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Objective This study aimed to develop and internally validate risk prediction models for predicting groin wound surgical site infections (SSIs) following arterial intervention and to evaluate the utility of existing risk prediction models for this outcome. Methods Data from the Groin wound Infection after Vascular Exposure (GIVE) multicentre cohort study were used. The GIVE study prospectively enrolled 1 039 consecutive patients undergoing an arterial procedure through 1 339 groin incisions. An overall SSI rate of 8.6% per groin incision, and a deep/organ space SSI rate of 3.8%, were reported. Eight independent predictors of all SSIs, and four independent predictors of deep/organ space SSIs were included in the development and internal validation of two risk prediction models. A systematic search of the literature was conducted to identify relevant risk prediction models for their evaluation. Results The “GIVE SSI risk prediction model” (“GIVE SSI model”) and the “GIVE deep/organ space SSI risk prediction model” (“deep SSI model”) had adequate discrimination (C statistic 0.735 and 0.720, respectively). Three other groin incision SSI risk prediction models were identified; both GIVE risk prediction models significantly outperformed these other risk models in this cohort (C statistic 0.618 – 0.629; p Conclusion Two models were created and internally validated that performed acceptably in predicting “all” and “deep” groin SSIs, outperforming current existing risk prediction models in this cohort. Future studies should aim to externally validate the GIVE models.

In power systems modelling, optimization methods based on certain objective function(s) are widely used to provide solutions for decision makers. For complex high-dimensional problems, such as network hosting capacity evaluation of intermittent renewables, simplifications are often used which can lead to the ‘optimal’ solution being suboptimal or nonoptimal. Even where the optimization problem is resolved, it would still be valuable to introduce some diversity to the solution for long-term planning purposes. This paper introduces a general framework for solving optimization for power systems that treats an optimization problem as a history match problem which is resolved via statistical emulation and uncertainty quantification. Emulation constructs fast statistical approximations to the complex computer simulation model in order to identify appropriate choices of candidate solutions for given objective function(s). Uncertainty quantification is adopted to capture multiple sources of uncertainty attached to each candidate solution and is conducted via Bayes linear analysis. It is demonstrated through a hosting capacity case study involving variable wind generation and active network management. The proposed method effectively identified not only the maximum connectable capacities but also a diverse set of near-optimal solutions, and so provided flexible guides for using the existing network to maximize the benefits of renewable generation.

The capacity of renewable distributed generation (DG) connected in distribution networks is increasing. Use of power electronic interfaces means DG can inject harmonic currents through the point of common coupling into upstream networks. The limits stipulated in harmonic emissions standards may create challenges for accommodating DG. To explore the impact of harmonic regulations on the ability of distribution networks to host DG, this work incorporates harmonic voltage constraints into a network hosting capacity assessment. A novel hosting capacity assessment approach is presented, incorporating percentile-based harmonic compliance levels as chance constraints over multiple periods into AC optimal power flow. The case study shows that network hosting capacity for DG could be evidently lower under rigorous compliance with harmonic distortion limits, but that relaxation of the risk constraints has significant value. Furthermore, the complex inter-connectivity between DG sites means that voltage, thermal and harmonic constraints all influence the locational feasibility for DG capacity.

In order to meet global climate targets, it will be essential to decarbonise transport along with emissions sources. For road transport, the two currently available technologies are battery electric vehicles and hydrogen fuel cell electric vehicles. Battery vehicles are more established than hydrogen, although both are capable of delivering the emissions reduction required.However, battery electric vehicles are usually considerably heavier than the equivalent hydrogen fuel cell electric vehicles, which are in turn slightly heavier than existing internal combustion engine (ICE) vehicles; a heavier vehicle will have a bigger impact on road wear, and associated maintenance and particulate emissions.Here we examine for the first time one impact and hidden cost, associated with the increased weight of zero emissions vehicles, not commonly addressed in energy research – the additional wear and tear on roads, associated with road maintenance cost and particulate emissions. We examine these in three different future scenarios of all battery vehicles, all hydrogen vehicles, and a combination, in comparison with the current ICE situation.We find that there is 30-40% additional road wear associated with battery vehicles compared to ICE vehicles; there is a 6% increase from ICE vehicles with hydrogen. This is overwhelmingly caused by the largest vehicles - buses and heavy goods vehicles. The contribution from cars, vans and motorcycles is negligible with any fuel type. Governmental bodies will become liable for increased road maintenance costs and may wish to set weight limits on roads, or require additional axles on heavier vehicles, to decrease road wear.

The frequency of power outages being experienced in Sub-Saharan Africa mean that traditional methods of electricity demand forecasting which rely on directly observed demand data are inadequate for use in projections. Nevertheless, accurate forecasting methods are urgently required to ensure efficient power system operations and expansion planning. To address this gap, we develop a novel method to estimate unsuppressed electricity demand for developing countries. This follows a bottom-up approach based on socioeconomic data and a time-use database developed from a householder survey, which are used to generate household profiles using a Markov Chain approach. These profiles are then converted into electrical load time series by a series of appliance models, using reanalysis weather data to accurately represent ambient conditions for the generation of cooling demand profiles. We apply our method to a Nigerian case study, obtaining the first time series of unsuppressed residential electricity demand for the country using the first Time-of-Use Survey (TUS) for Nigerian households. We validate our model outputs using the results of a small-scale residential metering trial, which yielded a correlation coefficient of 0.97, RMSE of 0.04, and percentage error of 6% between measured and model data. This evidences that our method is a credible and practical tool for electrical demand studies in developing countries. Using the model, the forecasted domestic demand for Abuja Electricity Distribution Company ranges between 345 and 575 MW, while that of Nigeria ranges between 3829 and 6605 MW.

The curtailment of renewable energy would be reduced by converting it to hydrogen or methane using power to hydrogen (P2H) facilities or power to methane (P2M) facilities. Both hydrogen and methane can be injected into the existing natural gas system which has significant potential to unlock the inherent flexibility of integrated energy systems. The coordinated operation strategy of the hybrid power-natural gas energy systems considering P2H and P2M is proposed aiming to minimize the operational cost. In addition, a method to calculate the higher heating value of hydrogen-natural gas mixture is presented along with a strategy for handling the constraints of hydrogen mixture level limits. The simulation results of three case studies demonstrate the economic and environmental benefits of P2H/P2M in terms of reductions in cost, CO2 emissions and wind power curtailment. The differences in benefits between P2H and P2M have also been compared and analyzed.

Background: Living in deprived areas is associated with poorer outcomes after certain vascular procedures and surgical site infection in other specialties. Our primary objective was to determine whether living in more income-deprived areas was associated with groin wound surgical site infection after arterial intervention. Secondary objectives were to determine whether living in more income-deprived areas was associated with mortality and clinical consequences of surgical site infection. Methods: Postal code data for patients from the United Kingdom who were included in the Groin Wound Infection after Vascular Exposure (GIVE) multicenter cohort study was used to determine income deprivation, based on index of multiple deprivation (IMD) data. Patients were divided into three IMD groups for descriptive analysis. Income deprivation score was integrated into the final multivariable model for predicting surgical site infection. Results: Only patients from England had sufficient postal code data, analysis included 772 groin incisions (624 patients from 22 centers). Surgical site infection occurred in 9.7% incisions (10.3% of patients). Surgical site infection was equivalent between income deprivation tertiles (tertile 1 = 9.5%; tertile 2 = 10.3%; tertile 3 = 8.6%; p = 0.828) as were the clinical consequences of surgical site infection and mortality. Income deprivation was not associated with surgical site infection in multivariable regression analysis (odds ratio [OR], 0.574; 95% confidence interval [CI], 0.038-8.747; p = 0.689). Median age at time of procedure was lower for patients living in more income-deprived areas (tertile 1 = 68 years; tertile 2 = 72 years; tertile 3 = 74 years; p

This paper discusses and compares deterministic and probabilistic approaches for assessing hosting capacity (HC) of distribution networks for connecting wind-based and PV-based distributed generation (DG). The presented approaches consider actual variations of solar and wind energy resources, supplied demands and dynamic thermal ratings (DTRs) of network components, based on available measurement data. The obtained results allow for a detailed analysis of the firm HC allocation for wind-based and PV-based DG in distribution networks, confirming that, due to implementation of DTRs, firm HC of wind DG is roughly twice the firm HC of PV DG.

It is imperative to increase the connectable capacity (i.e., hosting capacity) of distributed generation in order to decarbonise electricity distribution networks. Hybrid generation that exploits complementarity in resource characteristics among different renewable types potentially provides value for minimising technical constraints and increasing the effective use of the network. Tidal, wave and wind energy are prominent offshore renewable energy sources. It is of importance to explore their potential complementarity for increasing network integration. In this work, the novel introduction of these distinct offshore renewable resources into hosting capacity evaluation enables the quantification of the benefits of various resource combinations. A scenario reduction technique is adapted to effectively consider variation of these renewables in an AC optimal power flow-based nonlinear optimisation model. Moreover, the beneficial impact of active network management (ANM) on enhancing the renewable complementarity is also investigated. The combination of complementary hybrid generation and ANM, specifically where the maxima of the generation profiles rarely co-occur with each other and with the demand minimum, is found to make the best use of the network components.

This paper presents a multi-stage optimal power flow (OPF) approach for efficient management of contingencies, in which dynamic thermal models of overhead lines and power transformers are used to evaluate maximum lead time (MLT) available to network operators to resolve violated post-contingency constraints. At the first stage, optimal settings of volt-var controls, such as transformers with on-load tap changers and shunt capacitors, are selected, taking into account voltage-dependent load models. The second stage provides optimal generation re-dispatch, including fast-start generators. At the final third stage, optimal load shedding is implemented to clear all remaining constraint violations. The OPF problem is solved by a hybrid metaheuristics method and illustrated on a modified IEEE 14-bus network. Obtained results demonstrate that presented method can help network operators to efficiently resolve all violated constraints within the available MLT.

Background: Surgical site infections (SSIs) of groin wounds are a common and potentially preventable cause of morbidity, mortality and healthcare costs in vascular surgery. Our aim was to define the contemporaneous rate of groin SSIs, determine clinical sequelae, and identify risk factors for SSI. Method: An international multicentre prospective observational cohort study of consecutive patients undergoing groin incision for femoral vessel access in vascular surgery was undertaken over 3 months, follow up was 90 days. The primary outcome was incidence of groin wound SSI. Results: 1337 groin incisions (1039 patients) from 37 centres were included. 115 groin incisions (8.6%) developed SSI, of which 62 (4.6%) were superficial. Patients who developed an SSI had a significantly longer length of hospital stay (6 vs 5 days, p=0.005), a significantly higher rate of post-operative acute kidney injury (19.6% vs 11.7%, p=0.018), with no significant difference in 90-day mortality. Female sex, Body Mass Index≥30kg/m2, ischaemic heart disease, aqueous betadine skin preparation, bypass/patch use (vein, xenograft or prosthetic) and increased operative time were independent predictors of SSI. Conclusion: Groin infections which are clinically apparent to the treating vascular unit are frequent and their development carries significant clinical sequelae. Risk factors include modifiable and non-modifiable variables.

International audience; This paper evaluates deterministic and probabilistic approaches for assessing hosting capacity (HC) of distribution networks for wind-based distributed generation (DG). The presented methodology considers variations of demands and DG power outputs, as well as dynamic thermal ratings (DTR) of network components. Deterministic approaches are based on a limited number of scenarios with minimum and maximum demands and DTR limits, while probabilistic approaches use simultaneous hourly values of all input parameters. The presented methodology has three stages. First, locational HC (LHC) of individual buses is calculated assuming connection of a single DG unit in the considered network. Afterwards, the LHC results are used to calculate network HC (NHC), assuming that DG units are connected at all network buses. Finally, busto-bus LHC-sensitivity factors are used to determine LHC and NHC for any number of DG units connected at arbitrary network buses.

Wave energy has the potential to play an important role in the UK's electricity mix in the coming years and it is important to understand the interactions of wave energy converters with the environment before considering them viable alternatives for other technologies. The aim of this study was to identify the environmental impacts of the deployment of the Oyster wave energy converter to the EMEC test site at Orkney, UK over its lifetime across three general categories: resource use, human health and ecological consequences. A full life cycle assessment was performed on two different models of the Oyster wave energy converter: Oyster 1 and Oyster 800. It was found that the latter is a fitting upgrade for its predecessor as it has lower environmental impacts in all categories; however, the high infrastructural needs of the Oyster technology makes its environmental performance worse than most other wave energy converters. Key sustainability indicators for energy converters include carbon footprint and energy payback period, and these were found to be 79 and 57 gCO2 eq/kWh and 45 and 42 months for the Oyster 1 and Oyster 800, respectively. Although these are significantly higher than most estimates for other types of renewable energy converter, the carbon impacts are still significantly lower than for conventional fossil-fuelled power generation.

There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity and thermal load modelling in a large, mixed-use, university building. The independent feature variables of the model include outdoor temperature, historical energy consumption data sets, and several types of ‘agent schedules’ that provide proxy information that is based on broad classes of activity undertaken by the building’s inhabitants. The case study compares four different ML models testing three different feature sets with a genetic algorithm (GA) used to optimize the feature sets for those ML models without an embedded feature selection process. The results show that the regression models perform significantly better than classification models for the prediction of electricity demand and slightly better for the prediction of heat demand. The GA feature selection improves the performance of all models and demonstrates that historical heat demand, temperature, and the ‘agent schedules’, which derive from large occupancy fluctuations in the building, are the main factors influencing the heat demand prediction. For electricity demand prediction, feature selection picks almost all ‘agent schedule’ features that are available and the historical electricity demand. Historical heat demand is not picked as a feature for electricity demand prediction by the GA feature selection and vice versa. However, the exclusion of historical heat/electricity demand from the selected features significantly reduces the performance of the demand prediction.

Purpose To date, very few studies have attempted to quantify the environmental impacts of a wave energy converter, and almost all of these focus solely on the potential climate change impacts and embodied energy. This paper presents a full life cycle assessment (LCA) of the first-generation Pelamis wave energy converter, aiming to contribute to the body of published studies and examine any potential trade-offs or co-benefits across a broad range of environmental impacts. Methods The process-based attributional LCA was carried out on the full cradle-to-grave life cycle of the Pelamis P1 wave energy converter, including the device, its moorings and sub-sea connecting cable up to the point of connection with the grid. The case study was for a typical wave farm located off the north-west coast of Scotland. Foreground data was mostly sourced from the manufacturer. Background inventory data was mostly sourced from the ecoinvent database (v3.3), and the ReCiPe and CED impact assessment methods were applied. Results and discussion The Pelamis was found to have significantly lower environmental impacts than conventional fossil generation in 6 impact categories, but performed worse than most other types of generation in 8 of the remaining 13 categories studied. The greatest impacts were from steel manufacture and sea vessel operations. The device performs quite well in the two most frequently assessed impacts for renewable energy converters: climate change and cumulative energy demand. The carbon payback period is estimated to be around 24 months (depending on the emissions intensity of the displaced generation mix), and the energy return on investment is 7.5. The contrast between this and the poor performance in other impact categories demonstrates the limitations of focussing only on carbon and energy. Conclusions The Pelamis was found to generally have relatively high environmental impacts across many impact categories when compared to other types of power generation; however, these are mostly attributable to the current reliance on fossil fuels in the global economy and the early development stage of the technology. Opportunities to reduce this also lie in reducing requirements for steel in the device structure, and decreasing the requirements for sea vessel operations during installation, maintenance and decommissioning.

Although the integration of meshed multi-terminal direct current (MTDC) grids with the existing AC grid has some added economic advantages, significant challenges are encountered in such system. One of the major challenges is ensuring secure and optimal operation of the combined AC/MTDC grid considering the stability requirements of AC and DC grids at different operating conditions. This paper presents the implementation of hierarchical control for the combined AC/MTDC grid. The hierarchical control is based on the well-established three-layered control of the AC power system, comprising primary,secondary, and tertiary controls. A set of appropriate control methods are proposed for the primary, secondary, and tertiary control layers to accomplish the identified requirements for secure and optimal operation of the combined AC/MTDC grid.

The main purpose of load forecasting is to provide an accurate estimation of the future electricity demands, which is becoming increasingly important for the operation and planning of existing electricity network and future smart grids, as they will feature much higher ranges of uncertainties, larger variations of power flows and increased levels of interactions between supply and demand sides. Typical load profiles exhibit periodicity, allowing to extract patterns from demand time series and available historical recordings. However, there are many factors that cause strong variations of these demand patterns, including calendar and socio-behavioral aspects (time of the day, day of the week, season of the year, but also weekly working schedule, public holidays, etc.), as well as meteorological or weather related factors (ambient temperature, solar irradiation, precipitation, etc.). This paper analyzes load forecasting using a stacked bidirectional long short-term memory (SB-LSTM) recurrent neural network based approach, which is a state-of-the-art method for regression analysis of time-series data under deep learning framework. The analysis is performed on a case study of residential demands in Scotland, for which a five-year-Iength datasets containing both load and weather data recordings are available. The presented results and analysis allow to evaluate how accurately SB-LSTM approach can provide predictions for both day-ahead and week-ahead load forecasting, taking into account meteorological information.

Day-ahead load forecasting plays an increasingly important role for the operation of networks and generation dispatch. The accuracy of the forecasting depends on many factors, including the quality and size of historical data, selected forecasting model and available information on influential factors (e.g., weather data). This paper compares three state-of-the-art models in terms of their ability to capture complex variations in load profiles and provide accurate day-ahead load forecasting: multilayer perceptron (MLP), gradient boosting regression trees (GBRT) and stacked bidirectional long short-term memory (SB-LSTM). The models are implemented on one dataset from China and another from Scotland. The performance is evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE) and other energy-based indices. The presented results show that GBRT outperforms MLP with the same expert extracted load characteristics as additional inputs, but SB-LSTM provides the most accurate forecasting results, without extracting any artificial data feature from the two considered demand datasets.

Exploiting the diversity between different renewable resources is regarded as a significant tool to managing their grid integration. Hybrid combinations of resources provide the potential to smooth output and so overcome limits on the export of power, but their network wide impact is not well understood. This paper examines whether combinations of renewable distributed generation can make more effective use of distribution network capacity. A multi-period, multi-resource optimal power flow approach is used to optimally configure wind and solar photovoltaic capacity to maximise energy production whilst complying with network physical limits. The effectiveness of hybrid distributed generation and the optimization method was examined through comparison with cases using single types of renewable distributed generation. This study demonstrates that by capturing the complementarity between renewables through hybrid design, the network can host more renewable generation capacity and increase total energy export. In addition, smart grid techniques, such as active network management, further boosts the value of resource diversity by allowing connection of more generation capacity of all considered renewables through isolating the infrequent co-occurrence of high outputs during periods of low electricity demand.

This paper presents a novel optimal power flow (OPF) based approach for post-contingency management of severe congestions, aimed at maximizing lead time available to the network operators before the next contingency occurs. The approach first computes the maximum allowed overloading times for the congested transmission lines and transformers, using their dynamic thermal models. Afterwards, the OPF analysis is performed to identify the maximum lead time available to the network operator for managing post-contingency constraints and for devising and implementing the most efficient corrective actions. The corresponding OPF problem is modelled as a mixed-integer nonlinear optimization problem and solved using mixed-discrete particle swarm optimization (MDPSO) approach. The approach is illustrated on a modified IEEE 14-bus network and obtained results demonstrate that presented approach can manage considered constraints within the available lead time.

The interdependencies of electricity, heat and gas systems have significantly increased in recent years, and their further coupling becomes increasingly important to realize the efficient and secure operation of the future low carbon energy systems. Integration of multi- energy systems at different scales represents an opportunity for overall system improvements using the flexibilities across the distributed local small scale to utility energy network level, bus also poses a new challenge of complexity. Therefore, a ‘whole system’ planning approach is essential to capture the synergies and reduce the risks associated with securing an integrated system. However, the common use of centralised planning models overlook the different ownerships of sub-energy systems, and can lead to impractical in reality as the subsystems are often independently managed. In this paper, a novel decentralized planning framework is proposed to allow a two-level integration with multiple subsystems, which includes the lower-level of several local communities and the upper-level of an integrated electricity and gas distribution network. The two levels are separately optimised to minimise their own cost while an overall carbon emission budget is achieved for the whole system using a carbon-led bi-level coordinated strategy. A case study is also provided to illustrate the application of the proposed framework.

This work assesses the changes in power capacity expansion decisions regarding power generation and transmission that occur when the effects of Climate Change on wind speed are captured in the decision model. Considering an 85-year period (2016–2101), we use a Mixed-Integer Linear Program (MILP) model to analyze the optimal power capacity expansion in diverse types of power generation technologies, throughout the years and geographical locations. The optimization model minimizes the total (investment and operational) costs of the power system subject to several technical and economic constraints. We implement our model using the main Chilean power system. We compare two scenarios: one assuming that Climate Change affects wind speeds and hence wind farm capacity factors and the other assuming it does not. Our results reveal that, when taking into account the impact of Climate Change on wind speed, the optimal power generation and transmission expansion plan is different than when ignoring this effect. The variation of wind speed affects not only wind power capacity installed, but also other-technology power capacity installed. In particular, power capacity installed in wind and solar generation plants is higher (measured in MW installed) than the power capacity installed when we ignore the effects of Climate Change; and power capacity installed in diesel and natural gas technologies are lower. We perform sensitivity analyses, changing power capacity expansion limits and the discount rate, to check for the robustness of our results.

Power-to-gas (P2G) using excess renewable sources is an effective method to reduce renewable curtailment issues in microgrid system. The produced hydrogen is versatile green fuel for different energy sectors, such as electricity, heat and mobility. In recent researches, fuel cell-based system is considered as a promising technology to consume hydrogen (H2) generated from P2G due to high efficiency and cleanness. However, its economic and thermodynamic adaptability when coupled with intermittent renewable sources remains an open question to be addressed carefully. One of the major challenges in optimizing such system is to simultaneously capture the intraday and seasonal variation of renewable sources & load, as well as the internal thermodynamic process of critical components in appropriate modeling detail. This paper presents a multi-energy system for microgrid in which a wind-powered P2G is coupled with a detailed thermoeconomic model of solid oxide fuel cell/gas turbine (SOFC/GT) hybrid system. A two-level multi-objective optimization of planning and operation together is proposed. For system planning, the optimal balance between the least wind curtailment rate and total life cycle cost (LCC) is determined. To facilitate the coordinate operation of system components, a power management strategy is proposed in response to fluctuations of wind power and electricity load with considerations of multiple thermodynamic safety criteria. Results show that in the selected case, the multi-energy system operates with low wind curtailment rate of 0.63% and high renewable penetration level of 90.1%. The optimized LCC of multi-energy system is £2,468,093 with wind power accounting for 68.35% of total capital investment. With the power management strategy applied, the SOFC/GT could operate under the maximum electrical efficiency of 67.1% with safety constraints satisfied, making up only half investment cost of P2G. To capture seasonal variations, both winter and summer scenarios are detailly analyzed, sensitivity analysis is also carried out to evaluate the interaction between capacities of MES components.

Grid connected electrical energy storage is expected to enable the integration of variable renewable generation in the future. As the electricity sector develops wholesale electricity prices will change, which will change the way in which storage technologies are operated. This paper investigates the sensitivity of storage revenue to uncertain market variables. Results indicate that higher gas prices, carbon prices and average demand would increase peak electricity prices, leading to larger daily price spreads and increased storage revenue. Increased wind generation, however, would reduce opportunities for price arbitrage and lessen storage revenue. Wind power also affects the way in which devices are operated and changes the characteristics which are rewarded by the market. With increased wind capacity, storage devices cycle less regularly as operation is driven by substantial changes in wind power output, rather than daily demand patterns. As a result, slower discharge times are more favourable and revenue is more sensitive to rates of self-discharge. Furthermore, there is less variation in wholesale electricity price and consequently conversion efficiency is more critical to performance.

Increasing amounts of variable renewable energy sources will cause fundamental and structural changes to thermal power plant operating regimes. Maintaining key reserve requirements will lead to an increase in power plant start-ups and cycling operations for some units. An enhanced unit commitment model with energy storage and flexible ${\text C}{{\text O}_2}$ capture is formulated. High-resolution on-/offshore wind data for the U.K., and probabilistic wind power forecast, model wind imbalances at operational timescales. The strategic use of flexible ${\text C}{{\text O}_2}$ capture and energy storage helps maintain reserve levels, decreasing power plant cycling operations and wind curtailment. A temporally explicit variability assessment of net demand illustrates the generation flexibility requirements and the nonlinear impacts of increasing wind capacity on power plant operating regimes.

Electricity generation by tidal current and wave power arrays represents a radical innovation and is confronted by significant technological and financial challenges. Currently, the marine energy sector finds itself in a decisive transition phase having developed full-scale technology demonstrators but still lacking proof of the concept in a commercial project environment. After the decades-long development process with larger than expected setbacks and delays, investors are discouraged because of high capital requirements and the uncertainty of future revenues. In order to de-risk the technology and to accelerate the commercialisation process, we identified stakeholder-wide balanced and realisable strategic targets. The objective is to name the top-level drivers for facilitating technology maturation and thus achieving market acceptance. Our analysis revealed that the two major risks for multi-megawatt projects (funding and device performance) are directly interlinked and that co-ordinated action is required to overcome this circular relationship. As funding is required for improving device performance (and vice-versa), showcasing an “array-scale success” was identified as the interim milestone on the way towards commercial generation. By this game-changing event, both mentioned risk complexes will be simultaneously mitigated. We observed that system dynamics modelling is appropriate for an unbiased analysis of complex multi-level expert interview data. The applied research model was found to be efficient and allows a regular re-assessment of the strategic alignment thus supporting the adaptation to a complex and continuously changing socio-technical environment.

In the context of smart grid transformation of existing electricity networks, optimal power flow (OPF) and security-constrained OPF (SCOPF) studies remain to be very important for power system planning, operation and market analysis. OPF study involves finding the (global) optimum solution to a set of nonlinear algebraic equations, subjected to a set of equality and inequality constraints. When the system is heavily stressed, particularly following a severe contingency, the conventional OPF methods may fail due to the problem or solution infeasibility, or inability to select proper initial values. The soft constraint handling approach and repetitive constraint relaxation in finding the causes of infeasibility could be either tedious, or may not be practical for the large-scale problems. This paper presents an alternative approach, based on use of meta-heuristic method, to pinpoint the main reasons for the failure of solution algorithms in nonlinear optimization, in general, and OPF problem, in particular. The presented approach is illustrated on commonly used IEEE 14-bus and 30-bus test networks.

UK carbon reduction targets have seen a rapid and sustained increase in renewable generation developers seeking connection to the distribution network. This study presents through simulations, the positive results that can be seen by integrating active network management techniques into the network planning scheme. The method is devised to allow distribution network operators to utilise the full available network capacity and as a result connect additional distributed generation.

The management of static security constraints (bus voltages and branch thermal limits) is one of the continuous tasks performed by operators. These constraints at any operating point can be divided into noncritical and critical constraints. To be secure, the system must fulfil both types of security constraints at all the time. While primary active and reactive power controls can manage noncritical constraints, emergency controls or remedial actions must be computed using optimal power flow (OPF) analysis to alleviate critical constraint violations. If the post contingency network involves a violation of critical constraints (which are not known a priori), conventional OPF algorithms may fail to converge and compute required control action, as the corresponding optimization problem becomes mathematically infeasible. Hence, the identification of critical constraint violations is necessary, as their location in the network decides the installation (during planning) and activation (during operation) of emergency controls. In this context, this paper first identifies the location of critical constraint violations using a metaheuristic approach, and then proposes a method to optimally place or control distributed generation to alleviate network thermal and voltage congestions during emergency situations.

Static security constraint management, formulated as security constrained optimal power flow (SCOPF), is one of the critical tasks performed during planning and operation. These constraints at any operating point can be divided into noncritical and critical constraints, where former can be managed with preventive controls, while the latter cannot. The violation of at least one critical constraint indicates that network is overstressed and needs a prompt activation of remedial actions to preserve system integrity. Accordingly, critical constraint violations (CCVs) must be identified by solving related SCOPF problem, as information on their types and locations can help to devise and activate optimal remedial actions. However, when dealing with overstressed systems, conventional SCOPF methods may not be able to identify CCVs due to problem infeasibility and there is no commonly accepted method to find related CCVs. Extending previously developed metaheuristic approach, this paper first identifies the CCVs and then proposes a novel methodology to devise the most effective remedial actions to mitigate overstressed system operating conditions. The practical relevance of the remedial actions is demonstrated using IEEE 30-bus, 39-bus, and 57-bus test networks.

Solar energy use in the UK is increasing dramatically, providing both heat energy and generation of electricity. This trend is expected to continue due to solar technologies becoming cheaper and more readily available along with low carbon government legislation such as the Renewable Heat Incentive (RHI) and Feed in Tariffs (FiTs) supporting solar energy deployment. However, the effects of climate change on the solar resource remain largely unstudied. Climate change affects cloud cover characteristics and consequently directly affects the performance of solar energy technologies. This paper investigates the UK solar irradiation resource for both the present and future climates. The present solar irradiation level was assessed through the conversion of 30 years of observed historical monthly average sunshine duration data. The method and results are validated by comparing the converted solar irradiation levels to actual solar irradiance measurements at weather stations with significant historical records of solar irradiance data. The impact of climate change is investigated across different regions of the UK by using the UKCP09 probabilistic climate change projections. We find that the current average UK annual solar resource is 101.2 Wm−2, ranging from 128.4 Wm−2 in the south of England to 71.8 Wm−2 in the northwest of Scotland. It seems likely that climate change will increase the average resource in the south of the UK, while marginally decreasing it in the Northwest. The overall effect is a mean increase of the UK solar resource, however it will have greater seasonal variability and discrepancies between geographical regions will be reinforced.

This paper presents a method for real-time active network management (ANM) control to maximize network-wide energy yield in constrained networks. Coordinated scheduling of renewable distributed generation (DG) and distribution network control assets can limit DG curtailment and significantly increase energy yield and economic performance of DG. Here an optimal power flow (OPF) approach has been developed for real-time online scheduling of network control settings to better integrate high levels of temporally and spatially variable DG from renewable energy resources. Results show that real-time prescription of ANM control settings provides a feasible alternative to network reinforcement under the existing passive management philosophy.

To reduce carbon emissions in the transportation sector, the deployment of hydrogen Fuel Cell Electric Vehicles (FCEV) is an alternative to battery Electric Vehicles. Green hydrogen fuel can be generated by converting low carbon electricity at refuelling stations equipped with water electrolysis, especially in renewable rich areas. The fast response capability of electrolysis, coupled with onsite hydrogen tanks, could potentially turn the station demand into a flexible load since the hydrogen can be stored and used when needed. This paper describes a scheme for actively operating hydrogen fuelling stations in renewable rich areas, providing flexible response to manage constraints and release network headroom for connecting renewable generation. Under this operational approach, electrolysers at the hydrogen fuelling station adaptively increase electricity consumption (i.e. overproduction) to overcome thermal overloading issues near the point of connection, and then subsequently reduce electricity consumption (i.e. under-production) to release the unscheduled hydrogen stored in onsite tanks. The results of a case study in a 11 kV distribution network are presented. Based on the results, the effectiveness of actively station operation is demonstrated with considerable reductions in curtailment.

Power to gas facilities (P2G) could absorb excess renewable energy that would otherwise be curtailed due to electricity network constraints by converting it to methane (synthetic natural gas). The produced synthetic natural gas can power gas turbines and realize bidirectional energy flow between power and natural-gas systems. P2G, therefore, has significant potential for unlocking inherent flexibility in the integrated system, but also poses new challenges of increased system complexity. A coordinated operation strategy that manages power and natural-gas network constraints together is essential to address such challenges. In this paper, a novel low-carbon economic environmental dispatch strategy is presented considering all the constraints in both systems. The multi-objective black-hole particle swarm optimization algorithm (MOBHPSO) is adopted. In addition to P2G, a gas demand management strategy is proposed to support gas flow balance. A new solving approach that combines the effective redundancy method, trust region method, and Levenberg-Marquardt method is proposed to address the complex coupled constraints. Case studies that use an integrated IEEE 39-bus power and Belgian high-calorific 20-node gas system demonstrate the effectiveness and scalability of the proposed model and optimization method. The analysis of dispatch results illustrates the benefit of P2G for the wind power accommodation, and low-carbon, economic, and environmental improvement of integrated system operation.

Chile has abundant solar and wind resources and renewable generation is becoming competitive with fossil fuel generation. However, due to renewable resource variability their large-scale integration into the electricity grid is not trivial. This study evaluates the long-term impact of grid level energy storage, specifically Pumped Thermal Energy Storage (PTES), on the penetration of solar and wind energies and on CO2 emissions reduction in Chile. A cost based linear optimization model of the Chilean electricity system is developed and used to analyse and optimize different renewable generation, transmission and energy storage scenarios until 2050. For the base scenario of decommissioning ageing coal plants and no new coal and large hydro generation, the generation gap is filled by solar photovoltaic (PV), concentrated solar power (CSP) and flexible gas generation with the associated drop of 78% in the CO2 emission factor. The integration of on-grid 8h capacity storage increases the solar PV fraction which leads to a 6% reduction in operation and investment costs by 2050. However, this does not necessarily lead to further reductions in the long term emissions. Thus, it is crucial to consider all aspects of the energy system when planning the transition to a low carbon electricity system.

The generation of renewable energy is strongly related to climate and could be vulnerable as the climate changes in the coming decades. The assessment of the potential risks is a new and evolving area of science. In light of the rapid pace of development and more prominent contribution of renewables to UK electricity generation, this work presents a summary of the current state of knowledge in the field and highlights some of the critical parameters and areas of uncertainty. An assessment of potential changes in the levelised cost of energy is presented, with the caveat that this is entirely based on existing data, which in some cases are highly uncertain. The areas of critical research for both understanding climate and engineering for adaptation are discussed.

Infrastructure is a means to an end: it is built, maintained and expanded in order to enable the functioning of society. Present infrastructure operation is characterised by: governance based on unmanaged growing demand, which is both inefficient and ultimately unsustainable; lack of integration of the end-users, in terms of the variety of their wants, needs and behaviours; separate and parallel delivery of different infrastructure streams prohibiting joint solutions. To achieve long-term sustainability, infrastructure needs to be designed and operated to provide essential service delivery at radically decreased levels of resource use. This new approach will need to: (1) incorporate the end-user, in terms of their wants and behaviours; (2) focus on the service provided; (3) use Information and Communication Technologies more effectively; (4) integrate the operation of different infrastructure systems; (5) be governed in a manner that recognises the complexity and interconnectedness of infrastructure systems; and (6) rethink current infrastructure valuation. Possible configurations incorporating these aspects with the explicit goal of contributing to long-term sustainability could be Multi-Utility Service Companies or “MUSCos”. This article presents new insights and ideas generated by considering the challenge of the transition towards a MUSCo infrastructure.

In this paper, a combinational AC/DC power flow approach is proposed for the solution of the combined AC/DC network. The unified power flow approach is extended to include DC voltage droop control. In the VSC based MTDC grids, DC droop control is regarded as more advantageous in terms of operational flexibility, as more than one VSC station controls the DC link voltage of the MTDC system. This model enables the study of the effects of DC droop control on the power flows of the combined AC/DC system for steady state studies after VSC station outages or transient conditions without needing to use its complete dynamic model. Further, the proposed approach can be extended to include multiple AC and DC grids for combined AC/DC power flow analysis. The algorithm is implemented by modifying the MATPOWER based MATACDC program and the results shows that the algorithm works efficiently.

This paper investigates the operating regimes of CCS power plants in future generation portfolios with large amounts of variable- output wind generation. An advanced electricity system dispatch model is developed and coupled with a Monte Carlo based energy storage optimization model to simulate the least-cost dispatch of an assumed thermal-energy storage generation portfolio with CCS. A historic high-resolution wind speed reanalysis dataset is employed and the proposed locations of future wind farms are used to produce plausible and internally consistent wind capacity deployment scenarios. The fundamental and structural changes that occur to CCS operating profiles and start-up/shut-down schedules are investigated for increasing levels of wind capacity, which creates seasonal and diurnal variations and potential flexibility implications. Non-linear interactions between flexible CCS power plants and other energy vectors are demonstrated for an illustrative case study example in Great Britain. This temporally explicit analysis of the short-term scheduling decisions of thermal plants with CCS highlights the asymmetric displacement of mid-merit thermal plants and the importance of using time-dependent start-up costs in wind-based unit commitment studies.

The total supply capability (TSC) is an important index for assessing the reliability of a distribution power system. In this paper, two models to evaluate the TSC are established. In the first, the TSC is acquired with the conditions that all load outages can be restored via network reconfiguration with transformers' N-1 contingencies, i.e., that all constraints related to branch thermal ratings and bus-voltage limits can be satisfied following restoration for each N-1 contingency. The second model, which is revision of the first, considers the daily load curves for different classes of customers, e.g., residential, commercial and industrial. Both models can be formulated as mixed integer problems with second-order cone programming (MISOCP), which can be solved using commercially available optimization software. Two test systems are used to demonstrate the applicability of the presented models. Numerical results show that the presented model is more accurate than the previously published models. This proposed analytical approach can be applied in a range of network planning studies, e.g., for selecting appropriate ratings of transformers, or for optimal locating of circuit breakers and distributed energy resources.