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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.

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.

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.

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.

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.

The decarbonisation of the energy system is attracting the attention of policy makers worldwide, with many measures targeting the residential sector. It is expected that emission reduction targets will bring changes on the energy system, such as energy conservation measures and the electrification of heating (in a context where the electric sector is mainly decarbonised). However, the changes on electricity prices due to the electrification of heating have been scarcely addressed in the literature. This paper presents an assessment of the impact on electricity prices produced by the decarbonisation of heating and energy efficiency in the residential sector. A linear programming problem is used to find the optimal planning and operation of electric heating and residential loads, following a price-maker approach. Then, the potential cost changes for residential consumers and the impacts on electricity prices in the wholesale day-ahead market are estimated considering different residential electric heating profiles and energy conservation scenarios. Following an explanation of the method, a discussion of results is presented, considering how the electrification of heating change the energy price curves and the policy implications of these findings. In addition, it is discussed how the optimal operation of heating systems with energy conservation technologies and thermal storage could mitigate the cost increase of these changes on the energy system.

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 describes the use of a cost - minimisation algorithm to explore the potential impact of two options for financial support for low carbon generation in the form of contracts for difference in a system with locational marginal pricing: 1. with a system-wide strike price; 2. with locational strike prices. A two zone system is modelled with the additional financial support for low carbon generation represented as negative variable costs that have the effect of filling in the difference between wholesale market prices and the strike price, the latter intended to cover the long-run costs of low carbon generation. The British case is modelled in which there is a limit to the total top-up expenditure. It is shown that the case of a system-wide strike price can result in less new low carbon generation capacity compared with the case of locational strike prices, due to the increased top-up spend in the lower price zone more rapidly meeting the constraint on the total cost of top-up payments to low carbon generation. However, it is also shown that the imposition of this constraint leads to a failure of the model to settle on one solution due to the non-convex relationship between installed capacity of low carbon generation and wholesale market price.

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.

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 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 concentrates on using fast acting DC Circuit Breakers (DCCBs) at strategic locations to allow the entire Multi-terminal HVDC (MTDC) system to be operated interconnected but partitioned into islanded DC network zones following faults. This configuration uses least number of DCCBs in order to minimise the capital cost and power loss while retaining the benefit of system interconnection. The proposed concept has greater flexibility during normal operating condition and reduces the need for pre-fault partitioning of the system. During a DC fault, the DCCBs at the strategic cable connections that link the different DC network partitions are opened such that the faulty DC section is quickly isolated from the remaining of the MTDC system. Thus, the healthy DC network zone can recover and power transmission can be restored quickly. The faulty DC section can be protected using slow AC circuit breakers. MATLAB/SIMULINK simulations are presented to demonstrate satisfactory system behaviour during DC faults.

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.

An analytic approximation for the critical clearing time (CCT) metric is derived from direct methods for power system stability. The formula has been designed to incorporate as many features of transient stability analysis as possible such as different fault locations and different post-fault network states. The purpose of this metric is to analyse trends in stability (in terms of CCT) of power systems under the variation of a system parameter. The performance of this metric to measure stability trends is demonstrated on an aggregated power network, the so-called two machine infinite bus network, by varying load parameters in the full bus admittance matrix using numerical continuation. The metric is compared to two other expressions for the CCT which incorporate additional non-linearities present in the model.

The UK offshore wind sector has developed rapidly, but needs to develop even further to meet the low carbon targets set by the government. In order to increase capacity and reduce costs, new offshore wind farms will use larger and more complex technology that goes beyond current understanding and experience. As a result, predicting the technical performance of offshore wind farms involves a considerable amount of uncertainty, especially during early life when teething problems may arise. We present a model that allows us to distinguish between natural variability and state-of-knowledge uncertainty when assessing the growth in technical performance of offshore wind farms over early operational life. As such, the model can be used to inform decisions to reduce uncertainties and to support effective risk management. To describe wind farm performance, we use an indicator we call availability-informed capability measures the capacity of the farm to generate electricity. We use an example based on a two-phase Monte Carlo simulation to illustrate the use of the model.

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.

The Cenozoic magmatic activity of Italy is characterized, from the Alps to the Aeolian Islands, by an abundance of SiO2-undersaturated potassic to ultra-potassic rock-types (leucite-phonolites, leucitites, kamafugites and lamproites). Rocks of sodic character, mainly sub-alkaline transitional and alkaline basaltic in composition plus some isolated lamprophyres, are situated along the western and southern side of the Tyrrhenian basin (Sardinia, Ustica, Sicily Channel, Etna) and eastward of it, within the Padan-AdriaticHyblean foreland (Veneto, la Queglia-Pietre Nere and Hyblean plateau). Although the sodic and potassic products belong to the same magmatotectonic domain, i.e. the Mediterranean “wide-rift system” and its shoulders, they have been attributed in the literature to contrasting geodynamic environments, one anorogenic and intra-plate, and the other orogenic and subduction-related (see Lustrino and Wilson, 2007 and references therein). There is widespread acceptance that the sodic magmatism is intra-plate in character, but in the case of the potassic suites there is no unanimity (Lustrino et al., 2011 and references therein). Models for the origin of the potassic rocks centre around the nature of the metasomatic components which might result from dehydration or decarbonation of a slab as it is subducted into the mantle or from the upwelling of deep mantle melts and/or fluids associated with hot spots/plumes. Some papers address the problem from a geochemical point of view comparing the radiogenic isotopic signatures, especially Sr, Nd and Pb, with the composition of well-known, world-wide, mantle components (Bell et al., 2006 among many others). Another category of papers address the problem from a tectonic-structural point of view, discussing the geological and seismological proof for or against subduction (Lavecchia and Creati, 2006 and references therein). Here, we provide an overview of the Paleocene to Present Italian igneous rocks in relationship to their tectonic setting at the time of their emplacement, and then evaluate possible alternative geodynamic scenarios for their origin, mainly based on integrated geophysical/geological and geochemical parameters.

This paper discusses the need for new test system models to be developed and made available to researchers. A number of features of such test systems are proposed. These include sufficient size and scope to allow control interactions to be studied but not so much that phenomena associated with new technologies cannot be understood. It is recalled that the performance of new technologies and their controls should be verified on a full system model that is as faithful to the real system and its parameters as possible and that this requires access to data often owned by generating companies to which system operators have access but do not feel able to disclose. Finally, arguments are presented as to why such data should be disclosed and it is recommended that regulatory authorities take steps to achieve it.

This paper presents three case studies of risk-based standards from Great Britain: the pre-liberalisation generation planning standard, the present method for setting operational reserve requirements, and the transmission network planning standard. These illustrate a number of key issues in developing planning and operational standards for wind, including: the benefits of risk-based standards in adapting to new circumstances; the importance of considering model assumptions carefully when interpreting risk calculations; the difficulty in calculating the cost of risk; and the need to account for uncertainty in system background. The transmission network planning standard is studied in particular detail, especially how the present combination of deterministic and probabilistic sections might evolve for use in a future power system with a very high renewable penetration.

Because of features that appear to be inherent in many social systems, modellers face complicated and subjective choices in positioning the scientific contribution of their research. This leads to a diversity of approaches and terminology, making interdisciplinary assessment of models highly problematic. Such modellers ideally need some kind of accessible, interdisciplinary framework to better understand and assess these choices. Existing texts tend either to take a specialised metaphysical approach, or focus on more pragmatic aspects such as the simulation process or descriptive protocols for how to present such research. Without a sufficiently neutral treatment of why a particular set of methods and style of model might be chosen, these choices can become entwined with the ideological and terminological baggage of a particular discipline. This paper attempts to provide such a framework. We begin with an epistemological model, which gives a standardised view on the types of validation available to the modeller, and their impact on scientific value. This is followed by a methodological framework, presented as a taxonomy of the key dimensions over which approaches are ultimately divided. Rather than working top-down from philosophical principles, we characterise the issues as a practitioner would see them. We believe that such a characterisation can be done 'well enough', where 'well enough' represents a common frame of reference for all modellers, which nevertheless respects the essence of the debate's subtleties and can be accepted as such by a majority of 'methodologists'. We conclude by discussing the limitations of such an approach, and potential further work for such a framework to be absorbed into existing, descriptive protocols and general social simulation texts.

The growing role of wind power in power systems has motivated R&D on methodologies to characterise the wind resource at sites for which no wind speed data is available, Applications such as feasibility assessment of prospective installations and system integration analysis of future scenarios, amongst others, can greatly benefit from such methodologies. This paper focuses on the inference of wind speeds for such potential sites using a Bayesian approach to characterise the spatial distribution of the resource. To test the approach, one year of wind speed data from four weather stations was modelled and used to derive inferences for a fifth site. The methodology used is described together with the model employed and simulation results are presented and compared to the data available for the fifth site. The results obtained indicate that Bayesian inference can be a useful tool in spatial characterisation of wind.