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Zero‑carbon electricity is a pre-requisite for decarbonisation of the wider economy and many scenarios envisage rapid expansion of renewables. IEA estimates a need to double the annual rate of investment to 2030. A rapid transition to low carbon electricity creates a risk to investors because projects being financed now will generate their revenue in a zero‑carbon electricity system for which there is no track record of price formation. Although the cost of renewable power has fallen greatly, future revenues from renewables are still affected by market price risks that are often outside the control of generators. If risks are difficult to quantify, they may be mis-priced, leading to inefficient premiums being added to the cost of capital, which could increase the overall cost of the transition. In this context many countries around the world provide support for low carbon energy, such as the UK Contracts for Difference. In this paper we investigate exposure to price risk caused by uncertainty over the mix of technologies used to achieve decarbonisation, which we term 'transition risk', and how this varies under different technology mixes, and for different policy regimes. We show that exposing investors to transition risk could increase the cost of delivering the renewables needed for a zero‑carbon electricity system by around 25% or £7bn/year compared to policy options that reduce exposure to wholesale market price risk. The paper concludes that as long as transition risks remain high, de-risking policy mechanisms can help to minimise the overall cost of achieving net-zero. • We characterise the current stage of the low-carbon transition as a 'build phase', requiring large volumes of investment • Uncertainty over the final physical state of the decarbonised system causes significant financial risk to investors • 'Contracts for difference' (CfDs) help to manage this 'transition risk' and reduce the cost of capital for projects • Exposing projects fully to transition risk could increase costs by a third, and raise the cost of transition by £7bn per year [ABSTRACT FROM AUTHOR]

• An innovative dew point cooling system was designed, constructed, installed and real live tested in live data centre. • The system can provide continuously stable cooling air and maintain the required temperature and humidity in data centre. • The average COP of 29.7 was achieved during the testing period. • 90 % energy saving can be achieved for data centre cooling. • Suitability, stability and reliability of the system used in data centre environment were proved. This paper presents the development and application of a super performance dew point cooling technology for data centres. The novel super performance dew point cooler showed considerably improved energy saving and carbon reduction for data centre cooling. The innovations of this technology are built upon a series of technological breakthroughs including, a novel hybrid flat/corrugated heat and mass exchanging sheets, an innovative highly water absorptive and diffusive wet-material for the sheets which enable an intermittent water supply with well-tuned water pressure and flow rate, and the optimised fan configurations. Following a list of fundamental research including theoretical, numerical and lab experimental testing of a small scale prototype system, a specialist 100 kW rated data centre dew point cooling system was dedicated designed, constructed, installed and real life tested in an operational live data centre environment, i.e., Maritime Data Centre at Hull (UK) to investigate its dynamic performance, suitability and stability for application in operational data centre environment conditions. During the testing period, the system showed its reliability and capability to remove a tremendous amount of heat dissipated from the IT equipment and maintain an adequate space temperature in the operational live data centre. The dynamic data collection and analysis during the continuous testing and monitoring period showed the average COP of 29.7 with the maximum COP of 48.3. Compared to the existing traditional vapor compression air conditioning system in the data centre, the energy saving using the super performance dew point cooling system is around 90 %. The work presented in this paper include detailed innovation aspects of the technology and the system operation, as well as the established bridging knowledges, methodology and technical procedure for bringing this new technology into real life operation which involve in data centre survey, optimum design and modularization of the specialist cooling system for data centre application, proven system installation, operating method and cooling air management for data centre as well as the assurance of the continuous sufficient cooling supply to the data centre. [ABSTRACT FROM AUTHOR]

Highlights • Suspended weights in disused mine shafts offers a new energy storage technology. • Requires minimal land-use and can make use of existing excavations. • Analysis is presented for sizing the weight to maximize the storage capacity. • Decoupled power and energy capacity makes it suitable for high power applications. • Former mines in the United Kingdom Midlands could provide 804 MWh of storage. Abstract This paper investigates the potential of using gravity energy storage with suspended weights as a new technology for redeveloping abandoned deep mine shafts. The technology has relatively low energy density, but has advantages including a power capacity decoupled from its energy capacity, no cycle-limit and the potential to be combined with compressed air energy storage. It is currently being trialled in the United Kingdom, targeting abandoned coal mines. The paper presents analysis for sizing the suspended weight to maximize the energy storage capacity, given a mine shaft's physical dimensions. In addition, it is shown that the power capacity of the system's motor and power electronics determine the maximum ramp-rate, and therefore the range of power system services that can be provided. A case study is presented, estimating the total energy storage capacity which could be obtained by converting abandoned mines in the United Kingdom Midlands, using geographic information system data from the United Kingdom Government Coal Authority Abandoned Mine Catalogue. [ABSTRACT FROM AUTHOR]

In light of the global energy and climate crises, integration of low-carbon technologies into energy systems is being considered to mitigate the high energy costs and carbon footprint. The wide range of available capacities, efficiencies, and investment costs of these technologies and their different possible operating schedules can unlock several pathways towards decarbonisation. This paper presents an optimisation framework for a public healthcare facility to determine the optimal operation schedule of the site's energy system. A detailed techno-economic analysis of low-carbon power generation, conversion, and energy storage technologies that can be incorporated into the system based on real historical data was carried out for different scenarios. The results reveal that a heat pump with a capacity of 1800 kW can replace gas boilers on-site to meet the heat demand while recovering the investment in 5 years and providing an operating and carbon cost saving of 22.47% compared to the base case. The analysis shows that a more electrified mode of operation is favoured during high gas prices, thus making electrical energy storage more attractive than thermal energy storage. While handling real data, the optimisation algorithm was sensitised to discriminate conventional energy supplies from clean energy sources by considering their carbon impact so that it minimises energy bills in a smart and eco-friendly way. The optimisation algorithm and the subsequent techno-economic analysis provide a comprehensive framework to decision-makers for facilitating energy investment decisions. The framework can be used based on the short and long term goals of the energy system, visualising the evolution of financial benefits over equipment lifetime, and understanding the environmental impacts of integrating renewable energy. • Heat pumps are the most favourable decarbonisation technology for the hospital. • The moderate variations in energy demand limit the scope of energy storage. • Fuel price surcharge favours EES over TES and reduces payback period of retrofits. • PVT's savings scale linearly with roof space until 3 times the available capacity. • Monetising the carbon footprint shortens the DPP of clean retrofit technologies. [ABSTRACT FROM AUTHOR]

The feasibility of the global energy transition may rest on the ability of nations to harness hydrogen's potential for cross-sectoral decarbonization. In countries historically reliant on natural gas for domestic heating and cooking such as the UK, hydrogen may prove critical to meeting net-zero targets and strengthening energy security. In response, the UK government is targeting industrial decarbonization via hydrogen, with parallel interest in deploying hydrogen-fueled appliances for businesses and homes. However, prospective hydrogen futures, and especially the domestic hydrogen transition, face multiple barriers which reflect the cross-sectoral dynamics of achieving economies of scale and social acceptance. Addressing these challenges calls for a deep understanding of socio-technical factors across different scales of the hydrogen economy. In response, this paper develops a socio-technical systems framework for overcoming barriers to the domestic transition, which is applied to the UK context. The paper demonstrates that future strategies should account for interactions between political, techno-economic, technical, market, and social dimensions of the hydrogen transition. In parallel to techno-economic feasibility, the right policies will be needed to create an even playing field for green hydrogen technologies, while also supporting stakeholder symbiosis and consumer buy-in. Future studies should grapple with how an effective repurposing of pipelines, policies, and public perceptions can be aligned to accelerate the development of the hydrogen economy, with maximum net benefits for society and the environment. • Desirable hydrogen futures will rest on more than a repurposing of pipelines. • Policymaking should couple techno-economic feasibility to social acceptability. • Multi-regime interactions can enable synergies for blue and green hydrogen. • Hydrogen backbones should be examined as a multi- and cross-scalar phenomenon. • Socio-technical systems thinking supports an accelerated and just transition. [ABSTRACT FROM AUTHOR]

This paper presents a novel agent-based, stochastic model, which uses game-theoretic principles to simulate Contract for Difference (CfD) auctions. The framework has use cases and implications for policymakers and renewable generators alike, and can be used by developers to prepare bidding strategy and for policymakers to empirically test auction design. The model is demonstrated through replication of the offshore wind CfD Allocation Round 3 (AR3) pot, and utilises high-level cost modelling distribution data to estimate bid prices for the competing projects. The model produces a distribution of most likely results which better categorises uncertainty, and through comparison of AR3 and simulation results, demonstrates how outcomes can be predicted with reasonable confidence by developers. Analysis show that the transmission network and grid connection charges are a significant barrier for projects in some geographical regions to be awarded a CfD contract, potentially hindering renewable deployment in those areas. Moreover, this paper demonstrates how players can use probability theory to select an optimum bidding strategy which maximises expected profit while factoring the uncertainty inherent in CfD auctions. Results show that a 1200 MW wind farm development can increase potential profits by £135 million over the CfD contract length in exchange for a 25 p.p. chance reduction in being awarded a subsidy. • A novel simulation approach is introduced for analysing the UK's CfD auction • We show how players can attempt to optimise their bid based on expected profits • We demonstrate how uncertainty in auctions can be characterised by developers • Transmission charges act as a barrier for offshore wind projects in some regions [ABSTRACT FROM AUTHOR]

• Assesses grid impact of EV charging under diverse tariff and control strategies. • Stepwise Time of Use tariffs cause higher peak loads than on-demand EV charging. • Smart tariffs reduce grid carbon emissions with dynamic tariffs most effective. • Diversity of tariffs should be encouraged to mitigate the risk from demand peaks. • Smart tariffs fail to avoid local EV peak loads with capacity management necessary. With the rapid increase in ownership of Electric Vehicles (EVs), widespread concern has been raised regarding the potential for EV charging demand to overload electricity grids. Smart control of charging is advocated as a solution, gaining attention from business and support from policymakers. However, the ultimate grid benefits (or disbenefits) of smart charging will follow from a combination of user behaviour and pricing arrangements / tariffs. Local clustering of vehicle uptake can lead to unintended consequences as national incentives fail to align with local pressures. In this paper, we describe a simulation of the dynamic electricity demand pattern arising from a fleet of grid connected EVs. The model developed for this study combines stochastic sampling of data from a UK-based smart charging trial (Western Power Distribution's Electric Nation project) with a set of plausible tariffs, including a strategy which specifically seeks to minimize grid carbon emissions. This provides insights into the potential impacts of EV charging by encompassing a wider range of tariffs than previously assessed, while also separating the control actions of optimising cost and managing capacity. We examine the carbon implications of tariff choice and introduce a range of grid overload metrics that reveal nuances in the tariff implications and evolution of impacts as EV penetration increases. The results show that smart charging is not necessarily a better solution for the grid compared to on-demand charging. Stepwise tariffs, currently favoured by UK energy suppliers, present a particular risk. Such tariffs can tend to increase load synchronization by shifting load towards periods where more cars are connected and awaiting charge. This can lead to an increased peak load even at moderate EV uptake levels. Dynamic tariffs proved preferable but still increase peak demand at higher vehicle uptakes. All smart tariffs offer a strong carbon benefit, but, again, current stepwise tariffs are failing to realise the full potential that could be realized by targeting low carbon time periods. Separate local capacity management was able to eliminate overload at the secondary substation, even with very high EV uptake, with only rare, very small levels of unserved demand. [ABSTRACT FROM AUTHOR]

• Annual EV LiB recycling demand in the UK is projected to grow dramatically and reach 12.7 GWh by 2040, sufficient to feed 60% of a 20GWh battery plant in 2040. • Financial incentives, robust EV commitment and strict EOL LiB enforcement, are required to support the nascent recycling industry. • Scaling up a geospatially-optimised central recycling plant is the most cost-effective option, while the domestic market can host up to three pyrometallurgical recyclers or seven hydrometallurgical recyclers. • Closed-loop recycling is estimated to substantially reduce cost, save resources and cut emissions of EV battery manufacture. Rapid electrification of the transport system will generate substantial volumes of Lithium-ion-battery (LiB) waste as batteries reach their end-of-life. Much attention focuses on the recycling processes, neglecting a broader systemic view that considers the concentration of the costs and impacts associated with logistics and transportation. This paper provides an economic, environmental and geospatial analysis of a future LiB recycling industry in the UK. Hitherto, state-of-the-art assessment methods have evaluated life cycle impacts and costs but have not considered the geographical layer of the problem. This paper develops a GSC derived supply chain model for the UK electric vehicle and end-of-life vehicle battery industry. Considering both pyrometallurgical and hydrometallurgical recycling technologies, the optimisation process takes into account anticipated EV volumes, and, based on anticipated near-term technological evolution of LiBs, the evolution of the mix of battery cathodes in production, and presents a number of scenarios to show where LiB recycling facilities should ideally be geographically located. An economic and environmental assessment based on a customised EverBatt model is provided. [ABSTRACT FROM AUTHOR]

Highlights • Energy consumption in higher education institutions (HEIs) modelled. • For the first time, panel data used, with cross-sectional and time-series variations. • Over time and across HEIs, energy use increases as income and size increase. • Economies of scale as HEIs grow, but energy use still rising with sector growth. • Research universities more energy intensive. Abstract Among the various sustainability goals of higher education institutions (HEIs), reducing energy use and carbon emissions are particularly important. However, not much is known about energy demand from the higher education sector – especially since there is a lack of robust models of energy demand in this sector. This paper, the first to utilize a panel dataset and advanced panel econometric techniques in order to model energy use in higher education, investigates variations in energy use between HEIs (cross-sectional analysis), and also changes in energy use over time (temporal analysis), using the UK as a case study. We argue that panel dataset and methods are more useful for understanding growth (and reduction) in energy use within the HE sector than the methods used within previous cross-sectional studies. Results show that, over time and also across the sector, energy consumption in the HEIs increases with increases in income and floor space, but at a slower rate. As HEIs grow overall (in terms of income, floor space, student and staff number) over time, they become more 'energy efficient' (using less energy per unit of area, population or income), indicating economies of scale in the temporal dimension. Results also show that after controlling for income and size, research intensive HEIs consume more energy. We also find a small but statistically significant effect of energy prices on energy consumption, as might be expected. Simulation using the model parameters for an example scenario suggests that energy consumption will continue to increase unless there is a significant change in the policies driving income growth and spatial expansion in the HE sector in the UK. [ABSTRACT FROM AUTHOR]

In this paper, a systematic review of academic literature and policy papers since 2008 is undertaken with an aim of identifying the prevalent energy systems models and tools in the UK. A list of all referenced models is presented and the literature is analysed with regards sectoral coverage and technological inclusion, as well as mathematical structure of models. The paper compares available models using an appropriate classification schema, the introduction of which is aimed at making the model landscape more accessible and perspicuous, thereby enhancing the diversity of models within use. The distinct classification presented in this paper comprises three sections, which specify the model purpose and structure, technological detail and mathematical approach. The schema is not designed to be comprehensive, but rather to be a broad classification with pertinent level of information required to differentiate between models. As an example, the UK model landscape is considered and 22 models are classified in three tables, as per the proposed schema. [ABSTRACT FROM AUTHOR]

Dynamic tariffs play an important role in demand response, contributing to smoothing power consumption and reducing generation capacity requirement and carbon emission. However, in the existing works, tariffs are usually designed without comprehensive consideration, such as potential user responses to tariffs. Thus, assuming an electricity trading market contains a utility company and multiple residential users, a dynamic tariff design method is proposed in this paper, considering user responses to tariff changes. Leveraging the non-intrusive load monitoring technique, rated power and user preference features for each appliance are acquired by the utility company to quantify user comfort (discomfort) based on derived user appliance usage habits. Then, a bi-level Stackelberg game model is built on the supply side for designing optimal dynamic tariffs and imitating the influence of tariff changes on DR plans for users. The upper level represents the utility company, trying to maximize utility profit, social welfare and carbon emission reduction. While the lower level represents users, aiming to minimize electricity bills and user discomfort. By solving such an optimization problem with multiple objectives, a novel hybrid probabilistic multi-objective evolutionary algorithm balancing evolutionary efficiency and stability is applied where random forest is adopted to boost performance. The proposed model is benchmarked with two state-of-the-art pricing methods and validated on a publicly accessible REFIT dataset, where low-rate power measurements are collected from real houses in the UK. The experimental results show the proposed model generally outperforms benchmarks on dynamic tariff design in achieving peak-shaving and low carbon emission while preserving user satisfaction. Furthermore, a case study is implemented, which verifies the necessity of various objectives employed in the proposed method. • A bi-level Stackelberg game model for designing tariff based on user demand response. • Electricity-consuming feature extraction on non-intrusive load monitoring results. • A hybrid probabilistic multi-objective algorithm balancing efficiency and stability. • A random forest classifier for reducing computational complexity in late iterations. • Extensive case studies on evaluating objectives and visualizing optimization process. [ABSTRACT FROM AUTHOR]

Offshore wind energy is experiencing a rising importance for many electricity markets. While the effects of wind energy overall on electricity prices have been thoroughly studied, it remains unknown if offshore wind has a different impact on electricity prices than onshore wind. The aim of this paper is therefore to estimate the effect of offshore wind energy on wholesale electricity prices and how it differs to the impact of onshore wind. For this purpose, we propose three time series models to describe the development of electricity prices in Germany, Western Denmark and Great Britain from 2015–2018. We focus on the impact on the level and volatility of electricity prices using different time series models such as AR-GARCH or ARMA. Following these models, we can identify that onshore and offshore wind power do have a significantly different impact on wholesale electricity prices in the investigated countries. Based on our results, we discuss the implications of our findings for electricity markets and policy makers. • The results indicate different impacts of off- and onshore wind on wholesale prices • Offshore wind tends to reduce price levels and price volatility more than onshore • The differential impact of offshore wind is mainly due to a more constant feed-in [ABSTRACT FROM AUTHOR]

• Isolating the impact of heat pumps on national electricity under historic weather. • Validations with measured data indicate the best heat demand generation method. • 41 % heat pumps would increase Great Britain's winter electricity demand by 30 TWh. • Year-to-year variability of electricity demand increases by 37 %. • 25 % inaccuracy of future peak electricity demand estimates. Amongst all future developments it is the electrification of heat that is anticipated to have the largest impact on seasonal and interannual electricity demand. There is therefore a need to accurately quantify and assess this impact. This paper uses a combination of existing advanced techniques to modify the historic electricity demand to incorporate the impact of heat pumps alone for long-term historic weather data using Great Britain as an example. The methods for generating time series were compared and extensively validated. This includes comparisons with measured data that have not been used previously for this purpose. The research reveals that for predicted 2050 heat pump penetration levels the monthly demand for electricity doubles in winter. This leads to an increase of approximately 30 TWh for each winter month and a 37 % increase in year-to-year variability of electricity demand due to weather. Peak electricity demand is very sensitive to the method of generating heat demand and the assumptions on hourly heat pump operating profiles, suggesting inaccuracies of 25 % in estimates of future peak demand. This work, rather than just assessing the impact of projected changes provides a reference case for policy makers to guide the decision process and planning for future scenarios. [ABSTRACT FROM AUTHOR]

This paper presents new evidence from a nationwide cross-project meta-study investigating the magnitude and extent of the difference between designed and measured thermal performance of the building fabric of 188 low energy dwellings in the UK. The dataset was drawn from the UK Government’s national Building Performance Evaluation programme, and comprises 50 Passivhaus (PH) and 138 non-Passivhaus (NPH) dwellings, covering different built forms and construction systems. The difference between designed and measured values of air permeability (AP), external wall/roof thermal transmittance (U-value) and whole house heat loss were statistically analysed, along with a review of thermal imaging data to explain any discrepancies. The results showed that fabric thermal performance gap was widespread especially in terms of AP, although the magnitude of underperformance was much less in PH dwellings. While measured AP had good correlation with measured space heating energy for PH dwellings, there was no relationship between the two for NPH dwellings. The regression analysis indicated that for every 1 m 3 /h/m 2 reduction in designed air permeability, the gap increased by 0.8 m 3 /h/m 2 @50 Pa. Monte Carlo analysis showed that likelihood of AP gap was 78% in NPH dwellings designed to 5 m 3 /h/m 2 @50 Pa or lower. The study provides useful evidence for improving the fabric thermal performance of new housing through in-situ testing. [ABSTRACT FROM AUTHOR]

Relatively little energy research has attended to justice considerations. The limited energy justice inquiry that exists focuses on justice in relation to impacts of energy production, consumption, and policies that either exacerbate or seek to mitigate distributive and procedural concerns. Programmes aimed at facilitating the so-called ‘energy transition’ have strong implications for energy justice. For example, efforts to reduce energy consumption and/or carbon emissions, policies to increase energy security, and programmes to increase energy access and affordability all address distributive concerns. Nevertheless, the costs associated with meeting such goals and running such programmes also have justice implications and should be viewed alongside the other aforementioned normative issues as an aspect of energy justice. Here, we examine public perceptions of who should fund programmes designed to ease the transition to a more sustainable and equitable energy system, finding most responsibility assigned to energy companies, and beliefs about procedural justice meaningfully shaping thoughts on who should pay. Our UK-based mixed methods inquiry reveals that whilst our respondents (survey) and participants (focus groups) accept some personal costs directed towards governmental programmes that could reduce energy injustices, acceptance is dependent on several factors, including perceived importance of distributive justice and whether the energy system exhibits procedural justice. The influence of normative factors on cost acceptance has implications for feasibility of polices to promote energy justice. We conducted a survey (N = 3,150), followed by five focus groups (N = 6–9 each) throughout Great Britain with survey respondents to explore further their answers and explain some of our quantitative findings. We conclude this paper with tangible policy recommendations for government, such as the amount (cost) and types of environmental and social levies that are viable, based on their public acceptance, and suggestions for other approaches to funding energy transitions, so as not to exceed the limits of public acceptance. [ABSTRACT FROM AUTHOR]

Electric heat pumps (HPs), hybrid heating technology and district heating networks (DHN) are the main low-carbon heating technologies that can deliver the ambitious carbon reduction target in the UK. The optimal design of the heating system on a national scale to maximize the economic benefits regarding both investment cost and operation cost while satisfying the carbon target remains an open question. This paper aims to compare the economic advantages as well as the associated impacts on the electricity system under the full deployments of ASHP (air source HP), hybrid HP-Bs (ASHP and gas boilers) and DHNs. To do so, a novel whole-system approach is applied with the explicit consideration of the coordinated operation between heat and electricity system. The optimized strategy for heat sector decarbonisation is also demonstrated, providing an outline of the optimal deployment for different heating technologies in terms of their penetrations and deployed areas. The UK case study suggests the significant economic advantage of the hybrid HP-B over the other two heating technologies, while DHN may play an important role in urban areas under the optimized heat decarbonisation strategy. The results also clearly demonstrate the changes on the electricity side driven by the different decarbonisation strategies in the heating system. [ABSTRACT FROM AUTHOR]

The UK is in the early stages of developing a shale gas industry and to date six test wells have been drilled but none yet exploited commercially. Some argue that shale gas could reduce energy prices and improve national energy security. However, the costs of bringing commercial-size wells into operation are uncertain and the impact shale gas could have on the UK energy market is currently unknown. Therefore, this paper evaluates the economic viability of developing a UK shale gas industry and the impacts it could have on the UK gas and electricity markets and consumer energy bills up to 2030. The estimated life cycle (levelised) costs of shale gas production range from 0.47 to 56.74 pence/MJ (0.61–73 US$ cents/MJ), with an average value of 4.64 pence/MJ. The break-even price at which shale gas can be sold varies between 0.95 and 114.44 pence/MJ, averaging at 9.47 pence/MJ, depending on the volume of gas produced by a shale gas well. The latter is two times higher than imported liquefied natural gas, around 30% more expensive than UK natural gas and three times greater than the price of US shale gas. Electricity from shale gas is on average 17% more expensive than from domestic conventional gas but still more competitive than most other electricity options, including coal and renewables. However, the impact of shale gas on the energy market would be limited across the expected range of shale gas penetration into the gas and electricity mixes, suggesting that it would have little effect on energy prices. This is reflected in an almost negligible impact on consumer energy bills. The potential of shale gas to boost the UK economy is also limited, contributing 0.017–0.033% to the GDP. This is an order of magnitude lower than the contribution of US shale gas to its GDP (0.2%), indicating that the economic success of shale gas in the US may not be replicated in the UK. These findings will be of interest to shale gas developers and policy makers not only in the UK but in other countries considering exploitation of shale gas resources. [ABSTRACT FROM AUTHOR]

Understanding homeowners' renovation decisions is essential for policy and business activity to improve the efficiency of owner-occupied housing stock. This paper develops, validates and applies a novel modelling framework for explaining renovation decisions, with an emphasis on energy-efficiency measures. The framework is tested using quantitative data from a nationally-representative survey of owner-occupied households in the UK (n = 1028). The modelling advances formal representations of renovation decisions by including background conditions of domestic life to which renovating is an adaptive response. Path analysis confirms that three conditions of domestic life are particularly influential on renovation decisions: balancing competing commitments for how space at home is used; signalling identity through homemaking activities; and managing physical vulnerabilities of household members. These conditions of domestic life also capture the influence of property characteristics (age, type) and household characteristics (size, composition, length of tenure) on renovation decisions but with greater descriptive realism. Multivariate probit models are used to provide rigorous, transparent and analytically tractable representations of the full renovation decision process. Model fits to the representative national sample of UK homeowners are good. The modelling shows that renovation intentions emerge initially from certain conditions of domestic life at which point energy efficiency is not a distinctive type of renovation. The modelling also shows clearly that influences on renovation decisions change through the decision process. This has important implications for policy and service providers. Efficiency measures should be bundled into broader types of home improvements, and incentives should target the underlying reasons why homeowners decide to renovate in the first place. [ABSTRACT FROM AUTHOR]

This study explores and quantifies the social costs and benefits of grid-scale electrical energy storage (EES) projects in Great Britain. The case study for this paper is the Smarter Network Storage project, a 6 MW/10 MWh lithium battery placed at the Leighton Buzzard Primary substation to meet growing local peak demand requirements. This study analyses both the locational and system-wide benefits to grid-scale EES, determines the realistic combination of those social benefits, and juxtaposes them against the social costs across the useful lifecycle of the battery to determine the techno-economic performance. Risk and uncertainty from the benefit streams, cost elements, battery lifespan, and discount rate are incorporated into a Monte Carlo simulation. Using this framework, society can be guided to cost-effectively invest in EES as a grid modernization asset to facilitate the transition to a reliable, affordable, and clean power system. [ABSTRACT FROM AUTHOR]

In the UK, the Contract for Difference (CfD) subsidies for renewable energy generation are awarded through a competitive auction process. This paper simulates the most recent CfD auction for offshore wind, using a novel methodology to assist developers in preparing their bid strategy and for policymakers to test auction efficiency. The simulation's results show developer's leading strategy is to shade their bid to increase auction pay-off. A developer's incentive to shade their bid depends on the project's capacity and minimum bid price; the offshore wind farm Hornsea 3 has the greatest incentive to shade its bid as its optimum bid price is further from its cost price, and results in the highest expected value of additional auction pay-off. The median strike price estimated by the model is £39.23/MWh, and the most likely winners, as predicted from the simulations, are Hornsea 3, Inch Cape, East Anglia 3 and Norfolk Boreas. Published auction results show that the estimated strike price from the simulation is 5% higher than the £37.35/MWh awarded strike price; however, the model successfully predicted the winners. Further analysis of results demonstrates that developers adopted a risk-averse bidding strategy, bidding at a pre-determined floor (coexist) price, guaranteeing subsidy. As a result, £38 million of the subsidy budget was unused. • Stochastic modelling of CfD auction case studies. • Auction allocation efficiency tested using novel methodology. • Uncertainty is characterised to inform bidding strategy and mitigate against winner's curse. • Results indicate larger players have a greater incentive to bid strategically. • Policymakers can readily increase auction competition, but increase non-realisation risk. [ABSTRACT FROM AUTHOR]

• A novel multicriteria (MCDM) framework using AHP and q-ROLPBM operator is presented. • We incorporate stakeholders' degree of pessimistic and optimistic rate for all criteria. • Environmental and economic are the most crucial dimensions followed by social and technical ones. • e-fuel and e-biofuel are found to be the two top ranked production pathways. • Findings are valuable for policy/investment development for sustainable fuel production. It is widely believed that alternative low carbon fuels (ALCF) can be instrumental in achieving the transportation sector's decarbonization goal. Unlike conventional fossil-based fuels, ALCF can be produced through a combination of different chemical processes and feedstocks. The inherent complexity of the problem justifies the multi-criteria decision-making (MCDM) approach to support decision-making in the presence of multiple criteria and data uncertainty. In this paper, we propose a novel stakeholder participation-based MCDM framework integrating experts' perspectives on ALCF production pathways using the analytics hierarchy process (AHP) and the q-rung orthopair linguistic partition Bonferroni mean (q-ROLPBM) operator. The key merit of our approach lies in treating criteria of different dimensions as heterogeneous indicators while considering the mutual influence between criteria within the same dimension. The proposed framework is applied to evaluate four ALCF production pathways against 13 criteria categorised under economic, environmental, technical, and social dimensions for the case of the United Kingdom (UK). Our analysis revealed the environmental and the economic dimensions to be the most important, followed by the social and technical evaluation dimensions. The e-fuel followed by the e-biofuel are found to be the two top-ranked production pathways that utilise the electrochemical reduction process and its combination with anaerobic digestion. These findings, along with our recommendations, provide decision-makers with guidelines on ALCF production pathway selection and formulate effective policies for investment. [ABSTRACT FROM AUTHOR]

With the emergence of energy communities, where a number of prosumers invest in shared generation and storage, the issue of fair allocation of benefits is increasingly important. The Shapley value has attracted increasing interest for redistribution in energy settings — however, computing it exactly is intractable beyond a few dozen prosumers. In this paper, we first conduct a systematic review of the literature on the use of Shapley value in energy-related applications, as well as efforts to compute or approximate it. Next, we formalise the main methods for approximating the Shapley value in community energy settings, and propose a new one, which we call the stratified expected value approximation. To compare the performance of these methods, we design a novel method for exact Shapley value computation, which can be applied to communities of up to several hundred agents by clustering the prosumers into a smaller number of demand profiles. We perform a large-scale experimental comparison of the proposed methods, for communities of up to 200 prosumers, using large-scale, publicly available data from two large-scale energy trials in the UK (UKERC Energy Data Centre, 2017, UK Power Networks Innovation, 2021). Our analysis shows that, as the number of agents in the community increases, the relative difference to the exact Shapley value converges to under 1% for all the approximation methods considered. In particular, for most experimental scenarios, we show that there is no statistical difference between the newly proposed stratified expected value method and the existing state-of-the-art method that uses adaptive sampling (O'Brien et al., 2015), although the cost of computation for large communities is an order of magnitude lower. • Systematic study of Shapley value approximation methods for community energy applications. • New deterministic method for approximating the Shapley value for large communities. • New methodology to allow systematic comparison of different Shapley approximations. • Experimental comparison using data from two large-scale energy trials from the UK. • Redistribution very close to real Shapley values, but at much lower computation cost. [ABSTRACT FROM AUTHOR]

• Methodology for capital and operational indicators estimation is presented. • Introducing stochastic operation and maintenance modelling for uncertainty reduction. • Reference database for further works on floating wind farms provided. • Results shows agreement with observed values for offshore wind projects. • Comparison against previous work shows underestimation of operational costs. Floating offshore wind is rapidly gaining traction in deep water locations. As with all new technologies, to gain the confidence of developers and investors, the technical and economic feasibility of this technology must be proven and robust cost estimates are necessary. In this paper, the authors present a methodology to calculate the capital and operational indicators of a floating wind farm over its project lifetime. A set of computational models is used to reduce the uncertainties in the estimation of the technical and economical parameters. In particular, the effect of using detailed operation and maintenance models and strategies allows a better estimation of operational cost. The paper highlights the requirements and specific adjustments considered for floating offshore wind technology. The methodology is demonstrated for two case studies inspired by real floating wind installations in the United Kingdom, namely the Hywind and Kincardine projects. The related input data, gathered from publicly available sources, constitute a reference database for future studies in the floating offshore wind sector. Results are presented for the two case studies. These show that availability and energy production are in line with typical values for offshore wind projects, and highlight the substantial contribution of operational expenses to the cost of energy. Results are also compared against previous estimations for floating offshore wind projects, showing satisfactory agreement for the overall project costs but an underestimation of operation and maintenance costs in previous studies. This highlights the importance of using detailed operation and maintenance models to adequately capture operational expenses. [ABSTRACT FROM AUTHOR]

In this paper, we examine the electrical power-generation potential of a domestic-scale solar combined heating and power (S-CHP) system featuring an organic Rankine cycle (ORC) engine and a 15-m 2 non-concentrated solar-thermal collector array. The system is simulated with a range of organic working fluids and its performance is optimised for operation in the UK climate. The findings are applicable to similar geographical locations with significant cloud coverage, a low solar resource and limited installation areas. A key feature of the system’s design is the implementation of fixed fluid flow-rates during operation in order to avoid penalties in the performance of components suffered at part-load. Steady operation under varying solar irradiance conditions is provided by way of a working-fluid buffer vessel at the evaporator outlet, which is maintained at the evaporation temperature and pressure of the ORC. By incorporating a two-stage solar collector/evaporator configuration, a maximum net annual electrical work output of 1070 kW h yr −1 (continuous average power of 122 W) and a solar-to-electrical efficiency of 6.3% is reported with HFC-245ca as the working fluid at an optimal evaporation saturation temperature of 126 °C (corresponding to an evaporation pressure of 16.2 bar). This is equivalent to ∼32% of the electricity demand of a typical/average UK home, and represents an improvement of more than 50% over a recent effort by the same authors based on an earlier S-CHP system configuration and HFC-245fa as the working fluid [1] , thus highlighting the gains possible when using optimal system configurations and fluids and suggesting that significant further improvements may be possible. A performance and simple cost comparison with stand-alone, side-by-side PV and solar-thermal heating systems is presented. [ABSTRACT FROM AUTHOR]

This paper quantifies the impacts of policy objectives on the composition of an optimum new passenger vehicle fleet. The objectives are to reduce individually absolute energy use and associated emissions of CO 2 , NO x and PM 2.5 . This work combines a top down, diversity-led approach to fleet composition with bottom-up models of 23 powertrain variants across nine vehicle segments. Changing the annual distance travelled only led to the smallest change in fleet composition because driving less mitigated the need to shift to smaller vehicles or more efficient powertrains. Instead, managing activity led to a ‘re-petrolisation’ of the fleet which yielded the largest reductions in emissions of NO x and PM 2.5 . The hybrid approach of changing annual distance travelled and increasing willingness to accept longer payback times incorporates management of vehicle activity with consumers’ demand for novel vehicle powertrains. Combining these changes in behaviour, without feebates, allowed the hybrid approach to return the largest reductions in energy use and CO 2 emissions. Introducing feebates makes low-emitting vehicles more affordable and represents a supply side push for novel powertrains. The largest reductions in energy use and associated emissions occurred without any consumer behaviour change, but required large fees (£79–99 per g CO 2 /km) on high-emitting vehicles and were achieved using the most specialised fleets. However, such fleets may not present consumers with sufficient choice to be attractive. The fleet with best diversity by vehicle size and powertrain type was achieved with both the external incentive of the feebate and consumers modifying their activity. This work has a number of potential audiences: governments and policy makers may use the framework to understand how to accommodate the growth in vehicle use with pledged reductions in emissions; and original equipment manufacturers may take advantage of the bottom-up, vehicle powertrain inputs to understand the role their technology can play in a fleet under the influence of consumer behaviour change, external incentives and policy objectives. [ABSTRACT FROM AUTHOR]

• Social acceptance research should be critical, systematic, and holistic. • Domestic hydrogen acceptance is shaped by the interplay of five distinct dimensions. • Hydrogen acceptance hinges on cognitive, sociopolitical, and sociocultural legitimacy. • Hydrogen acceptance is composed of a rich matrix of potential consumer responses. • Overcoming attitudinal and behavioral barriers is critical to the hydrogen transition. The 'deep' decarbonization of the residential sector is a priority for meeting national climate change targets, especially in countries such as the UK where natural gas has been the dominant fuel source for over half a century. Hydrogen blending and repurposing the national grid to supply low-carbon hydrogen gas may offer respective short- and long-term solutions to achieving emissions reduction across parts of the housing sector. Despite this imperative, the social acceptance of domestic hydrogen energy technologies remains underexplored by sustainability scholars, with limited insights regarding consumer perceptions and expectations of the transition. A knowledge deficit of this magnitude is likely to hinder effective policymaking and may result in sub-optimal rollout strategies that derail the trajectory of the net zero agenda. Addressing this knowledge gap, this study develops a conceptual framework for examining the consumer-facing side of the hydrogen transition. The paper affirms that the spatiotemporal patterns of renewable energy adoption are shaped by a range of interacting scales, dimensions, and factors. The UK's emerging hydrogen landscape and its actor-network is characterized as a heterogenous system, composed of dynamic relationships and interdependencies. Future studies should engage with domestic hydrogen acceptance as a co-evolving, multi-scalar phenomenon rooted in the interplay of five distinct dimensions: attitudinal, sociopolitical, community, market, and behavioral acceptance. If arrived to, behavioral acceptance helps realize the domestication of hydrogen heating and cooking, established on grounds on cognitive, sociopolitical, and sociocultural legitimacy. The research community should internalize the complexity and richness of consumer attitudes and responses, through a more critical and reflexive approach to the study of social acceptance. [ABSTRACT FROM AUTHOR]

The evolution towards Smart Grids (SGs) represents an important opportunity for modernization of the energy industry. It is characterized by a bidirectional flow of information and energy between consumers and suppliers. However, the rapid increase of energy demands in residential areas is becoming a challenging problem. In order to address this issue, Demand-Side Management (DSM) has proven to be an effective solution. In this paper, we propose LOSISH, a price-based Demand Response (DR) system for load scheduling in residential Smart Homes (SHs) that achieves a trade-off between electricity payments and consumer's discomfort. Our proposed system considers Renewable Energy Sources (RESs), Battery Energy Storage System (BESS) and Plug-in Electric Vehicle (PEV). We formulate our scheduling as a constrained optimization problem and we propose a new hybrid algorithm to solve it. The latter combines two well known heuristic algorithms: Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO). Moreover, we propose a new clustering algorithm based on Machine Learning (ML) to extract consumer's preferences from a real dataset that contains the historical consumption patterns of his smart appliances. We test our approach on real data traces obtained from a SH and we set up an experiment to evaluate our algorithm on a Raspberry Pi and measure its energy consumption. To prove the effectiveness of our approach, we compare our results with another approach from the literature in terms of electricity bill, Peak-to-Average Ratio (PAR), energy consumption, and execution time. Numerical results show that LOSISH outperforms the other approach in terms of electricity bill (up to 52.92% cheaper), PAR (up to 44% decrease in peak demands), energy consumption (up to 69.44% less consumption), and execution time (up to 63.15% faster). • The load scheduling problem in residential smart homes is studied. • A constrained problem is formulated to make a trade-off between costs and comfort. • A hybrid heuristic algorithm and a Machine Learning clustering model are proposed. • Real data collected from a smart home in the UK was used to validate our approach. • Simulation and experimental results demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This paper presents an advanced stochastic analytical framework to quantify the benefits of smart electric vehicles (EVs) and heat pumps (HPs) on the carbon emission and the integration cost of renewable energy sources (RES) in the future UK electricity system. The typical operating patterns of EVs/HPs as well as the potential flexibility to perform demand shifting and frequency response are sourced from recent UK trials. A comprehensive range of case studies across several future UK scenarios suggest that smart EVs/HPs could deliver measurable carbon reductions by enabling a more efficient operation of the electricity system, while at the same time making the integration of electrified transport and heating demand significantly less carbon intensive. The second set of case studies establish that smart EVs/HPs have significant potential to support cost-efficient RES integration by reducing: (a) RES balancing cost, (b) cost of required back-up generation capacity, and (c) cost of additional low-carbon capacity required to offset lower fuel efficiency and curtailed RES output while achieving the same emission target. Frequency response provision from EVs/HPs could significantly enhance both the carbon benefit and the RES integration benefit of smart EVs/HPs. [ABSTRACT FROM AUTHOR]

Demand elasticity for petrol or diesel is an important policy parameter, both from energy security and global warming perspective. Despite an abundance of literature on petrol demand, there are few studies on diesel demand, and even fewer on demand by different vehicle types. This paper aims to model diesel demand for different freight duty vehicle types (e.g. heavy vs. light goods vehicles and rigid vs. articulated trucks) in the UK. We argue that the switch to diesel from petrol engines in the light vehicles sectors could have biased earlier petrol or diesel demand elasticities in Europe, and show that it was indeed the case for the light goods vehicle sector. Results show that both light and heavy goods vehicles have similar income elasticities, although within the heavy duty sector, articulated trucks are more elastic than rigid trucks. Overall, heavy goods vehicles were responsive to fuel prices, but light goods vehicles were not. Within the heavy duty sector, rigid trucks showed statistically significant price elasticity, but articulated trucks did not respond to changes in fuel prices. Our results show that price-based policies to curb fuel consumption from the light or heavy goods vehicles are unlikely to be effective. [ABSTRACT FROM AUTHOR]

There is currently a lack of data recording the carbon and emissions inventory at household level. This paper presents a multi-disciplinary, bottom-up approach for estimation and analysis of the carbon emissions, and the organic carbon (OC) stored in gardens, using a sample of 575 households across a UK city. The annual emission of carbon dioxide emissions from energy used in the homes was measured, personal transport emissions were assessed through a household survey and OC stores estimated from soil sampling and vegetation surveys. The results showed that overall carbon patterns were skewed with highest emitting third of the households being responsible for more than 50% of the emissions and around 50% of garden OC storage. There was diversity in the relative contribution that gas, electricity and personal transport made to each household’s total and different patterns were observed for high, medium and low emitting households. Targeting households with high carbon emissions from one source would not reliably identify them as high emitters overall. While carbon emissions could not be offset by growing trees in gardens, there were considerable amounts of stored OC in gardens which ought to be protected. Exploratory analysis of the multiple drivers of emissions was conducted using a combination of primary and secondary data. These findings will be relevant in devising effective policy instruments for combatting city scale green-house gas emissions from domestic end-use energy demand. [ABSTRACT FROM AUTHOR]

In order to pursue the low-carbon development around the world, a large scale of renewable generation will be connected to the power systems. Take the Great Britain (GB) as an example, the GB power system has a large wind energy integration potential. The intermittency of wind generation will have great impact on the system frequency stability. Electric vehicles (EVs) have a crucial role in decarbonizing the transport sector. To increase the utilization of wind energy, EVs are suggested to provide frequency response service to the power system due to their quick power reaction characteristic. This paper proposes a general dynamic EV frequency control strategy considering the travelling behavior of the EV users. A droop control method is used to regulate the EV charging/discharging power according to the frequency signal. A Forced-Charge Boundary (FCB) and a Forced-Charge Area (FCA) are proposed to guarantee sufficient energy in the EV battery for user’s travel at the plug-out time. A dynamic Virtual Energy Storage System (VESS) is developed to evaluate the frequency response capacity of the EV clusters. In the case study, the model of the GB power system is used to investigate the frequency control effect of the control strategy. The simulation results show that the proposed strategy provides effective EV frequency response to the power system and thus is able to facilitate the integration of wind energy. [ABSTRACT FROM AUTHOR]

A techno-economic analysis is undertaken to assess hybrid PV/solar-thermal (PVT) systems for distributed electricity and hot-water provision in a typical house in London, UK. In earlier work (Herrando et al., 2014), a system model based on a PVT collector with water as the cooling medium (PVT/w) was used to estimate average year-long system performance. The results showed that for low solar irradiance levels and low ambient temperatures, such as those associated with the UK climate, a higher coverage of total household energy demands and higher CO 2 emission savings can be achieved by the complete coverage of the solar collector with PV and a relatively low collector cooling flow-rate. Such a PVT/w system demonstrated an annual electricity generation of 2.3 MW h, or a 51% coverage of the household’s electrical demand (compared to an equivalent PV-only value of 49%), plus a significant annual water heating potential of to 1.0 MW h, or a 36% coverage of the hot-water demand. In addition, this system allowed for a reduction in CO 2 emissions amounting to 16.0 tonnes over a life-time of 20 years due to the reduction in electrical power drawn from the grid and gas taken from the mains for water heating, and a 14-tonne corresponding displacement of primary fossil-fuel consumption. Both the emissions and fossil-fuel consumption reductions are significantly larger (by 36% and 18%, respectively) than those achieved by an equivalent PV-only system with the same peak rating/installed capacity. The present paper proceeds further, by considering the economic aspects of PVT technology, based on which invaluable policy-related conclusions can be drawn concerning the incentives that would need to be in place to accelerate the widespread uptake of such systems. It is found that, with an electricity-only Feed-In Tariff (FIT) support rate at 43.3 p/kW h over 20 years, the system cost estimates of optimised PVT/w systems have an 11.2-year discounted payback period (PV-only: 6.8 years). The role and impact of heat-based incentives is also studied. The implementation of a domestic Renewable Heat Incentive (RHI) at a rate of 8.5 p/kW h in quarterly payments leads to a payback reduction of about 1 year. If this incentive is given as a one-off voucher at the beginning of the system’s lifetime, the payback is reduced by about 2 years. With a RHI rate of 20 p/kW h (about half of the FIT rate) PVT technology would have approximately the same payback as PV. It is concluded that, if primary energy (currently dominated by fossil fuels) and CO 2 emission minimisation are important goals of national energy policy, PVT systems offer a significantly improved proposition over equivalent PV-only systems, but at an elevated cost. This is in need of careful reflection when developing relevant policy and considering technology incentivation. Currently, although heat outweighs electricity consumption by a factor of about 4 (by energy unit) in the UK domestic sector, the support landscape has strongly favoured electrical microgeneration, being inclined in favour of PV technology, which has been experiencing a well-documented exponential growth over recent decades. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic led to partial or total lockdowns in several countries during the first half of 2020, which in turn caused a depressed electricity demand. In Great Britain (GB), this low demand combined with large renewable output at times, created conditions that were not expected until renewable capacity increases to meet emissions targets in coming years. The GB system experienced periods of very high instantaneous penetration of non-synchronous renewables, compromising system stability due to the lack of inertia in the grid. In this paper, a detailed analysis of the consequences of the lockdown on the GB electricity system is provided, focusing on the ancillary services procured to guarantee stability. Ancillary-services costs increased by £200m in the months of May to July 2020 compared to the same period in 2019 (a threefold increase), highlighting the importance of ancillary services in low-carbon systems. Furthermore, a frequency-secured scheduling model is used in the present paper to showcase the future trends that GB is expected to experience, as penetration of renewables increases on the road to net-zero emissions by 2050. Several sensitivities are considered, demonstrating that the share of total operating costs represented by ancillary services could reach 35%. • The GB electricity grid experienced an operational challenge due to low inertia. • Cost of ancillary services during lockdown was 3 times higher than in 2019. • This cost could reach up to 35% of total operating costs by 2030. • Fast frequency response shows to be very beneficial in the future system. [ABSTRACT FROM AUTHOR]

Exploitation of shale gas in the UK is at a very early stage, but with the latest estimates suggesting potential resources of 3.8 × 10 13 cubic metres – enough to supply the UK for next 470 years – it is viewed by many as an exciting economic prospect. However, its environmental impacts are currently unknown. This is the focus of this paper which estimates for the first time the life cycle impacts of UK shale gas, assuming its use for electricity generation. Shale gas is compared to fossil-fuel alternatives (conventional gas and coal) and low-carbon options (nuclear, offshore wind and solar photovoltaics). The results suggest that the impacts range widely, depending on the assumptions. For example, the global warming potential (GWP100) of electricity from shale gas ranges from 412 to 1102 g CO 2 -eq./kWh with a central estimate of 462 g. The central estimates suggest that shale gas is comparable or superior to conventional gas and low-carbon technologies for depletion of abiotic resources, eutrophication, and freshwater, marine and human toxicities. Conversely, it has a higher potential for creation of photochemical oxidants (smog) and terrestrial toxicity than any other option considered. For acidification, shale gas is a better option than coal power but an order of magnitude worse than the other options. The impact on ozone layer depletion is within the range found for conventional gas, but nuclear and wind power are better options still. The results of this research highlight the need for tight regulation and further analysis once typical UK values of key parameters for shale gas are established, including its composition, recovery per well, fugitive emissions and disposal of drilling waste. [ABSTRACT FROM AUTHOR]

The change in climate has led to an interest in how this will affect the energy consumption in buildings. Most of the work in the literature relates to offices and homes. However, this paper investigates a supermarket in northern England by means of a multiple regression analysis based on gas and electricity data for 2012. The equations obtained in this analysis use the humidity ratio derived from the dry-bulb temperature and the relative humidity in conjunction with the actual dry-bulb temperature. These equations are used to estimate the consumption for the base year period (1961-1990) and for the predicted climate period 2030-2059. The findings indicate that electricity use will increase by 2.1% whereas gas consumption will drop by about 13% for the central future estimate. The research further suggests that the year 2012 is comparable in temperature to the future climate, but the relative humidity is lower. Further research should include adaptation/mitigation measures and an evaluation of their usefulness. [ABSTRACT FROM AUTHOR]

• Data-driven and comprehensive thermodynamic technology models are developed. • Multi-fidelity approaches are used to compare heat-generation solutions. • Centralised CHP systems appear to be the most profitable option at present. • Heat pumps can achieve a significant degree of heat decarbonisation (55–62%). • Thermodynamic models reduce uncertainty by 75% compared to black-box approaches. This paper presents a multi-scale framework for the design and comparison of centralised and distributed heat generation solutions. An extensive analysis of commercially available products on the UK market is conducted to gather information on the performance and cost of a range of gas-fired combined heat and power (CHP) systems, air-source heat pumps (ASHPs) and ground-source heat pumps (GSHPs). Data-driven models with associated uncertainty bounds are derived from the collected data, which capture cost and performance variations with scale (i.e. , size and rating) and operating conditions. In addition, a comprehensive thermoeconomic (thermodynamic and component-costing) heat pump model, validated against manufacturer data, is developed to capture design-related performance and cost variations, thus reducing technology-related model uncertainties. The novelty of this paper lies in the use of multi-fidelity approaches for the comparison of the economic and environmental potential of important heat-generation solutions: (i) centralised gas-fired CHP systems associated with district heating network; (ii) gas-fired CHP systems or GSHPs providing heat to differentiated energy communities; and (iii) small-scale micro-CHP systems, ASHPs or GSHPs, installed at the household level. The pathways are evaluated for the case of the Isle of Dogs district in London, UK. A centralised CHP system appears as the most profitable option, achieving annual savings of £13 M compared to the use of decentralised boilers and a levelised cost of heat equal to 31 £/MWh th. However, if the carbon intensity of the electrical grid continues to reduce at current rates, CHP systems will only provide minimal carbon savings compared to boilers (<6%), with heat pumps achieving significant heat decarbonisation (55–62%). Differentiating between high- and low-performance and cost heat pump designs shows that the former, although 25% more expensive, have significantly lower annualised costs (£4.9 M vs. £7.7 M) and 12% higher CO 2 -equivalent savings. The uncertainty propagation reveals that data-driven and detailed thermoeconomic models can reduce the uncertainty in the total annualised energy cost and emissions by more than 75% compared to simplified black-box approaches, while the economic comparison of centralised CHP systems and domestic heat pumps for various electricity and gas price scenarios provides guidance to support energy policy. [ABSTRACT FROM AUTHOR]

• A market-based approach to procure reactive power is analysed. • Reactive power support from network assets and DER is considered. • The analysis is done in the context of an innovation project. • Savings are given by the difference between the NVP of BAU costs and alternative solutions. • Actual prices from the live trial are used. This paper quantifies the benefits of introducing reactive power markets that promote the participation of distributed energy resources (DER) in a coordinated way, between the electricity system operator and the electricity distribution utilities. The contribution that DER could make by displacing conventional network assets in supplying reactive power support is evaluated in the context of a case study, the Power Potential (PP) project in Great Britain. We discuss the rising need for absorptive (leading) reactive power in the PP trial area, driven by the rapid connection of renewable generation in an area of low demand growth. A social cost benefit analysis (SCBA) is performed to quantify the net benefits, with sensitivities regarding bid prices, % of DER participation, time horizons. Price information from the PP live trial conducted between January and March 2021 is also used to evaluate the robustness of the SCBA and to estimate benefits using actual prices. Our results suggest that energy consumers could save from 8 to 21% of business as usual asset costs by 2050. The introduction of actual bid prices increases these savings by around 3% of business as usual asset costs out to 2050. Potential sources of additional benefits on top of those identified in the SCBA are also discussed. [ABSTRACT FROM AUTHOR]

Energy policy is too often not designed for energy consumers in a low-cost and consumer-friendly manner. This paper proposes a novel Stackelberg game and Blockchain-based framework that enables consumer-centric decarbonization by automating iterative negotiations between policy makers and consumers or generators to reduce carbon emissions. This iterative negotiation is modeled as a Stackelberg game-theoretic problem, and securely facilitated by Blockchain technologies. The policy maker formulates carbon prices and monetary compensation rates to dynamically incentivize the carbon reduction, whereas consumers and generators schedule their power profiles to minimize bills and maximize profits of generation, respectively. The negotiating agreement is yielded by reaching a Stackelberg equilibrium. The exchanged information and controlling functions are realized by using smart contracts of Blockchain technologies. Case studies of GB power systems show that the proposed framework can incentivize 9% more bill savings for consumers and 45.13% more energy generation from renewable energy sources. As a consumer-centric decarbonization framework, it can at least reduce carbon emissions by 40%. [Display omitted] • A novel consumer-centric and negotiation-based framework to decarbonize power systems. • A Blockchain based platform to integrate the exchanged information and controlling functions of the power system scheduling into data structures, nodes, and smart contracts of Blockchain networks. • How Blockchain based platform can prevent cyber attacks is discussed. • Case studies demonstrate the effectiveness of the proposed framework in the context of Great Britain power systems. [ABSTRACT FROM AUTHOR]

• There is lack of competition in the procurement of reactive power. • Reactive power tends to be compensated by administratively determined pricing methodologies. • More market-based mechanisms are needed (preferably regional or more decentralised). • DER may constitute an important source of reactive power support for the electric power system. • Greater interaction between the DER, DSOs and TSOs will be required. This paper reviews the international experience in the competitive procurement of reactive power and other electricity ancillary services. It involves system operators from different jurisdictions including Australia, the United States and Great Britain. The paper evaluates the different procurement mechanisms, related compensation schemes and looks at more competitive ways for procuring reactive power. It appraises a novel approach (from the Power Potential initiative in the UK) for contracting reactive power services from distributed energy resources using a market-based mechanism. A conceptual auction design applicable to the procurement of reactive power is also discussed. Our findings suggest that competition in reactive power is very limited in comparison with other ancillary services (such as frequency regulation and capacity reserves) and the energy market. The introduction of more market-oriented mechanisms and resources (such as distributed energy resources) for acquiring reactive and active power services by the system operator gives rise to new opportunities and new ways to deal with voltage stability issues. Power Potential brings the opportunity to trial the technical and commercial solution, new roles and new interactions required in the introduction of a competitive reactive power market. [ABSTRACT FROM AUTHOR]

• Severe PV ramping at high penetration could cause voltage instability in presence of Induction motor loads. • Replacement of Induction motor by Variable Speed Drive (VSD) loads enhances voltage stability of distribution network. • Enhanced stability with VSD is studied theoretically with Q-V analysis and with time simulations. • Minimum IM replacement by VSD is determined for realistic distribution network at various PV penetration levels. The Photovoltaic (PV) sources have become the most popular renewable energy resources as it is modular and are deployed quickly. However, the distributed PVs are highly volatile and can have high ramping characteristics where the ramping can go up to 90% of its capacity in about 20 s due to events such as the passing of fast-moving clouds. This kind of PV ramps with high magnitudes can lead to voltage instability in distribution networks that are usually dominated by Induction motor (IM) loads. This paper explains the mechanism of such an instability analytically using Q-V analysis with distribution feeder and IM load characteristics. Highlighting the gradual replacement of IM loads by Variable Speed Drive (VSD) loads due to energy efficiency policies, the paper demonstrates the enhanced voltage stability under VSD loads by both time simulation and analytical approach. Furthermore, the analysis is extended to a realistic distribution network namely, United Kingdom General Distribution System (UKGDS) and extensive case studies are conducted to analyze the voltage stability during PV ramping events at various load compositions of IMs and VSDs. Moreover, the minimum VSD penetration level required to avert the voltage instability at various PV penetration levels have also been determined. [ABSTRACT FROM AUTHOR]

• Emissions performance of road surface transport in UK is investigated up to 2050. • User behaviour of hybrid electric car owners will have a significant impact. • UK transport sector might fail to meet the 4th, 5th, 6th and 7th carbon budget targets. • The authorities might have to start discussing a hybrid car ban in the near future. The United Kingdom aims for an 80% reduction of carbon dioxide emissions by 2050 compared to the levels in 1990. If the transport sector will be able to achieve its share is questionable. This paper aims to analyse how the pace of Battery Electric Vehicles transition and driving behaviour of Plugged in Hybrid Electric Vehicles users can impact the Green House Gas emissions reduction performance of the UK transport sector in meeting the carbon targets by 2050. A reference model for different types of vehicle classes is developed to determine the emissions contributed. The growth of each vehicle class, improvements in efficiency of Electric Vehicles and low carbon fuel targets are projected until the year 2050. Scenario-based models are then developed to analyse how varying the pace of Battery Electric Vehicle transition and hybrid car user driving behaviour would impact the emissions reduction performance. The results of the paper show that the current pace of Battery Electric Vehicle adoption is insufficient for the UK transport sector to achieve the 4th to 7th carbon budgets, due to the slow reduction in Internal Combustion Engine Vehicles. For the 8th and final carbon budgets, emissions may still exceed the carbon budget despite transiting to Electric Vehicles if hybrid car users continue to drive on gasoline modes. The proposed model will serve as a useful reference to assess if the current UK transport strategies and policies suffice and if more actions are necessary to meet the 2050 carbon target. [ABSTRACT FROM AUTHOR]

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT's predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency, cost savings and carbon emission reduction. This paper is motivated by the fact that practical IESs suffer from multiple uncertainty sources, and complicated surrounding environment. To address this problem, a novel DT-based day-ahead scheduling method is proposed. The physical IES is modelled as a multi-vector energy system in its virtual space that interacts with the physical IES to manipulate its operations. A deep neural network is trained to make statistical cost-saving scheduling by learning from both historical forecasting errors and day-ahead forecasts. Case studies of IESs show that the proposed DT-based method is able to reduce the operating cost of IES by 63.5%, comparing to the existing forecast-based scheduling methods. It is also found that both electric vehicles and thermal energy storages play proactive roles in the proposed method, highlighting their importance in future energy system integration and decarbonisation. • A deep learning and digital twins (DT) based scheduling method is proposed. • Physical IES performance is addressed by its virtual replica's predictive capability. • A machine learning method is proposed to address multiple uncertainty challenges. • Historical U.K. datasets are used for performance evaluations. • Results demonstrate the Digital Twin model reduces the extra operating cost by 63.5%. [ABSTRACT FROM AUTHOR]

• Techno-economic analysis of low-carbon heat supply options for UK supermarkets. • RIHC, an ASHP, and a thermal storage tank with advanced control is recommended. • Best investments provide a 16-to-18-year payback without a carbon price. • The UK renewable heat incentive subsidy (for ASHP) generates 89% of the 'savings' • When a £100/tCO2e tax is considered, paybacks are reduced to a 9–11 years. Eliminating the use of natural gas for non-domestic heat supply is an imperative component of net-zero targets. Techno-economic analyses of competing options for low-carbon heat supply are essential for decision makers developing decarbonisation strategies. This paper investigates the impact various heat supply configurations can have in UK supermarkets by using heat recovery principles from refrigeration systems under different climatic conditions. The methodology builds upon a steady-state model that has been validated in previous studies. All refrigeration integrated heating and cooling (RIHC) systems employ CO 2 booster refrigeration to recover heat and provide space heating alongside various technologies such as thermal storage, air-source heat pumps (ASHPs) and direct electric heaters. Seven cases evaluating various technology combinations are analysed and compared against a conventional scenario in which the building is heated with a natural gas boiler. The specific combinations of technologies analysed here contrasts trade-offs and is a first in the literature. The capital costs of these projects are considered, giving insights into their business case. Results indicate that electric heaters are not cost-competitive in supermarkets. Meanwhile, RIHC and ASHP configurations are the most attractive option, and if a thermal storage tank system with advanced controls is included, the benefits increase even further. Best solutions have a 6.3% ROI, a payback time of 16 years while reducing energy demand by 62% and CO 2 emissions by 54%. Such investments will be difficult to justify unless policy steers decision makers through incentives or the business case changes by implementing internal carbon pricing. [ABSTRACT FROM AUTHOR]

• A switchable photovoltaic window based on thermotropic hydrogel is developed. • Selected hydrogel can reversibly switch between highly transmissive and scattering states. • The window performance is evaluated by indoor and outdoor experiments. • This window can passively control daylighting and heat gain during hot sunny days. • This window offers better electrical performance than conventional PV glazing. Daylighting control technologies have become an essential part of sustainable building design to reduce overheating, glare and energy consumption in buildings. In this paper, a smart glazing system where a Building Integrated Photovoltaic (BIPV) glazing is coupled with an optically switchable thermotropic hydrogel layer is proposed to improve the daylighting control and electricity generation performance of traditional BIPV glazings. Thermotropic (TT) hydrogels made of various weight percentage combinations of hydroxypropyl cellulose (HPC) polymer, gellan gum and sodium chloride (NaCl) salt were synthesised and first evaluated by visible-near-infrared spectroscopy. Subsequently, small-scale prototypes of the proposed BIPV thermotropic (BIPV-TT) laminated glazing based on these TT hydrogels were fabricated and characterised experimentally under controlled laboratory conditions. The TT hydrogel, which was synthesised of 6 wt% HPC, 0.5 wt% gellan gum and 4.5 wt% NaCl, was selected for further experimental characterisations in a dynamic outdoor environment, due to its appropriate transition temperature of 30.7 °C for use in mild climates with a wide modulation range of solar transmittance from 85.8% (in the transparent state) to 9.6% (in the light-scattering state). The outdoor tests were conducted in Nottingham, the UK, on typical summer days with sunny and partial cloudy conditions. The results showed that using the prototype BIPV-TT laminated glazing can reduce up to 80% of the solar radiation transmitted into the outdoor test cell, while providing up to 12% higher electrical power outputs, compared to its counterpart system with no thermotropic hydrogel applied. [ABSTRACT FROM AUTHOR]

• Temporal characterization of marine energy resources, at varying time scales. • Characterized wave, tidal, and ocean currents, and compared to wind and solar. • Data based exposition of grid benefits for wave power at high renewable scenarios. • Data based exposition of higher capacity adequacy and stability for wave power. • Detailed discussion on market implications of grid integration of marine energy. In this paper, the applicability of marine renewable energy (MRE) for potential grid applications is presented. We show that many of the unique value streams from marine-based electricity generation resources stem from their inherent temporal characteristics, especially when compared to wind and solar. Specifically, in this work, we evaluate the timing value for three types of MRE resources: (a) tidal, (b) wave, and (c) ocean currents. First, through a suite of novel metrics, such as resource availability, persistence, and versatility, we evaluate the temporal value characteristics of these resources. Second, through a more grid-oriented numerical study, we comment on the potential ramifications of those temporal characteristics in context of energy balancing and effective load carrying capability for one marine-based resource i.e., wave. Finally, we further our understanding of the relative advantages that may be leveraged by operating wave-based generation in tandem with more established renewable resources, such as wind and solar. Our results indicate that compared to wind and solar, MRE resources are consistently more available and persistent on an hourly level throughout an entire year of operation. In addition, wave resources are also seen to reduce the balancing requirements within the power system. Our work focuses on sites specific to the United States (US) and a parallel study for a location in Great Britain (GB). Results are found to be consistent for sites in both the US and GB, implying that the grid benefits discussed in this work could apply to a number of locations globally. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to determine the effect of different carbon tax rates on the power generation composition of Britain. This was accomplished via a regional, geospatial model, accounting for regional loads, transmission losses and generators of Britain's current energy infrastructure. This regional model is also compared to a pure dispatch, nationally aggregated model which considers only costs on the generator side inclusive of the carbon tax, thus allowing the effect of including geospatial conditions to be identified. The effect of this tax (in both the geospatial and nationally aggregated cases) is a transition from coal to combined cycle gas turbine (CCGT) generated power to fulfil demand unmet by nuclear or renewable sources. The more sophisticated regional model, however, differs from the nationally aggregated case by having a significantly larger window of carbon tax rates over which this coal to CCGT transition occurs. Due regional differences in demand and installed capacity technology types it is determined that more than 50% of this transition occurs prior to CCGT becoming more economical than coal from a pure dispatch (nationally aggregated) perspective. Primarily due to CCGT generators typically being closer to larger southern loads than northern coal, transmission losses and the economic disincentive of a carbon tax combine in encouraging this transition. The transition window, therefore, is not only broadened by the consideration of geospatial effects, but furthermore, this broadening significantly and disproportionately occurs by decreasing the lower bound of this transition window. These findings validate the significance of utilising a geospatial model, particularly of regional resolution. They further identify the deployment of current energy infrastructure in Britain under differing carbon tax regimes and by extension, the transition window (found to be from coal to CCGT) an increasing carbon tax rate would create. These results bear not only significance in understanding the UK's currently incrementing (top-up) carbon tax rate, but also shed light on future policies due to the UK's leaving of the EU's Emissions Trading Scheme (ETS), with immediate plans to continue with a domestic carbon tax and trading scheme. Thus, these results hold importance in the understanding the effect of carbon taxation on existing infrastructure, energy modelling and national policy in the UK. [Display omitted] • OPF modelling performed at national and regional levels. • Effect of different carbon tax rates on generator composition determined. • Transmission losses accelerate coal to gas transition due to generator placement. • Differences in regional generator compositions and loads found to be significant. [ABSTRACT FROM AUTHOR]

The system states of battery energy storage systems (BESSs) such as state of charge (SOC) and state of health (SOH) are essential for the functions of the system, such as frequency support services and energy trading. However, the complexity of a large-scale battery system makes the estimations more difficult than at the cell-level. This is further compounded by real-world limitations on system monitoring data granularity, accuracy and quality. In this paper it is shown how cell-level state estimation techniques can be utilised on large-scale BESSs using experimental data from a 2MW, 1MWh BESS. The results show how a Dual Sigma point Kalman Filter (DSPKF) SOC estimation provides more accurate results compared to the commercial BESS battery management system SOC. It is shown how the DSPKF parameters can be tuned by a genetic algorithm to simplify selection and generalise the approach for different BESSs. Furthermore, it shows how this method of SOC estimation can be combined with a total least-squares (TLS) method for capacity estimation to less than 1% error. Online system state estimation is demonstrated using both designed tests and real-world operational profiles where the BESS has provided contracted frequency response services to the national electricity grid in the UK. • Successful utilisation of a cell-level model for large-scale BESS state estimation. • Multi-objective GA tuned multi-scale DSPKF is demonstrated for BESS SOC estimation. • TLS algorithm implemented for accurate capacity estimation of large-scale BESS. • DSPKF estimated SOC is proposed as an input for TLS algorithm for improved accuracy. [ABSTRACT FROM AUTHOR]

Compressor stations play a crucial role in natural gas networks to maintain required pressure levels for transporting gas. Centrifugal compressors commonly used in high pressure gas transmission networks could be driven by gas turbines or electric motors. Including compressor units powered via different fuels in a compressor station allows switching between fuels required by compressors to achieve a set outlet pressure and flow throughput. In this paper, an optimisation model of gas network was developed considering reasonably detailed representation of a compressor station to investigate flexibility provision from the compressor station to the power system. The model was formulated as a mixed integer linear programming (MILP) problem by linearising the nonlinear equations governing gas flow along pipes and compressor power consumption. The model was tested on the gas transmission system in South Wales, UK. The operation of compressors was optimised in response to gas and electricity price subject to meeting operational limits of the gas network. The results showed that the compressors can provide flexibility to the power system through shifting their electricity energy consumption in time or switching between gas- and electric-driven compressor units. It was found that the allowable range for variation of linepack plays a key role in the magnitude and duration of flexibility provision from compressor units. • An optimisation model was proposed for operation of compressors in the gas networks. • Flexibility from coordinated operation of gas- and electric-driven compressor units were quantified. • The value of linepack in providing flexibility to power systems was investigated. [ABSTRACT FROM AUTHOR]

Given the increasing penetration of distributed energy resources (DER) in low-voltage networks, peer-to-peer (P2P) energy trading has been proposed to facilitate DER integration into power systems. In this paper, we investigate P2P energy trading driven by the electrical distance between agents in a low-voltage network. Our market design considers the role of a distributed system operator (DSO) who determines the shortest path between agents, encoded in preference lists. The DSO submits the preference lists to the agents but does not intervene in the P2P matching process. Specifically, we propose two decentralised P2P market mechanisms that are induced by the relations in the preference lists. First, we introduce a stable-matching (SM) algorithm for matching agents and guaranteeing that no agents want to mutually deviate from the matching. Second, a mechanism based on a continuous double auction (CDA) is adopted for continually matching peers. The proposed mechanisms are tested on a UK low-voltage network. Simulation results show that the outcome of the proposed SM mechanism faces a trade-off between the stability of the matching and market efficiency, whereas the proposed CDA-based mechanism deviates from the stable outcome but can still capture the high market efficiency of the CDA. We show that a P2P market driven by electrical distance leads to reduced losses and line congestion in both mechanisms. • We introduced a novel decentralised electrical-distance driven P2P market design. • We proposed a stable-matching and a CDA-based mechanisms for matching peers. • The shortest paths between agents are encoded in preference lists by a DSO. • Technical implications of adopting the proposed mechanisms are assessed. • Inciting P2P trading between closer agents reduces power losses and congestion. [ABSTRACT FROM AUTHOR]

• Assessed CO 2 shipping as a global decarbonisation strategy for future developments. • Identified technical and safety challenges encountered during in CO 2 shipping chain. • Evaluated the environmental impact of shipping emissions in the transport chain. • Summarised emission control technologies as a key enabler in the CO 2 shipping chain. Carbon Capture, Utilisation and Storage (CCUS) can reduce greenhouse gas emissions for a range of technologies which capture CO 2 from a variety of sources and transport it to permanent storage locations such as depleted oil fields or saline aquifers or supply it for use. CO 2 transport is the intermediate step in the CCUS chain and can use pipeline systems or sea carriers depending on the geographical location and the size of the emitter. In this paper, CO 2 shipping is critically reviewed in order to explore its techno-economic feasibility in comparison to other transportation options. This review provides an overview of CO 2 shipping for CCUS and scrutinises its potential role for global CO 2 transport. It also provides insights into the technological advances in marine carrier CO 2 transportation for CCUS, including preparation for shipping, and in addition investigates existing experience and discusses relevant transport properties and optimum conditions. Thus far, liquefied CO 2 transportation by ship has been mainly used in the food and brewery industries for capacities varying between 800 m3 and 1000 m3. However, CCUS requires much greater capacities and only limited work is available on the large-scale transportation needs for the marine environment. Despite most literature suggesting conditions near the triple-point, in-depth analysis shows optimal transport conditions to be case sensitive and related to project variables. Ship-based transport of CO 2 is a better option to decarbonise dislocated emitters over long distances and for relatively smaller quantities in comparison to offshore pipeline, as pipelines require a continuous flow of compressed gas and have a high cost-dependency on distance. Finally, this work explores the potential environmental footprint of marine chains, with particular reference to the energy implications and emissions from ships and their management. A careful scrutiny of potential future developments highlights the fact, that despite some existing challenges, implementation of CO 2 shipping is crucial to support CCUS both in the UK and worldwide. [ABSTRACT FROM AUTHOR]