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

To exploit additional capacity on legacy transmission assets, network owners have developed a variety of techniques to support real-time thermal rating (RTTR) of overhead lines so as to get the maximum possible capacity within their operating range. Most RTTR techniques are inferred from effective weather conditions (EWCs) that are used to assess the thermal equilibrium of conductors indicated by the measured temperature , thus making RTTR estimates sensitive to measurement errors (MEs). To highlight the impacts that MEs have on RTTRs, this paper describes two EWC-based approaches to RTTR estimation and assesses the accuracy of their resulting estimates under steady state conditions given different levels of and weather variables. Furthermore, the paper develops a Monte Carlo-based approach to model the propagation of measurement uncertainties through to RTTR estimates. Numerous rating scenarios for a particular instance are generated through combining a Monte Carlo method, where possible MEs are sampled within the sensor specification based limits, with an enhanced EWC-based approach that models transient changes of in each scenario. The lower RTTR percentiles extracted from the rating samples can not only mitigate the RTTR overestimation due to MEs, but also inform system operators of the risk associated with RTTR decisions.

Vehicle-to-grid (V2G) from electric vehicles (EVs) represents an opportunity to provide transitioning electricity systems with battery storage as they face increasing shares of variable renewable generation. However, whilst the availability of V2G as dispatchable storage depends on the travel and charging habits of drivers, there is scarce experience of managing portfolios of EVs in this way. This paper investigates the impact of plug-in frequency – given real-life travel data – on the potential of V2G to reduce i) consumer bills and ii) carbon emissions of charging. In doing so, we investigate the extent to which consumers are incentivised to participate in V2G, how this might change based on different charging behaviours, and what the implications are for V2G as a storage asset. Two models of plug-in behaviour are input into a time-coupled optimisation that schedules EV (dis)charging to minimise the net cost of an EV’s required energy gain within network constraints, simulating how V2G could be dispatched by a ‘load controller’ in a liberalised electricity market. The cost minimisation is based on the Octopus Agile V2G tariff in January 2021, which is matched to GB grid carbon intensity data from National Grid ESO for the same period. It was found that, on the basis of the time range studied, V2G can reduce the average price paid for EV-charging electricity by 28–67% versus a flat tariff – with the lower end of that range representing a case where consumers only plug in when they ‘need’ to, and the higher end representing the case where consumers plug in whenever their cars are at home. It was also found that due to the weak positive correlation between price and carbon during the time range studied, optimising for price also resulted in slight reductions in carbon intensity of the EV-charging electricity by 5–6% compared to uncontrolled charging, with the range representing the same cases as before. Taking into account a review of battery degradation costs from V2G, using an EV’s battery in this manner only makes financial sense to the owner if they maximise their plug-in frequency; this, alongside the increased savings, should provide an incentive to owners to plug in as much as possible – thereby maximising storage resource for a low carbon electricity system.

Increasing renewable generation can lead to significant spatial and temporal changes to the rotor angle stability boundary, such that critical contingencies may drastically change. Additionally, the inherent variability of renewables increases the number of operational scenarios that require stability assessment. This paper presents a methodology whereby a series of location-specific Decision Tree Regressors are trained, using power system variables to estimate the Critical Clearing Time (CCT) on a locational basis throughout a network. Permutation feature importance is used to reveal the most important power system variables for CCT estimation at each location (capturing aspects related to physical system characteristics, operational parameters as well as economic dispatch). Consequently, estimation of the duration and location of the critical fault can also be made – along with identification of important system variables that explicitly impact the critical fault. Results on the IEEE 39-bus network show accurate estimation of locational CCTs, with a mean absolute percentage error of 1.19% on average. Moreover, the mean absolute percentage error for the minimum CCT is 0.49%. An analysis of important power system variables is provided, demonstrating how the method can assist in the design of targeted locational interventions to improve the stability margin at specific locations.

Optimisation and Analysis Toolbox for power Systems analysis (OATS) is an open-source simulation tool for steady-state analyses of power systems problems distributed under the GNU General Public License (GPLv3). It contains implementations of classical steady-state problems, e.g. load flow, optimal power flow (OPF) and unit commitment, as well as enhancements to these classical models relative to the features available in widely used open-source tools. Enhancements implemented in the current release of OATS include: a model of voltage regulating on-load tap-changing transformers; load shedding in OPF; allowing a user to build a contingency list in the security constrained OPF analysis; implementation of a distributed slack bus; and the ability to model zonal transfer limits in unit commitment. The mathematical optimisation models are written in an open-source algebraic modelling language, which offers high-level symbolic syntax for describing optimisation problems. The flexibility offered by OATS makes it an ideal tool for teaching and academic research. This paper presents novel aspects of OATS and discusses, through demonstrative examples, how OATS can be extended to new problem classes in the area of steady-state power systems analysis.

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.

Predicting car ownership patterns at high spatial resolution is key to understanding pathways for decarbonisation—via electrification and demand reduction—of the private vehicle fleet. As the factors widely understood to influence car ownership are highly interdependent, linearised regression models, which dominate previous work on spatially explicit car ownership modelling in the UK, have shortcomings in accurately predicting the relationship. This paper presents predictions of spatially disaggregated car ownership—and change in car ownership over time—in Great Britain (GB) using deep neural networks (NNs) with hyperparameter tuning. The inputs to the models are demographic, socio-economic and geographic datasets compiled at the level of Census Lower Super Output Areas (LSOAs)—areas covering between 300 and 600 households. It was found that when optimal hyperparameters are selected, these neural networks can predict car ownership with a mean absolute error of up to 29% lower than when formulating the same problem as a linear regression, the results from NN regression are also shown to outperform three other artificial intelligence (AI)-based methods: random forest, stochastic gradient descent and support vector regression. The methods presented in this paper could enhance the capability of transport/energy modelling frameworks in predicting the spatial distribution of vehicle fleets, particularly as demographics, socio-economics and the built environment—such as public transport availability and the provision of local amenities—evolve over time. A particularly relevant contribution of this method is that by coupling it with a technology dissipation model, it could be used to explore the possible effects of changing policy, behaviour and socio-economics on uptake pathways for electric vehicles —cited as a vital technology for meeting Net Zero greenhouse gas emissions by 2050.

This session will compare experiences of power sector decarbonisation opportunities and challenges from industry leaders around the world.

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.

To investigate the behaviour of Ba isotopes during carbonatite petrogenesis and to explore the possibility of using carbonatites to constrain the Ba isotopic composition of the mantle, we report high-precision Ba isotopic analyses of: (1) carbonatites and associated silicate rocks from the only active carbonatite volcano, Oldoinyo Lengai, Tanzania, and (2) Archean to Cenozoic carbonatites from Canada, East Africa, Germany and Greenland. Carbonatites and associated phonolites and nephelinites from Oldoinyo Lengai have similar δ137/134Ba values that range from +0.01 to +0.03‰, indicating that Ba isotope fractionation during carbonatite petrogenesis is negligible. The limited variation in δ137/134Ba values from −0.03 to +0.09‰ for most carbonatite samples suggests that their mantle sources have a relatively homogeneous Ba isotopic composition. Based on the carbonatites investigated in this work, the average δ137/134Ba value of their mantle sources is estimated to be +0.04 ± 0.06‰ (2SD, n = 16), which is similar to the average value of +0.05 ± 0.06‰ for mid-ocean ridge basalts. The lower δ137/134Ba value of −0.08‰ in a Canadian sample and higher δ137/134Ba values of +0.14‰ and + 0.23‰ in two Greenland samples suggest local mantle isotopic heterogeneity that may reflect the incorporation of recycled crustal materials in their sources.

‘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.

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

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.

Energy system models which cover multiple vectors have become increasingly used to provide an evidence base for policy and commercial decisions in real-world energy systems undergoing change. In particular, such models are often used to derive ‘optimal’ pathways to decarbonisation considering the planning or operation of systems with multiple technology options. This paper explores how the concept of ‘usefulness’ – the applicability and relevance of modelling outcomes – may be used to establish criteria for modelling design and practice at the outset, and looks at the difficulties that may be faced in achieving this. The application should inform the choice of modelling framework and the manner in which tractability should be addressed and results meaningfully presented. A process of continuous engagement is proposed which guides modelling work towards ‘useful’ outcomes, as well as mitigating the danger of results being more reflective of design choices than the properties of the real-world systems being modelled. Because of the difficulties in maintaining and auditing complex datasets spanning expertise from multiple sectors, there is a clear role for independent data curators to facilitate rigour in model parameterisation and to allow consistency between modelling efforts. Specialists from the different disciplines represented should be engaged to ensure that data have been interpreted and applied correctly. All modelling choices should be clearly documented along with advice on their possible implications in respect of use of the results.

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.

The majority of published approaches to real-time thermal rating (RTTR) deal with continuous or steady-state ratings for overhead lines. Less attention has been given to short-term or transient-state RTTRs, partly due to the increased computation time required. This paper describes a fast-computational approach to providing a transient-state RTTR in the form of percentiles based on the predictive distributions modelled for the measured weather variables that are combined with Monte Carlo simulation. An analytical method developed in IEEE Standard 738 calculates the transient-state conductor temperature after a step change in line current only and additionally requires the conductor to be in thermal equilibrium before the step occurs. The IEEE analytical method is enhanced here through inference of an equivalent steady-state initial line current from the initial conductor temperature and weather conditions over a specified time period. Numerous transient-state RTTR forecasts for a particular span are estimated via weather inputs randomly sampled from predictive distributions for a number of time steps ahead combined with the secant method to find the transient-state RTTR. Along with an enhanced analytical method, this yields a maximum allowable conductor temperature for a specified time period under each set of weather samples. The percentiles of transient-state RTTR forecasts are then determined from their sampled values using kernel density estimation. The approach developed here considers variations in weather forecasts at each 10-min time step.

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.

Purpose: Epidemiologic studies of cancer among U.S. firefighters suggest a disproportionate burden of select cancer types including respiratory, gastrointestinal, skin, and kidney when compared to the general population. Although routine colorectal and whole-body skin cancer screening is widely recommended, screening rates among firefighters remain low. The National Fire Protection Association estimates a 1.1 million firefighter workforce, most of whom are Caucasian male, however, sub-groups of women, Hispanic, sexual minority, and Black firefighters are growing. In the present study we describe facilitators and barriers to cancer screening among Black firefighters. Method: A semi-structured interview script was used to conduct five focus group sessions of 45 to 60-minute duration with a total of 22 Black firefighters in South Florida between December 2019 and June 2020. A 20-item demographic survey was administered prior to the start of the interview. Data analysis was conducted using a grounded theory approach and NVIVO 12 software. Qualitative data was analyzed for themes and patterns. Results: Among participating Black firefighters, 90.9% were male, 4.6% Hispanic, 77.3% married, 68.1% with college/master’s educational attainment, 72.7% with private health insurance, 72.7% had a personal doctor, and a group mean age was 44 years (SD ± 8.7 years). Across all focus group sessions, the following three major themes emerged as facilitators and barriers to cancer screening: 1) societal culture of machismo and familial culture; 2) cancer education and cancer awareness; and 3) individual faith. Black firefighters describe culture as influencing health decisions and behaviors throughout their lives, including food choices, receiving a health screening, or visiting a doctor. A second prevalent theme focused on education and cancer awareness; specifically, Black firefighters did not feel comfortable with doctors, particularly because they were Black and firefighters. Many felt that their doctors are not as aware of racial health differences as they should be, and they had to bring information to their doctors about firefighters’ unique job exposures. Lastly, a third theme describing individual faith influencing cancer screening practices frequently emerged. Many participants reported faith as being very important to them, however, they would not let religious beliefs prevent them from seeking medical interventions when needed. Conclusion: Black societal culture of machismo, cancer education and awareness, and individual faith were identified as facilitators and barriers of cancer screening among Black firefighters in South Florida. Strategies to improve cancer screening and prevention for Black firefighters should consider these factors in the design of workplace cancer interventions for this unique minority occupational subgroup. Citation Format: Kemi Ogunsina, Natasha Solle, Lauren A. Murphy, Debi Brannan, Keith Bell, William McAlister, Erin Kobetz, Alberto Caban- Martinez. Examining facilitators and barriers to cancer screening among Black firefighters in South Florida [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-251.

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.

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

The Western Link is a new point-to-point embedded HVDC connection due to be commissioned in Great Britain in 2018. This paper investigates the optimal loading of the Western Link with respect to the wider s transmission system. The work modelled a representation of behaviour of the wholesale market and system operator actions using mathematical optimisation in the form of an economic dispatch followed by an AC optimal power flow. A range of different system cases was studied using: a representative high voltage transmission network of Great Britain; system planned outages on AC circuits in parallel with the Western Link; system contingencies; and two possible post-contingency Western Link loading rules. It was concluded from the cases studied that the optimal dispatch of power on the Western Link is an affine function of power flow in the parallel AC circuits, modulated by system planned outages and the thermal rating of the Western Link.