can be used to usefully represent failure probability of lines during storms.

This paper investigates the impact of generator dispatch (dictated by cost) and locational aspects related to renewable generation connection and consequent disconnection of synchronous generation, on transient stability. As conventional synchronous generation (SG) is displaced by power electronic converter interfaced generation (PEC), the transient behaviour of the power system may change significantly due both to the changed power flows resulting from the different locations of renewables compared with thermal plant and the different dynamic characteristics of PEC. This paper uses an AC Optimal Power Flow (OPF) to determine credible, minimum cost dispatches of generation in a test network with increasing penetration of PEC in different locations. It is found that the critical locations of the system can vary significantly with respect to economic dispatch, location of disconnection of SG and location of PEC, highlighting the increasing temporal and spatial change in system dynamic behavior.

This paper presents a quantitative investigation of the inconvenience of electric vehicle (EV) charging relative to internal combustion engine vehicle (ICEV) fuelling in terms of the time penalty likely to be experienced by drivers. A heuristic approach to deriving idealised charging schedules from over 39,000 week-long travel diaries from the UK National Travel Survey is presented in order to quantify the expected convenience parity — the point at which EV charging and ICEV fuelling are of comparable convenience — for combinations of battery capacity, charger power and access to charging at different locations (home, workplace and public destinations). It was found that although the majority — up to 95% — of individuals who can charge at home are expected to be able to reach convenience parity with battery sizes currently available in EV models at the ‘affordable’ end of the market, this is significantly less likely for those who rely on workplace or public charging — and particularly for those who must rely solely on en route charging. These individuals are expected to suffer considerable inconvenience associated with EV charging relative to ICEV fuelling, and although greater battery capacities and charger power ratings are expected to lessen this inconvenience, there remains a significant gap in the convenience of EV ownership between those who can charge while parked at home and those who cannot. Further analysis is carried out to long journeys that cannot be made on a single charge; ‘range anxiety’ being a major obstacle to widespread EV adoption. It was found that if drivers are compliant with the UK Highway Code in taking regular breaks on long journeys, fewer than 0.01% of trips are expected to be delayed by charging when using battery capacities of 40–60 kWh.

This chapter addresses the power system of future several decades ahead from the perspective of system planning, economics, and asset management: The Paris Agreement on climate protection will likely lead to renewable energy sources dominating electricity systems; decarbonization of the entire energy system with sector coupling and electrification of heating and transport; the strong effect of the energy transition on citizens and stakeholder relationships; continental-scale interconnections and microgrids; and the balance between the goals of reliability, economics, and the environment. Eight key themes are identified as enablers of the evolutions needed: 1. Joint data and processes are needed across transmission and distribution, across asset management and planning, and increasingly across all energy sectors. 2. Price signals will likely need to become higher resolution in time, location and system services, and need to be modeled in system planning now. 3. Scarcity pricing and eventually appliance-specific price elasticity become the basis of highly reliable, renewables-dominated systems characterized by frequent very low energy prices and occasional price spikes, and planning must anticipate that in its modeling before it becomes reality. 4. Improved consistency of price and cost signals across energy sectors can help to achieve 2050 climate neutrality with less controversy and economic inefficiency. 5. Engagement and understanding of customers, market/system participants and stakeholders is crucial to being able to implement the system plans. 6. Planning will likely move from target networks in transmission and distribution via consideration of multiple time steps toward decision tree concepts and stochastic optimization. 7. Big data, improving computational and algorithmic capabilities, and data hubs, with data privacy-controlled access to data for market actors, improve asset management, forecasting, and planning. 8. The global goal of limiting climate change to 1.5° but also other economic, social, environmental, and reliability criteria will be fully incorporated into system planning and asset management through multi-criteria cost-benefit analysis approaches.

The use of thermal modelling to increase the permissible load-carrying capability of distribution system transformers is attracting increasing interest. Many reported approaches calculate the rating of transformers in real time in response to system conditions. In this study, an experiment to validate and tune the parameters of such a thermal model is described, and explained how the results have been used to inform the inclusion of model-based seasonal ‘enhanced’ ratings in the network planning process.

This paper presents the development of models of a real distribution network from Geographical Information System (GIS) data provided by the Distribution Network Operator (DNO) of an area that is likely to see significant electric vehicle (EV) uptake. Using UK Census data with building analysis from Ordnance Survey datasets, likely existing domestic load profiles and likely locations of EV charge points are established. 12 months of data from the 215-vehicle My Electric Avenue EV trial are used to simulate the temporal variation of EV charging for various levels of EV uptake and power flow studies are run to examine the probable impact of EV uptake on a real distribution network in a suburban residential area in Scotland. It is shown that several parts of the network are expected to be faced with severe issues when 70% of vehicles in the area are replaced by EVs. The method presented is general and can be applied to any distribution system for which data is obtained to provide valuable insight as to the network issues that are likely to arise as a result of the uptake of EVs.

It has been argued that increasing transmission network capacity is vital to ensuring the full utilisation of renewables in Europe. The significant wind generation capacity proposed for the North Sea combined with high penetrations of other intermittent renewables across Europe has raised interest in different approaches to connecting offshore wind that might increase also interconnectivity between regions in a cost effective way. These analyses to assess a number of putative North Sea networks confirm that greater interconnection capacity between regions increases the utilisation of offshore wind energy, reducing curtailed wind energy by up to 9TWh in 2030 based on 61GW of installed capacity, and facilitating a reduction in annual generation costs of more than €0.5bn. However, at 2013 fuel and carbon prices, such additional network capacity allows cheaper high carbon generation to displace more expensive lower carbon plant, increasing coal generation by as much as 24TWh and thereby increasing CO2 emissions. The results are sensitive to the generation "merit order" and a sufficiently high price would yield up to 28% decrease emissions depending on the network case. It is inferred that carbon pricing may impact not only generation investment but also the benefits associated with network development.

In order to strengthen the onshore transmission network in many parts of the world, voltage-source current–high-voltage direct current (VSC–HVDC) will increasingly be utilized. The effect of the operating point of a VSC-HVDC link and the control strategies employed can substantially affect the electromechanical oscillatory behavior of the ac network as well as the dc-side dynamics. In order for the full, flexible capability of VSC-HVDC to be exploited, further study of the effects of these controllers and their interactions with ac system responses is necessary. This paper addresses this gap. Both modal analysis and transient stability analysis are used to highlight tradeoffs between candidate VSC-HVDC power controllers and to study the electromechanical performance of the integrated ac/dc model. Tests are carried out on both a generic two-area model and a large-scale realistic network with detailed ac generator and HVDC models.

This paper outlines possibilities for connecting 2.4 GW of power from two separate wind farms at Dogger Bank in the North Sea to the GB transmission system in Great Britain. Three options based on HVDC with Voltage Source Converters (VSC HVDC) are investigated: two separate point-to-point connections, a four-terminal multi-terminal network and a four-terminal network with the addition of an AC auxiliary cable between the two wind farms. Each option is investigated in terms of investment cost, controllability and reliability against expected fault scenarios. The paper concludes that a VSC-HVDC point-to-point connection is the cheapest option in terms of capital cost and has the additional advantage that it uses technology that is commercially available. However, while multi-terminal connections are more expensive to build it is found that they can offer significant advantages over point to point systems in terms of security of supply and so could offer better value for money overall. A multi-terminal option with an auxiliary AC connection between wind farms is found to be lower cost than a full multi-terminal DC grid option although the latter network would offer ability to operate at greater connection distances between substations.

This paper details a methodology for investigating the reliability of different offshore grid design options for the connection of offshore wind power to shore. The methodology uses a sequential Monte Carlo-based technique that enables the investigation of realistic offshore phenomena, such as the weather dependency of component repair times. A number of case studies are examined for the connection to shore of a cluster of far offshore wind farms and a cost benefit analysis is performed which compares the capital costs, electrical losses, and reliability of each. There is found to be a clear value in options that have inherent redundancy and alternative protection strategies, which avoid the use of expensive dc circuit breakers, that are shown to be potentially viable. It is also found that low probability, high impact faults, such as transmission branch failures, are a key driver behind overall grid reliability.

Not all Electric Vehicle (EV) charging in future will take place at drivers' homes or on-street; at least some will take place at fast-charging 'forecourts' analogous to today's petrol stations. This paper presents a Monte Carlo (MC)-based method for the characterization of the likely demand profile of EV fast charging forecourts based on activity profiles of existing petrol stations, derived from smartphone users' anonymised positional data captured in the `Popular Times' feature in Google Maps. Unlike most academic works on the subject to date which rely on vehicle users' responses to surveys, these data represent individuals' actual movement patterns rather than how they might recall or divulge them. Other inputs to the model are generated from probability distributions derived from EV statistics in the UK and existing academic work. A queuing model is developed to simulate busy periods at charging forecourts. The output from the model is a set of expected time series of electrical demand for an EV forecourt and statistical analysis of the variation in results. Finally, a method is presented for the probabilistic evaluation of the combined loading of an EV forecourt and existing demand; this could be used to assess the sufficiency of existing network capacity and the potential for innovative smart grid technologies to facilitate increasing penetration of EVs.

A large share of renewable generation raises some unique challenges and is forcing change in the operation and planning of electricity transmission systems. In this paper, using real transmission network data representing a part of the Belgian network, the need for building a large set of credible future operating conditions to identify and validate system development decisions is demonstrated. The paper argues that the traditional approach of checking system development options on a few selected operating conditions is no longer adequate in a power system with high penetration of variable generation. An application of a clustering algorithm is demonstrated to build a representative set of operating conditions for system development that not only gives better representation of future operation, but also provides a system development planner with greater confidence with respect to investments that are likely to be necessary. The paper describes the way in which operability of the future system is evaluated and possible need for reinforcement is identified. A discussion is presented of the meeting of demand under initially intact network conditions and while facilitating maintenance outages securely.

Conventional approaches to forecasting of real-time thermal ratings (RTTRs) provide only single-point estimates with no indication of the size or distribution of possible errors. This paper describes weather-based methods to estimate probabilistic RTTR forecasts for overhead lines which can be used by a system operator within a chosen risk policy with respect to the probability of a rating being exceeded. Predictive centers of weather conditions are estimated as a sum of residuals predicted by a suitable auto-regressive model and temporal trends fitted by Fourier series. Conditional heteroscedasticity of the predictive distribution is modelled as a linear function of recent changes in residuals within one hour for air temperature and wind speed or concentration of recent wind direction observations within two hours. A technique of minimum continuous ranked probability score estimation is used to estimate predictive distributions. Numerous RTTRs for a particular span are generated by a combination of the Monte Carlo method where weather inputs are randomly sampled from the modelled predictive distributions at a particular future moment and a thermal model of overhead conductors. Kernel density estimation is then used to smooth and estimate the percentiles of RTTR forecasts which are then compared with actual ratings and discussed alongside practical issues around the use of RTTR forecasts.

This paper presents an investigation into the potential benefits of interconnecting adjacent 33 kV demand groups in the GB distribution network by presentation of two case studies. Results presented are, firstly, a comparison of load profiles of adjacent groups and, secondly, following application of a series of credible future scenarios, the potential reduction in loss of load and generation curtailment achievable from interconnection and the proportion of time for which interconnection would be utilised. It was found that there is significant dissimilarity between load profiles of the adjacent groups analysed and interconnection could be valuable for the future distribution system. The value of interconnection could be increased with the use of storage, though more analysis is needed to quantify the economic viability of this.

The European Single Market for Electricity aims to promote trade and competition in electricity generation across the EU, with investment signals for new generation capacity and interconnection coming from zonal electricity prices reflecting scarcity value. However, a growing number of EU Member States have implemented – or are in the process of developing – national Capacity Mechanisms in order to ensure future security of supply within their own borders, which may distort the cross-border trade of energy across interconnectors and reduce total welfare. This local view of energy security is in response to internal technical and economic constraints and a perceived inability of cross-border electricity flows to be a reliable source of capacity at times of maximum stress, in favour of self-sufficient approaches. A number of routes are available to resolve this conflict through a mechanism to permit cross-border participation of generators in local Capacity Mechanisms, but this requires resolution of a number of complicating factors, not least a means for properly allocating transmission capacity without introducing further distortions to the energy market. Alternative solutions could be enacted at an EU-level, such as through the alignment of Capacity Mechanisms to a common model, or the introduction of an EU-wide single Capacity Mechanism, but the current regulatory focus appears to remain on resolution of such issues at a national level.

The growth of wind power production in the electricity portfolio is striving to meet ambitious targets set, for example by the EU, to reduce greenhouse gas emissions by 20% by 2020. Huge investments are now being made in new offshore wind farms around UK coastal waters that will have a major impact on the GB electrical supply. Representations of the UK wind field in syntheses which capture the inherent structure and correlations between different locations including offshore sites are required. Here, Vector Auto-Regressive (VAR) models are presented and extended in a novel way to incorporate offshore time series from a pan-European meteorological model called COSMO, with onshore wind speeds from the MIDAS dataset provided by the British Atmospheric Data Centre. Forecasting ability onshore is shown to be improved with the inclusion of the offshore sites with improvements of up to 25% in RMS error at 6 h ahead. In addition, the VAR model is used to synthesise time series of wind at each offshore site, which are then used to estimate wind farm capacity factors at the sites in question. These are then compared with estimates of capacity factors derived from the work of Hawkins et al. (2011). A good degree of agreement is established indicating that this synthesis tool should be useful in power system impact studies.

This paper presents a novel active power flow management (PFM) method for managing multiple distributed generator (DG) units connected to medium voltage distribution networks. The method uses the artificial intelligence technique of constraint programming to autonomously manage DG real power outputs and offers flexible and network agnostic characteristics. The method is assessed using multiple scenarios on two real case study networks to examine simulated closed-loop control actions under certain thermal excursions. The test cases are explored with algorithms implemented, in software, on commercially available substation computing hardware to identify computation timescales and investigate algorithm robustness when presented with measurement error. The archival value of this paper is in the specification and evaluation of a novel application of the constraint programming technique for online control of DG in thermally constrained distribution networks.

This paper proposes a new breed of high-voltage dc (HVDC) transmission systems based on a hybrid multilevel voltage source converter (VSC) with ac-side cascaded H-bridge cells. The proposed HVDC system offers the operational flexibility of VSC-based systems in terms of active and reactive power control, black-start capability, in addition to improved ac fault ride-through capability and the unique feature of current-limiting capability during dc side faults. Additionally, it offers features such as smaller footprint and a larger active and reactive power capability curve than existing VSC-based HVDC systems, including those using modular multilevel converters. To illustrate the feasibility of the proposed HVDC system, this paper assesses its dynamic performance during steady-state and network alterations, including its response to ac and dc side faults.

In this paper we examine potential improvements in how load and generation forecast uncertainty is captured when setting reserve levels in power systems with significant renewable generation penetration and discuss the merit of proposed new methods in this area. One important difference between methods is whether reserves are defined based on the marginal distribution of forecast errors, as calculated from historic data, or whether the conditional distribution, specific to the time at which reserves are being scheduled, is used. This paper is a review of published current practice in markets which are at the leading edge of this problem, summarizing their experiences, and aligning it with academic modeling work. We conclude that the ultimate goal for all markets expected to manage high levels of renewable generation should be a reserve setting mechanism which utilizes the best understanding of meteorological uncertainties combined with traditional models of uncertainty arising from forced outages.

This paper presents two methods for evaluating and optimizing the configuration of a distribution network. A new loss-optimization method is described which partitions, optimizes and then recombines the network topology to identify the lowest loss configurations available. A reliability evaluation method is presented which evaluates, on a load-by-load basis, the most effective restoration path and the associated time. In contrast to previously-reported methods, the operation of different types of switch is integrated into this approach, reducing dependency on pre-determined restoration times for each load each fault location. This provides a more accurate estimate of the outage durations through identification of the specific restoration method for each load under each fault condition. The optimization method applied is shown to be effective in identifying optimally-reliable network topologies. Significant benefits are shown to be available.

This paper presents the Scottish Electricity Dispatch Model (SEDM), a cost minimization power system optimization model designed as a tool to investigate potential development trajectories of the Great Britain (GB) power system with particular focus on the Scottish region. Results of SEDM studies investigating the impact of low carbon policies on carbon emissions, generation portfolios and system costs are described. Two potential policies are investigated: carbon pricing and emissions limits, compared to a base scenario with neither of these policies included, but featuring financial support for low carbon generation in line with current GB policy. Model results suggest that carbon emissions could be reduced by up to 95.6% by 2050 with strict emissions limits, achieved largely through an eighteen fold increase in renewable generation capacity. This results in an increase in system costs (including carbon pricing costs) of up to 23.4% compared to the base case, in which carbon emissions increase by 130.8% from present day to 2050. This suggests current policies will be insufficient to achieve a reduction in carbon emissions to the level suggested by the UK’s Committee on Climate Change (CCC).

This paper aims to describe methods to determine short term probabilistic forecasts of weather conditions experienced at overhead lines (OHLs) in order to predict percentiles of dynamic line ratings of OHLs which can be used by a system operator within a chosen risk policy with respect to probability of a rating being exceeded. Predictive probability distributions of air temperature, wind speed and direction are assumed to be normal, truncated normal and von Mises respectively. Predictive centres are estimated as a sum of residuals predicted by a univariate auto-regressive model or a vector auto-regressive model and temporal trends fitted by a Fourier series. Conditional heteroscedasticity of the predictive distribution is modelled as a linear function of recent changes in residuals within one hour for air temperature and wind speed or concentration of recent wind direction observations within two hours. Parameters of the probabilistic models are determined to minimize the average value of continuous ranked probability score which is a summary indicator to assess performance of probabilistic models. The conditionally heteroscedastic models are shown to have appropriate sharpness and better calibration than the respective homoscedastic models.

There is growing recognition that decarbonisation of existing uses of electricity is only ‘part of the story’ and that closer attention needs to be given to demand for energy in heating or cooling and in transport, and to all the energy vectors and infrastructures that supply the end-use demand. In this respect, concepts such as ‘multi-energy systems’ (MES) have been put forward and are gaining increasing momentum, with the aim of identifying how multiple energy systems that have been traditionally operated, planned and regulated in independent silos can be integrated to improve their collective technical, economic, and environmental performance. This paper addresses the need for modelling of MES which is capable of assessing interactions between different sectors and the energy vectors they are concerned with, so as to bring out the benefits and potential unforeseen or undesired drawbacks arising from energy systems integration. Drivers for MES modelling and the needs of different users of models are discussed, along with some of the practicalities of such modelling, including the choices to be made in respect of spatial and temporal dimensions, what these models might be used to quantify, and how they may be framed mathematically. Examples of existing MES models and tools and their capabilities, as well as of studies in which such models have been used in the authors' own research, are provided to illustrate the general concepts discussed. Finally, challenges, opportunities and recommendations are summarised for the engagement of modellers in developing a new range of analytical capabilities that are needed to deal with the complexity of MES.

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.

This paper describes a study conducted to address the question of how much power transfer capacity should be provided on a meshed transmission system to maintain reliability of supply to consumers in the presence of wind generation. A simulation methodology is presented that includes modeling of the available wind generation in the longer term across a quite large area taking into account correlations in available power between different locations in that area. A description is given of the results obtained in characterization of the relationship between the peak load in an area, the total generation capacity in that area, the proportion of it that is wind generation, and how much transmission import capability- “interconnection reserve” -is required for a given reliability of supply. Finally, a number of issues faced by power system investment planners are discussed and pointers given to further work to enable transmission utilities to meet the challenges presented by policy makers in respect of development of wind power to meet renewable energy targets.

Cascading outages can cause large blackouts, and a variety of methods are emerging to study this challenging topic. The Task Force on Understanding, Prediction, Mitigation, and Restoration of Cascading Failures, under the IEEE PES Computer Analytical Methods Subcommittee (CAMS), seeks to consolidate and review the progress of the field towards methods and tools of assessing the risk of cascading failure. This paper discusses the challenges of cascading failure and summarizes a variety of state-of-the-art analysis and simulation methods, including analyzing observed data, and simulations relying on various probabilistic, deterministic, approximate, and heuristic approaches. Limitations to the interpretation and application of analytical results are highlighted, and directions and challenges for future developments are discussed.

The power industry in many parts of the industrialized world faces major challenges over the next two decades to renew ageing networks, accommodate new generation, especially renewables, and make grid operation “smarter”. This requires increasing numbers of professionals, but in many places, the industry faces great losses in personnel and experience as senior engineers retire. As a consequence, the industry needs to recruit a new generation of well-qualified electrical engineering graduates. This paper describes one particular initiative to attract school leavers into engineering and electric power engineering in particular: the U.K.'s “Power Academy”. The main features of the scheme and its success to date are described along with the new challenges and various issues that remain to be addressed. These include the need for “engineering leadership” and career progression for technical specialists. It is argued that companies' continued investment in schemes like the Power Academy is essential to meet the challenges faced by the power industry and to ensure a healthy future supply of professional engineers.

Scientific research to characterize the long-term wind energy resource is plentiful. However, if the impact of wind power on the electric power system is the goal of modeling, consideration must be given to diurnal and seasonal effects, as well as the correlation of wind speed between geographical areas. This paper provides such detail by modeling these effects explicitly, enabling accurate evaluations of wind power impact on future power systems to be carried out. This is increasingly important in the context of ambitious wind energy targets driven in the U.K., for example, by the requirement for 20% of Europe's energy to be met from renewable energy sources by 2020. Both univariate and multivariate auto-regressive models are presented here and it is shown how they can be applied to geographically dispersed wind speed data. These models are applied to suitably de-trended data. The accuracy of the models is assessed both by inspection of the residuals and by assessment of the forecasting accuracy of the models. Finally, it is shown how the models can be used to synthesize wind speed and thus wind power time series with the correct seasonal, diurnal, and spatial diversity characteristics.

Purpose – The purpose of this paper is to develop a model for overcoming the key barriers to the implementation of supply chain integration systems by small engineering to order (ETO) companies.Design/methodology/approach – Qualitative data from three in‐depth case studies are collected. The three case studies are small UK ETO companies, i.e. manufacturing companies that customise generic product types to meet specific customer requirements and only make these to order.Findings – The paper identifies three key barriers: management awareness of the benefits and implementation challenges of these systems, perceived risk to the business and to information security and intensity of skills needed for their successful implementation. The paper proposes an iterative model that aims at overcoming these barriers.Research limitations/implications – The authors apply the knowledge on technology adoption in the context of small ETO companies to explain the apparent lack of implementation of supply chain integration s...

This paper aims to describe methods to forecast wind speeds experienced around overhead lines (OHLs) in order to predict the wind cooling effect and thus the dynamic line ratings (DLRs) of OHLs. The wind speed at a particular OHL span is forecast through a kriging interpolation between the wind speed predictions produced by a vector auto-regressive (VAR) model for a limited number of weather stations at which observations have been obtained. A temporal de-trending method is used to ensure the stationarity of de-trended data from which model parameters are determined. A spatial de-trending method is adopted in a kriging model. The results show that the kriging model performs better than the inverse distance weighting (IDW) method and that the spatial de-trending makes the main contribution to the accuracy of interpolation. Furthermore, the VAR forecasting model is shown to give greater improvement over persistence than a simple auto-regressive (AR) model.

The increase of non-synchronous generation sources on a power network are changing the system dynamics and this can lead to problems with stability. One of the contributions to the change in system dynamics is the variation in system inertia. By approximating a traditional stability metric, the critical clearing time (CCT) in an energetic framework we conduct a parametric investigation of the effect of inertia on power system stability. A set of solid three phase to ground faults are considered on a small but not trivial power system, the two-machine infinite bus network. The performance of the approximated CCT is compared to the true CCT.

A background of increasing uncertainty in all time horizons of power system planning and operation has prompted the development of a comprehensive methodology and an advanced new practical tool for assessing both the static and dynamic security of a real network facing a large number of uncertainties. This paper describes this development, the steps of a study methodology and the incorporation of advanced security-constrained optimal power flow, dynamic simulation, statistical analysis tools and a specially developed set of sampling functions into a unique environment. The possible hardware set-ups are outlined and, finally, some examples of the tool's application are given.

This paper analyses the dynamic behaviour of a power system with both FACTS and VSC HVDC, in particular, possible potential interactions between a STATCOM and a VSC in a point-to-point HVDC link. The investigation considers different STATCOM locations and VSC control strategies. A generic linearized mathematic model is firstly developed, where a combined method involving relative gain array (RGA) and modal analysis is applied to identify the interactions within the plant model and the outer controllers. The interactions identified are further analysed by creating a set of scenarios integrating both the STATCOM and VSC HVDC into a dynamic equivalent realistic AC system. Results show a collaborative operation between the STATCOM and the closely located VSC with reasonably tuned controllers.

During high wind speed shutdown (HWSS) events, the power outputs of wind power plants may be subject to high ramp rates, causing issues for the System Operator (SO) in predicting total wind output, allocating adequate reserve levels and minimising balancing costs. As the timing of these events is difficult to predict, it is proposed that individual turbines may be used as probabilistic early warning indicators of HWSS events across sites, and by extension to a wide geographical area. The shut-down history of two separate wind farms across Scotland is analysed to determine the likelihood and impact of such events. It is shown that in most cases, HWSS does not result in the full loss of availability. Factors such as turbine elevation and mean wind exposure are key indicators of the order of shut-down across a site. The suggestion that sites could be used as early warning indicators for the pattern of HWSS across a transmission zone is difficult to characterise and for the two wind farms studied, prediction was not consistent.

We outline a simulation development process, backed by a software framework, which focuses on developing and using a partial conceptual model as a ‘lens’ to compare and possibly re-implement existing models in a chosen problem domain (as well as to design new models). To make this feasible for existing models of arbitrary structure and background social theory, we construct our (partial) conceptual model in a way that acknowledges that it is a base representation which any individual model will typically add detail to, and abstract away from, in various ways which we argue can be formalised. A given model’s design is fitted to the conceptual model to capture how its structural architecture (and selected aspects of the system’s state and driving processes) map to the conceptual model. This fit can be used to produce incomplete skeleton code which can then be extended to produce a simulation. Along the way, we use robust decision-making to provide a useful frame and discuss how our approach differs from others. This is inevitably a preliminary approach to a broad and difficult problem, which we explore in the conclusions.

This chapter describes the behaviour of power systems during faults and illustrates the requirements for power system protection. The components of protection systems and the typical schemes used to protect power systems are described. An overview of the future challenges relating to protection is provided, with specific comments relating to the impact of distributed generation on network protection systems.

This chapter describes the main technical limits that must be satisfied in the operation of T&D networks and how various system elements are controlled to ensure safe, continuous supply of electricity. Some discussion is given on the impact of renewable generation and the potential contribution of energy storage, along with some other developments that promise to provide greater flexibility to system operators.

We consider a microgrid for energy distribution, with a local consumer, a renewable generator (wind turbine) and a storage facility (battery), connected to the external grid via a transformer. We propose a 2 steps-ahead reinforcement learning algorithm to plan the battery scheduling, which plays a key role in the achievement of the consumer goals. The underlying framework is one of multi-criteria decision-making by an individual consumer who has the goals of increasing the utilization rate of the battery during high electricity demand (so as to decrease the electricity purchase from the external grid) and increasing the utilization rate of the wind turbine for local use (so as to increase the consumer independence from the external grid). Predictions of available wind power feed the reinforcement learning algorithm for selecting the optimal battery scheduling actions. The embedded learning mechanism allows to enhance the consumer knowledge about the optimal actions for battery scheduling under different time-dependent environmental conditions. The developed framework gives the capability to intelligent consumers to learn the stochastic environment and make use of the experience to select optimal energy management actions.

To integrate wind power into the electrical power system studies need to be made of its impact. Wind plants are stochastic in nature and this makes them differ from traditional plants. One method to model the stochastic nature of wind plants is to create a wind field model and simulate wind data that can then be used to determine power outputs. There are a large number of models available, however often these models are not adequately validated. This paper proposes a number of criteria that all wind field models should be validated against and the methods that can be used to do this.