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As regulatory constraints limit energy communities' (ECs) participation in wholesale markets, these might rely on retailers' supply when local generation falls short. As balancing responsible parties, retailers are financially responsible for matching the volumes traded in the market with customers' actual needs. However, inadequate information about ECs' operations may complicate this task. This paper explores the interactions and financial implications for retailers under contractual agreements with ECs. We design a novel modelling framework, consisting of: (1) a stochastic model of a strategic retailer participating in the day ahead market considering imbalance costs, (2) a community model optimising its operations based on the agreed tariff subscription with the retailer, and (3) a simulation of the imbalance settlement process. The frameworks' applicability is demonstrated via a case study in London (UK). Results indicate that retailers' primary source of profit loss arises from the increased self-sufficiency of customers belonging to EC. On the other hand, deviations from the market commitments exerts limited effects on retailers financial outcomes. This is explained by the earnings obtained by providing passive balancing services to system operators. Also, the paper underscores retailers' need to reassess their business models, looking beyond merely establishing operational data exchange with ECs. • A unique modelling framework representing retailers and Energy Communities (ECs) interactions. • EC increase in self-sufficiency is the main driver for retailers' loss of profits. • The single imbalance price mechanism reduces retailers' loss of profits. • Retailers' information on EC operations does not lead to substantial cost savings. • Retailers might rethink their business strategies with ECs to keep their profit level. [ABSTRACT FROM AUTHOR]

The heating sector is a major contributor to greenhouse gas emissions in most countries, and in the UK in particular it accounts for around 20% of carbon emissions. Therefore, various scenarios have been proposed to decarbonise heating. However, studies in this direction do not typically consider the tight interactions with the electricity and gas sector in a multi-energy system context, nor the required geographical and time resolution. To bridge this gap, a novel high-resolution spatial and temporal integrated electricity-heat-gas model has been developed to assess the impact of low-carbon heating options on electricity and gas transmission networks with the consideration of heat demands requirements. The presentation of this modelling is provided in a two-part paper. By using building simulation software and statistical information, Part I, presented here, develops a model for the seasonal and intraday heat demand at half-hourly intervals for 404 areas across the whole of Great Britain (GB). Modelling of different heating technologies has then been used to map the equivalent impact on electricity and gas regional supplies. Model validation is performed by comparing results against historical gas consumption data and shows that fuel consumption can be evaluated to within a 3% accuracy. The numerical studies highlight the importance of high-resolution modelling to capture peak and asset requirements. In particular, the results show how the half-hourly peak heat demand can be around 200% greater than the mean daily heat demand. In the companion paper, Part II, the developed heat demand model is then applied as an input into a novel integrated heat, gas and electrical transmission network model that addresses questions concerning potential future infrastructure impacts and requirements upon changes to the heating sector. [ABSTRACT FROM AUTHOR]

Abstract Energy efficiency, which is one of the pillars of the EU's Energy Union strategy, has been proposed as a solution, namely as a highly effective pathway to improve economic competitiveness and sustainability of the European economy, lower emissions, reduce energy dependency and increase security of supply, and job creation. The paper reviews the EU strategies and policies on energy efficiency and argues that further focus should be placed on industrial energy efficiency. Despite a decline in energy consumption in recent years in industry, this sector is one of the largest users of energy in the EU. Therefore, the paper reviews the extent to which the European and national policies in the selected jurisdictions, such as Italy and the UK address energy efficiency in industry and whether there are any measures in place to promote it. Highlights • Legal certainty is required in industrial energy efficiency policies. • White Certificates play a major role in industrial energy efficiency in Italy. • Yet, constant changes in the WC regulations can act as an investment barrier. • The UK's Energy Efficiency policy is underpinned with decarbonisation of the industry. [ABSTRACT FROM AUTHOR]

Bottom-up energy system models rely on cost optimization to produce energy scenarios that inform policy analyses, debates and decisions. This paper reviews the rationale for the use of cost optimization and questions whether cost-optimal scenarios are adequate proxies of the real-world energy transition. Evidence from ex-post modeling shows that cost optimization does not approximate the real-world UK electricity system transition in 1990–2014. The deviation in cumulative total system costs from the cost-optimal scenario in 1990–2014 is equal to 9–23% under various technology, cost, demand, and discount rate assumptions. In fact, cost-optimal scenarios are shown to gloss over a large share of uncertainty that arises due to deviations from cost optimality. Exploration of large numbers of near-optimal scenarios under parametric uncertainty can give indication of the bounds or envelope of predictability of the real-world transition. Concrete suggestions are then made how to improve bottom-up energy system models to better deal with the vast uncertainty around the future energy transition. The paper closes with a reflective discussion on the tension between predictive and exploratory use of energy system models. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of incorporating spatiotemporal wind data on the performance of wind forecasting neural networks. While previous studies have shown that including spatial data enhances the accuracy of such models, limited research has explored the impact of different spatial and temporal scales of input wind data on the learnability of neural network models. In this study, convolutional neural networks (CNNs) are employed and trained using various scales of spatiotemporal wind data. The research demonstrates that using spatiotemporally correlated data from the surrounding area and past time steps for training a CNN favorably affects the predictive performance of the model. The study proposes correlation analyses, including autocorrelation and Pearson correlation analyses, to unveil the influence of spatiotemporal wind characteristics on the predictive performance of different CNN models. The spatiotemporal correlations and performances of CNN models are investigated in three regions: Korea, the USA, and the UK. The findings reveal that regions with smaller deviations of autocorrelation coefficients (ACC) are more favorable for CNNs to learn the regional and seasonal wind characteristics. Specifically, the regions of Korea, the USA, and the UK exhibit maximum standard deviations of ACCs of 0.100, 0.043, and 0.023, respectively. The CNNs wind prediction performances follow the reverse order of the regions: UK, USA, and Korea. This highlights the significant impact of regional and seasonal wind conditions on the performance of the prediction models. [Display omitted] • 3D CNN-based wind velocity prediction. • Estimation of CNN learnability through spatiotemporal wind correlation analysis. • Discovery of influence of local geometric and seasonal wind on CNN prediction. • The proposed correlation analysis can aid in selecting yaw-control wind farm sites. [ABSTRACT FROM AUTHOR]

Departing from the prior literature on the energy consumption-economic growth linkages, this paper aims to quantify the asymmetric impact of unpredictable variations in energy use on GDP (gross domestic product) volatility across the high- and low-volatility regimes in the United Kingdom. For this purpose, the paper utilizes quarterly and monthly data on energy consumption and real GDP, covering the period from 1960 to 2013. To obtain the regime-dependent conditional volatility series, we estimate the two-state Markov regime switching ARCH model. We find significant evidence of the presence of regime shifting in behaviour of both underlying volatility series. By estimating the Markov regime switching model, we find that not only the current (contemporaneous) but also the past (intertemporal) volatility shocks in energy utilization are significant in explaining the output dynamics. Yet, we show that the impact of energy consumption volatility is quite asymmetric, depending on the intensiveness of volatility. Specifically, we show that although the effect of energy consumption volatility on GDP volatility is statistically insignificant in the low-volatility regime, in the high-volatility regime, it appears highly significant. From a policy perspective, our findings suggest that controlling unwanted variations in energy usage would be useful in stabilizing real sector of the economy. [ABSTRACT FROM AUTHOR]

With the increasing interactions between the heat, electricity and gas sectors due to the introduction of low carbon heating technologies, there is a need for models to assess the inter-sector interactions at a network level. This paper presents a novel integrated electricity-heat-gas transmission network model that considers electrical and gas network flows coupled with the fuel requirements for the heating sector. The latter is modelled at a nodal network level and on a half-hourly basis, building upon the high-resolution temporal and spatial heat demand model presented in Part I. In particular, here the modelling is developed further to include an integrated heat-electricity-gas optimization to assess the operation of different heating technologies, and particularly hybrid ones. More specifically, DC power flow modelling is coupled with steady-state gas network energy and transportation cost optimization to assess gas-electricity price interactions, and with transient gas flow modelling for gas network operational studies. Numerical case studies are performed on the GB energy system, considering the evolution towards a low carbon future, the operation of hybrid dual-fuel electric heat pump/gas boiler technologies, and the ability to alleviate gas network constraints. Resilience case studies considering nodal gas price implications under gas network supply shocks are also considered. The results show how pathways to electrify heating and decarbonise the power sector can lead to a 75% reduction in carbon emissions of the heat and electricity sectors and that using hybrid heating technologies can reduce conventional generation peaks by 24%. Additionally, it is shown that exploiting gas demand response can provide an additional resilience option to the gas network. [ABSTRACT FROM AUTHOR]

Although district heat network schemes provide a pragmatic solution for reducing the environmental impact of urban energy systems, there are additional benefits that could arise from servicing electric vehicles. Using the electricity generated on-site to power electric vehicles can make district heating networks more economically feasible, while also increasing environmental benefits. This paper explores the potential integration of electric vehicle charging into large-scale district heating networks with the aim of increasing the value of the generated electricity and thereby improving the financial feasibility of such systems. A modelling approach is presented composed of a diverse range of distributed technologies that considers residential and commercial electric vehicle charging demands via agent-based modelling. An existing district heating network system in London was taken as a case study. The energy system was modelled as a mixed integer linear program and optimised for either profit maximisation or carbon dioxide emissions minimisation. Commercial electric vehicles provided the best alternative to increase revenue streams by about 11% against the current system configuration with emissions effectively unchanged. The research indicates that district heating network systems need to carefully analyse opportunities for transport electrification in order to improve the integration, and sustainability, of urban energy systems. • Framing of a modern district energy system as a mixed integer linear program. • Agent-based modelling used to simulate electric vehicle charging demands. • Electric vehicle charging in a district heating network simulated for a London area. • District heating integration with electric vehicles improves revenues by 11%. [ABSTRACT FROM AUTHOR]

Abstract Energy-related investment decisions in the buildings sector are heterogeneous in that the outcome for each individual varies according to budget, values, and perception of a technology, even if an apparently identical decision task is faced. In particular, the rate of adoption of new energy-efficient technologies is often hard to model and underlines the need for an advanced approach to capture diversity in decision-making, and enable the inclusion of economic, comfort, environmental and social aspects. This paper presents an enhanced agent-based model that captures several characteristics of consumer behaviour that influence investment decisions. Multiple agents with different objectives, search strategies, and decision methods are implemented. A case study is presented which illustrates the benefits of the approach for the residential sector in the UK. The agent-based method shows diversity in investment decisions, without requiring the constraints on uptake needed in many models. This leads to a range of technologies in the market during a transition phase, continuous investment in low capital cost technologies, and eventually the emergence of a low carbon system based on new mass market technologies. The system that emerges is vastly different from one observed when economically rational investment is assumed and uptake constraints are applied. Highlights • An enhanced building sector model using an agent-based method. • Represent real-world decision making for investment in energy technologies. • A method to model the limits, barriers and speed of consumer technology adoption. • Modelling framework adaptable to different geographical regions. • A case study is investigated for the residential sector in the United Kingdom. [ABSTRACT FROM AUTHOR]

Abstract This paper analyses the consequences and effects of volatile energy prices in the UK. The evidence provided are from an estimated DSGE model of energy. The model is applied on filtered data from 1981:Q1 to 2013:Q1 and evaluated by the indirect inference testing. In analysing the structural shocks, the study found higher volatility in energy prices shock during the Great Recession compared to the sample period. The high volatility of energy prices shock caused inflationary pressures in the economy. The study found energy prices shock amplified the Great Recession by significantly contributing to the fall in output. Thus, energy prices shock is an important driver of economic activity. However, given the shocks are stationary, energy prices shock is temporary. Therefore, all consequences of energy prices in the economy are short-term. By implication, when volatile energy prices create an output shortfall, monetary policy is the tool used to off-set short-term falls in output. We find results persists with robustness check. The findings justified why the DSGE model is a policymakers' workhorse. Highlights • Analysed the effects of volatile energy prices in the UK in an estimated DSGE model. • The model implies energy prices shock significantly drives economic activity. • Found volatile energy prices shock contributed to the Great Recession in the UK. • Monetary authorities were able to reduce the consequences of world energy prices. • Results persists with robustness check of VAR-IRF and variance decomposition. [ABSTRACT FROM AUTHOR]

Abstract The present paper explored the potential of the seasonal solar thermal energy storage (SSTES) system using ammonia-based chemisorption for domestic application in the UK. The dynamic charging/discharging performance of the SSTES has been simulated using the real weather data with the solar thermal collector models, the domestic heating demand model and the chemisorption model. The selection of working salts has significantly influence on the system design and dynamic performance. The CaCl 2 -4/8NH 3 chemisorption can satisfy almost 100% of space heating demand when using low temperature hating facility during discharging stage, however, due to its relatively higher desorption temperature and limited sunlight available in the Newcastle-upon-Tyne the required solar collectors area exceeds the commonly available space of dwelling roof. The NaBr-0/5.25NH 3 chemisorption is only able to contribute 18.6% of heating demand because the temperature of the discharged heat cannot reach the required level for most of the time in the heating season. The best scenario studied was using BaCl 2 -0/8NH 3 chemisorption SSTES (45.2 m3 storage volume) combined with low temperature heating facilities and a 30.5 m2 solar collector, which can cover about 57.4% of space heating for a dwelling with a heat loss coefficient at 150 W/K. Highlights • Feasibility of domestic seasonal solar thermal storage using NH 3 chemisorption. • Integrate simulation of solar heat, domestic heating demand and NH 3 chemisorption. • Solar collector area, storage volume size and storage ratio using different salts. • BaCl 2 -0/8NH 3 was identified as the preferable working pair. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the integration of renewable electricity into the UK system in 2020. The purpose is to find the optimal wind generation that can be integrated based on total cost of supply. Using EnergyPLAN model and the Department of Energy and Climate Change (DECC) energy projections as inputs, this paper simulates the total cost of electricity supply with various levels of wind generation considering two systems: a reference and an alternative system. The results show that 80 TWh of wind electricity is most preferable in both systems, saving up to 0.9% of total cost when compared to a conventional system without wind electricity production. The alternative system, with decentralized generation and active demand management, brings relatively more cost saving, and higher wind utilisation, compared to the reference case. The sensitivity analysis with alternative fuel and capital costs again confirms the superiority of the alternative over the reference system. [Copyright &y& Elsevier]

Abstract: Building energy use accounts for almost 50% of the total CO2 emissions in the UK. Most of the research has focused on reducing the operational impact of buildings, however in recent years many studies have indicated the significance of embodied energy in different building types. This paper primarily focuses on illustrating the relative importance of operational and embodied energy in a flexible use light distribution warehouse. The building is chosen for the study as it is relatively easy to model and represents many distribution centres and industrial warehouses in Europe. A carbon footprinting study was carried out by conducting an inventory of the major installed materials with potentially significant carbon impact and material substitutions covering the building structure. Ecotect computer simulation program was used to determine the energy consumption for the 25 years design life of the building. This paper evaluates alternative design strategies for the envelope of the building and their effects on the whole life emissions by investigating both embodied and operational implications of changing the envelope characteristics. The results provide an insight to quantify the total amount of CO2 emissions saved through design optimisation by modeling embodied and operational energy. [Copyright &y& Elsevier]

Large-scale integration of battery energy storage systems (BESS) in distribution networks has the potential to enhance the utilization of photovoltaic (PV) power generation and mitigate the negative effects caused by electric vehicles (EV) fast charging behavior. This paper presents a novel deep reinforcement learning-based power scheduling strategy for BESS which is installed in an active distribution network. The network includes fast EV charging demand, PV power generation, and electricity arbitrage from main grid. The aim is to maximize the profit of BESS operator whilst maintaining voltage limits. The novel strategy adopts a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and requires forecasted PV power generation and EV smart charging demand. The proposed strategy is compared with Deep Deterministic Policy Gradient (DDPG), Particle Swarm Optimization and Simulated Annealing algorithms to verify its effectiveness. Case studies are conducted with smart EV charging dataset from Project Shift (UK Power Networks Innovation) and the UK photovoltaic dataset. The Internal Rate of Return results with TD3 and DDPG algorithms are 9.46% and 8.69%, respectively, which show that the proposed strategy can enhance power scheduling and outperforms the mainstream methods in terms of reduced levelized cost of storage and increased net present value. • Proposed a deep reinforcement learning-based power scheduling strategy for BESS operator profit maximization. • Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm delivers the optimal power scheduling. • Case study based on Smart EV charging dataset from UK Power Networks, Project Shift. • IRR with TD3 and DDPG algorithms can achieve up to 9.46% and 8.69%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a realistic model for energy and carbon management of an advanced municipal wastewater treatment works. Through minimisation of total cost of operations, it provides operators with a visual daily operational schedule based on varying tariffs. This site is the first in the UK with a mixed operational strategy for biomethane produced on site: to burn in CHP (Combined Heat and Power) engines to create electricity, burn in Steam Boilers for onsite steam use or inject the biomethane into the gas distribution network - Natural Gas can be imported to make up shortfalls in biomethane if required. Implemented using a novel mixed integer linear programming (MILP) approach, results indicate that biomethane injection should be maximised for the highest financial gain - the driving force for optimising the remaining operations being the site electricity imports and whether the electricity imported 'generates' carbon emissions. Based on the source of electricity and the new carbon emissions performance criteria, under the current operational strategy importing electricity from carbon-based sources has no tangible impact on site revenues (but does impact CO 2 emissions), however carbon free (renewable) electricity sources could see shift in operations leading a revenue increase of 12% [Display omitted] • Optimisation of biogas and energy distribution and carbon emissions monitoring using MILP. • Changes to current operations are needed to meet emissions pledges. • Changes to operational strategy could also see revenues increase by up to 12%. • Biomethane Injection primary driver of revenues. [ABSTRACT FROM AUTHOR]

The decarbonisation of heat supply will play a critical role in meeting the emissions reduction target. There is, however, great uncertainty associated with the achievable levels of heat decarbonisation and the optimal heat technology mix, which can have serious implications for the future electricity and gas demand. This work employs an integrated gas, electricity and heat supply model to quantify the impacts of heat decarbonisation pathways on the future electricity and gas demand. A case study in the Great Britain is performed considering two heat decarbonisation scenarios in 2050: one is the predominantly electrified heat supply and the other is the predominantly hydrogen-based heat supply. The electricity demand becomes more volatile in the electrified heat scenario as the peak surges to 107.3 GW compared to 51.1 GW in the 2018 reference scenario, while the peak in hydrogen-based heat scenario is 78.4 GW. The peak gas demand declines from 247.6 GW for 2018 to 81.7 GW for electrified heat scenario and to 85.1 GW for hydrogen-based heat scenario, confirming that the seasonality associated with heat demand is shifting away from the gas network and towards electricity network. Moreover, a sensitivity analysis shows that the future electricity demand is highly sensitive to parameters such as relative heat demand, coefficient of performance of air source heat pumps and share of electricity in hydrogen production. Finally, the application of a load shifting strategy demonstrates that demand-side flexibility has the potential to maintain the electricity system balance and minimise the generation and network infrastructure requirements arising from heat electrification. While the case study presented in this paper is based on the Great Britain, the findings regarding the future electricity and gas demand are relevant for the global energy transition. • Integrated operation of gas, electricity and heat supply systems is modelled. • Impacts of heat decarbonisation on GB electricity and gas demand are quantified. • Sensitivity of future electricity demand to input uncertainties is investigated. • Role of flexibility in maintaining the electricity balance is demonstrated. [ABSTRACT FROM AUTHOR]

Since 2008, the U.K. natural gas market has witnessed a marked drop in volatility. This fall has coincided with specific events in oil and gas sector such as the onset of the U.S. “shale gas revolution” and the subsequent rerouting of liquefied natural gas (LNG) shipments from the U.S. to other markets such as Asia and Europe. LNG cargoes, along with other sources of flexibility such as underground storages and interconnector import, can potentially reduce volatility. On the other hand, demand shocks can increase volatility. To examine the dynamics relationship between daily shocks in U.K. gas demand and supply, and the gas spot price volatility, we use a vector autoregressive (VAR) model. While we find evidence that daily deviations in aggregated gas demand significantly impacts volatility, we are unable to find direct evidence for an impact from shocks in disaggregated demand or supply. In fact, one important contribution of the paper is to suggest that flexible sources of supply such as LNG, storage and interconnector flows react to shocks in retail demand, dampening their potential effects on volatility. [ABSTRACT FROM AUTHOR]

In this paper we develop fundamental quantile regression models for the UK electricity price in each trading period. Intraday properties of price risk, as represented by the predictive distribution rather than expected values, have previously not been fully analyzed. The sample covers half hourly data from 2005 to 2012. From our analysis we are able to show how the sensitivity towards different fundamental factors changes across quantiles and time of day. In the UK the supply of electricity is to a large extent generated from coal and gas plants, thus the price of gas and coal, as well as the carbon emission price, are included as fundamental factors in our model. We also include the electricity price lagged by one day, as well as demand and margin forecasts. We find that the sensitivities vary across the price distribution. Our findings also suggest that the sensitivity to fundamental factors exhibit intraday variation. We find that the sensitivity to gas relative to coal is higher in high quantiles and lower in low quantiles, as well as some indications of market power being exercised during peak hours. We have demonstrated a scenario analysis based on the quantile regression models, showing how changes in the values of the fundamentals influence the electricity price distribution. [ABSTRACT FROM AUTHOR]

With the popularization of electric vehicles, vehicle-to-grid (V2G) has become an indispensable technology to improve grid economy and reliability. However, battery aging should be mitigated while providing V2G services so as to protect customer benefits and mobilize their positivity. Conventional battery anti-aging V2G scheduling methods mainly offline operates and can hardly be deployed online in hardware equipment. This paper proposes a novel online battery anti-aging V2G scheduling method based on a novel two-stage parameter calibration framework. In the first stage, the V2G scheduling is modeled as an optimization problem, where the objective is to reduce grid peak-valley difference and mitigate battery aging. The online deployment of the developed optimization-based V2G scheduling is realized by a rule-based V2G coordinator in the second stage, and a novel parameter calibration method is developed to adjust controller hyper-parameters. With the parameter calibration process, the global optimality and real-time performance of V2G strategies can be simultaneously realized. The effectiveness of the proposed methodologies is verified on a practical UK distribution network. Simulation results indicate that it can effectively mitigate battery aging in providing V2G services while guaranteeing algorithm real-time performance. • Battery-protective V2G scheduling method with online deployment capability. • Grid peak-shaving and battery anti-aging are considered in an optimization model. • A rule-based coordinator is built to deploy the derived optimal strategies online. • A practical UK distribution network is employed to verify the performances. • 37.8% battery life loss can be reduced while providing auxiliary power in real-time. [ABSTRACT FROM AUTHOR]

The decarbonisation of heat requires a transition from gas boilers to low-carbon heating systems such as heat pumps. Efficiency gains can be achieved by linking heating systems through ambient loops called Fifth Generation District Heating and Cooling (5GDHC) networks. The UK needs working demonstrators to understand both the technical and practical challenges in the heat transition. The Balanced Energy Network (BEN) links two buildings on LSBU's campus and is the UK's first 5GDHC system at scale and among the first in the world to be retrofit in parallel to an incumbent gas system and include an active demand side response control system to toggle between energy vectors in way that minimises cost and carbon emissions. This paper presents performance data from its first year of operation in baseline mode, as it was commissioned and optimised. High temperature heat pumps were retrofit to an existing gas boiler circuit and match the 79 °C output temperature of the gas boiler system. No fabric upgrades were required and no pipes, ducts or heat emitters were resized, however the system maintained performance to reduce overall building carbon emissions by 13% and gas use by 40% across both buildings compared to the pervious heating season while the system was in use. • First known UK 5th Gen ambient heat network retrofit to existing buildings. • Demonstration of high temperature heat pumps (79 °C) maintaining a high COP. • Performance data from heat pumps retrofit into existing gas heating systems. • Demonstration of a phased transition from gas to heat pumps in complex buildings. • Up to 40% reduction in use CO2 emissions with opportunities to optimise over time. [ABSTRACT FROM AUTHOR]

Solar irradiation data is required in many applications to obtain the solar energy output. However, solar irradiation data is not always available, especially horizontal surface diffuse solar irradiation. This has led to the creation of empirical solar models to predict these values. The aim of this paper is to develop an empirical model for the estimation of hourly diffuse solar irradiance on horizontal surfaces. Initially, a set of existing empirical models have been evaluated to test their accuracy. New models have been developed based on ground level measurements of global and diffuse horizontal solar irradiation that have been obtained from three different regions of the United Kingdom: South Yorkshire, Norfolk, and West Sussex for a period between 1982 and 1999. The models created for each region correlate the diffuse fraction (kd) and the clearness index (kt), for the estimation of hourly diffuse solar irradiance on horizontal surfaces. The models developed for the three regions were evaluated to test their accuracy using error histograms and by contrasting different datasets. Finally, a comparison of the new models to the existing empirical models showed that the new developed correlations significantly improved the existing empirical models for the three regions evaluated in this study. • New models were developed to estimate horizontal diffuse solar irradiance (Gd). • The new empirical correlations were developed for three different regions in the UK. • The accuracy of existing empirical models to estimate Gd in the UK was evaluated. • The existing and new empirical models were compared using statistical analysis. • The new correlations significantly improved on existing models in the areas studied. [ABSTRACT FROM AUTHOR]

This paper presents the first financial analysis of the United Kingdom's local energy business sector. This analysis relies on financial ratios and degrees of localism as inputs for descriptive statistics, cluster, and canonical discriminant analyses. Our findings suggest that privately-owned energy businesses, typically with limited commitments to localities, account for the great majority of sectoral assets and turnover, and are in comparatively good financial condition. Highly-local energy businesses typically have low profitability and high reliance on debt. The latter is the key variable differentiating them from other less local energy businesses. Moreover, we find financial commonalities within different groups of local energy businesses, which correlate with their specific level of localism. In the context of increasing digitalisation in energy markets, more technological innovation may help strengthen local energy businesses' revenue sources and value creation. Further research is needed in terms of investability, specific financing terms and conditions, and geographical aspects of value creation, retention, and delivery to localities. This work can improve the understanding of sectoral dynamics and development needs, with value for policy making to incentivise investment in this emerging sector. • An analysis of the United Kingdom's local energy business sector is presented. • The analysis relies on financial ratios and estimates for degrees of localism. • Highly-local businesses typically have low profitability and high reliance on debt. • Businesses with limited local commitment usually have a good financial condition. • Research on investability, financing, and locational value creation are needed. [ABSTRACT FROM AUTHOR]

For decades the focus of district heating (DH) has been on energy efficiency and minimum operating temperatures. This quest for continuous efficiency improvements led to the modern 4th generation of DH (4GDH), operating at lowest possible temperature for direct utilization by end-user. In recent years the term 5th generation DH (5GDH) has become popular for individual heat pump systems sharing thermal sources via uninsulated pipe network. While 5GDH has similarity with 4GDH it is a technically different solution, as the heat generation is moved to the end-users. When discussing 4GDH and 5GDH the focus quickly revolves about the efficiency of the distribution grid, however the discussion should be on the overall system efficiency and the levelized cost of the heat (LCOH). This paper analyzes LCOH for a mixed building area consisting of a central heat source, high or low energy buildings connected to 4GDH, 5GDH or a 4GDH variant with end-user temperature boosting for domestic hot water purposes. The analysis considers two countries: DK and UK. The analysis further explores the impact of the heat source temperature, from 10 °C to 60 °C, on the LCOH. The results indicate that 4GDH is the more competitive heat supply solution for the considered case. • Levelized cost of heat from 4GDH and 5GDH systems are compared. • The centralized aspects of 4GDH provides clear economic benefits compared to 5GDH. • 4GDH is a preferred solution for both high and low energy consuming buildings. [ABSTRACT FROM AUTHOR]

Distributed energy storage is a solution for increasing self-consumption of variable renewable energy such as solar and wind energy at the end user site. Small-scale energy storage systems can be centrally coordinated by "aggregation" to offer different services to the grid, such as operational flexibility and peak shaving. This paper shows how centralized coordination vs. distributed operation of residential electricity storage (home batteries) could affect the savings of owners. A hybrid method is applied to model the operation of solar photovoltaic (PV) and battery energy storage for a typical UK householder, linked with a whole-system power system model to account for long-term energy transitions. Based on results, electricity consumers can accumulate greater savings under centralized coordination by between 4 and 8% when operating no technology, by 3-11% with electricity storage alone, by 2-5% with stand-alone solar PV, while 0-2% with PV-battery combined. Centralized coordination of home batteries offers more optimized electricity prices in the system, and as such, higher private savings to all consumers. However, consumers without onsite energy technologies benefit more than PV-battery owners. Therefore, based on system-level benefits of aggregation, the regulator should incentivize prosumers with PV-battery, who are able to balance their electricity supply-demand even without central coordination, to let their storage be controlled centrally. Possible revenues of storage owners from ancillary services as well as the cost of aggregation (e.g., transaction fees charged by aggregators) are not considered in this analysis. • Centralized coordination vs. distributed operation of residential solar PV-battery is discussed. • Centralized coordination offers greater savings to prosumers, especially, under time of use tariffs. • Value of home batteries is dependent on the need for flexibility in the energy system in long term. • Consumers without battery benefit from the impact of "storage coordination" on power prices, more than battery owners themselves. • Private benefits of storage aggregation drops by 20% if aggregated storage devices increase five-fold. [ABSTRACT FROM AUTHOR]

Abstract: Smart homes will be a key component of smart grids as without them the functionalities and capabilities offered at network level will not be realised fully by householders. Yet an extensive body of literature focuses on energy consumption and management services via various demand side management programmes and informs us of a significant variation in the realisation of these benefits to reduce demand. However services which can be enabled via smart technology are much broader than simply energy consumption and management, spanning from assisted living to security to remote monitoring, control and management of appliances and devices. Using a combination of in-depth deliberative public workshops, expert interviews and a review of the existing literature, this paper gives an overview of services that smart homes can offer and reveals key barriers to smart home adoption such as interoperability, deregulated electricity supply industry, UK housing stock characteristics, tenure as well as reliability, security, and cost of smart technologies. The findings indicate a need for a holistic view for the design and delivery of smart home services so that consumers are offered integrated services across energy consumption and management to air quality management to security, allowing for individual or tailored services for householders. [Copyright &y& Elsevier]

This paper presents a mixed integer linear programming model for the optimal design of a distributed energy resource (DER) system that meets electricity, heating, cooling and domestic hot water demands of a neighbourhood. The objective is the optimal selection of the system components among different technologies, as well as the optimal design of the heat pipeline network to allow heat exchange between different nodes in the neighbourhood. More specifically, this work focuses on the design, interaction and operation of the pipeline network, assuming the operation and maintenance costs. Furthermore, thermal and cold storage, transfer of thermal energy, and pipelines for transfer of cold and hot water to meet domestic hot water demands are additions to previously published models. The application of the final model is investigated for a case-study of a neighbourhood of five houses located in the UK. The scalability of the model is tested by also applying the model to a neighbourhood of ten and twenty houses, respectively. Enabling exchange of thermal power between the neighbours, including for storage purposes, and using separate hot and cold pipeline networks reduces the cost and the environmental impact of the resulting DER. • The domestic hot water demand for the small neighbourhood can be met by the system. • Thermal energy stored can be distributed to other nodes via the pipeline network. • Houses can send their self-generated thermal energy to a storage tank located in another house via the pipeline network. • The operational and maintenance costs of the pipeline network are accounted for in the overall annual costs of the system. • The optional use of a biomass boiler (BB) to meet space heating and domestic hot water demands. [ABSTRACT FROM AUTHOR]

In this paper the driving factors of carbon dioxide emissions from electricity generation in the European Union are examined for the years 2000–2018, separated into three time periods using decomposition analysis, and particularly LMDI-I. Seven driving factors are examined, namely the economic activity effect, the population effect, the electricity intensity effect, the electricity trade effect, the energy intensity effect, the generation structure effect, and the emissions factor effect. The results showed that the main driving factor leading to increased carbon dioxide emissions is the economic activity effect counterbalanced mainly by the contribution of the generation structure effect. Moreover, a decoupling analysis between economic growth and carbon dioxide emissions was carried out aiming to identify the state of each country for each period. In the third examined period (2013–2018) most countries in the EU-27 are in a state of strong decoupling. • Decomposition analysis of CO2 emissions from the power sector in the EU and the UK. • A decoupling analysis between GDP and CO 2 emissions. • Overall reduction in CO 2 emissions from power generation in the period 2000–2018. • Economic activity effect is the main driving factor for CO2 emissions. • Most countries progressed into a state of strong decoupling. [ABSTRACT FROM AUTHOR]

Abstract: Despite increasingly stringent building energy regulations worldwide, non-compliance exists in practice. This paper examines the profile of compliance with building energy regulations for new-build dwellings. In total 404 new-build dwellings completed in the UK from 2006 to 2009 were investigated. Only a third of these dwellings were evidenced as being compliant with Building Regulations Part L (England and Wales). Such low compliance casts a serious concern over the achievability of the UK Government''s target for all new-build homes to be ‘zero carbon’ from 2016. Clearly evidenced was a lack of knowledge of Part L and its compliance requirements among the supply and building control sides of new-build dwellings. The results also indicate that the compliance profile was influenced by factors including Standard Assessment Procedure (UK Government''s methodology for energy efficiency) calculation submissions, learning and experience of builders and building controls with Part L, use of Part L1A checklist, the introduction of energy performance certificate (EPC), build method, dwelling type, and project size. Better compliance was associated with flats over houses and timber frame over masonry. The use of EPC and Part L1A checklist should be encouraged. Key to addressing the lack of compliance with building energy regulations is training. [Copyright &y& Elsevier]

Abstract: This paper presents a study which linked demographic variables with barriers affecting the adoption of domestic energy efficiency measures in large UK cities. The aim was to better understand the ‘Energy Efficiency Gap’ and improve the effectiveness of future energy efficiency initiatives. The data for this study was collected from 198 general population interviews (1.5–10 min) carried out across multiple locations in Manchester and Cardiff. The demographic variables were statistically linked to the identified barriers using a modified chi-square test of association (first order Rao–Scott corrected to compensate for multiple response data), and the effect size was estimated with an odds-ratio test. The results revealed that strong associations exist between demographics and barriers, specifically for the following variables: sex; marital status; education level; type of dwelling; number of occupants in household; residence (rent/own); and location (Manchester/Cardiff). The results and recommendations were aimed at city policy makers, local councils, and members of the construction/retrofit industry who are all working to improve the energy efficiency of the domestic built environment. [Copyright &y& Elsevier]

Abstract: This paper presents the results of a life cycle assessment of domestic heat pumps in the UK in comparison with gas boilers. The study considers air (ASHP), ground (GSHP) and water (WSHP) source heat pumps. The results show that heat pumps have higher environmental impacts than gas boilers due to the use of electricity. On average, the impacts for the ASHP are 82% higher than from the boiler and 73% for the GSHP and WSHP. The exception to this are the global warming, fossil resource depletion and summer smog impacts which are lower for the pumps than the boilers. For example, up to 36% of CO2 eq. can be saved with the WSHP and 6% with the ASHP in comparison with the boiler. Among the heat pumps considered, ASHP have the highest impacts due to lower efficiencies and higher material requirements for the system. The GSHP and WSHP have comparable impacts, with the latter being marginally better. The life cycle impacts of heat pumps may improve if the UK electricity mix is sufficiently decarbonised; however, they will still remain higher than for the gas boiler. Overall, their potential to contribute to the UK climate change targets is limited. [Copyright &y& Elsevier]

Due to lockdown measures taken by the UK government during the Coronavirus disease 2019 pandemic, the national electricity demand profile presented a notably different performance. The Coronavirus disease 2019 crisis has provided a unique opportunity to investigate how such a landscape-scale lockdown can influence the national electricity system. However, the impacts of social and economic restrictions on daily electricity demands are still poorly understood. This paper investigated how the UK-wide electricity demand was influenced during the Coronavirus disease 2019 crisis based on multivariate time series forecasting with Bidirectional Long Short Term Memory, to comprehend its correlations with containment measures, weather conditions, and renewable energy supplies. A deep-learning-based predictive model was established for daily electricity demand time series forecasting, which was trained by multiple features, including the number of coronavirus tests (smoothed), wind speed, ambient temperature, biomass, solar & wind power supplies, and historical electricity demand. Besides, the effects of Coronavirus disease 2019 pandemic on the Net-Zero target of 2050 were also studied through an interlinked approach. • Impact of COVID-19 measures on UK electricity demands are quantitatively identified. • Nationwide lockdown restrictions accelerated the UK energy demand reduction. • Renewable supplies will keep growing across the UK in the post-epidemic era. [ABSTRACT FROM AUTHOR]

Abstract: Currently, a wide range of strategies is implemented in different countries to increase the share of electricity from renewable energy sources (RES-E). A still controversial discussion is whether quantity-driven (like Tradable Green Certificates (TGCs) based on quotas) or price-driven (like feed-in-tariffs (FIT)) instruments lead to preferable solutions for society. The core objective of this paper is to compare the perspectives of quota-based certificate trading systems for an efficient and effective increase of RES-E with FIT. The major results of this analysis are: (i) The success stories of growth in RES-E in EU member states in recent years has been triggered by FIT implemented in a technology-specific manner at modest costs for European citizens; (ii) At present, TGC systems in most countries applied show a low effectiveness with respect to RES-E deployment of less mature technologies such as solar PV (with improving tendencies in e.g. the UK or Italy with respect to certain technologies); (iii) Compared to short term trading in TGC markets the intrinsic stability of FIT systems appears to be a key element for success; (iv) Hence, currently a well-designed (dynamic) FIT system provides a certain deployment of RES-E in the shortest time and at lowest costs for society. [Copyright &y& Elsevier]

Abstract: The reduction of CO2 emissions linked with human activities (mainly energy services and transport), together with the increased use of renewable energies, remain high priorities on various political agendas. However, considering the increased consumption of energy services (especially electricity), and the stochastic nature of some of the most promising renewable energies (wind for instance), the challenge is to find the optimal mix of technologies that will provide the energy services, without increasing the CO2 emissions, but nonetheless ensuring reliability of supply. The focus of this paper is to present the DESDOP tool, based on mixed integer linear optimisation technics, that helps giving insight in the optimal mix of technologies that will simultaneously help decrease the emissions while at the same time guarantee resilience of supply. The results show that while it is not yet possible to avoid electricity from the grid completely (hence nuclear or fossil fuel), CO2 reductions up to 20%, at no extra costs compared to the business-as-usual case, are easily achievable. [Copyright &y& Elsevier]

Abstract: This paper presents a comparison of the characteristics of the electricity demand for London, UK and Athens, Greece and explores its relationship with both climate and non-climate related factors. Year-to-year trends for both cities are identified, associated mainly with economic, social and demographic factors. In addition, several other effects such as weekly and holiday effects, unrelated to weather conditions are detected and are examined in comparison for the two cities. Gross National Product (GNP) per capita has been employed to aid de-trend the data in order to isolate the weather/climate influence on electricity demand. Temperature has been found to play the most important role in controlling the electricity load demand, especially for Athens. For both cities electricity demand peaks in winter but for Athens a second significant peak is apparent in the summer, not present in London. Thermal comfort levels for both cities have also been identified and compared. The optimum ambient temperature for low levels of electricity demand is found to be 20°C for Athens and 16°C for London. Using the approach of cooling and heating degree-days, it was identified that the sensitivity of the system electricity-demand/air-temperature is greater during the cold period of the year for both cities. [Copyright &y& Elsevier]

This paper aims to evaluate the energy-saving potential, affordability and thermal comfort performance of various passive building retrofit measures for a historical building (late nineteenth-century Victorian house) renovation. Three types of interior passive retrofit measures (i.e. internal wall insulation, glazing upgrade and airtightness improvement), classified as traditional and advanced measures, are further grouped into 63 retrofit combinations. In order to evaluate the performance of these defined retrofit combinations, five assessment indicators are proposed, including energy reduction rate, specific initial cost, discounted payback period, space volume reduction rate, and indoor thermal comfort. Under three internal space reduction scenarios with a space reduction rate of 5.2%, 2.6% and 1.3%, these combinations are evaluated and compared, and the most favourable combinations are determined to achieve multiple objectives (maximum energy saving, most cost-effective and most energy-efficient). The influence of the most favourable retrofit combinations on the internal thermal comfort level improvement is also dynamically evaluated using the EnergyPlus simulation tool. The final recommended passive retrofit combination consists of vacuum insulation windows, gypsum air infiltration reduction, and 2 cm thickness of Polyisocyanurate (PIR) panels, with 51.8% of primary energy reduction, a specific initial investment of £144.71/m2 and a discounted payback period of 18 years. • Traditional and advanced passive retrofit combinations were evaluated in historical building renovation. • Space volume reduction rate is introduced to evaluate the building interior renovation. • A bi-objective optimal approach was proposed considering the effectiveness of energy and cost. • Indoor thermal comfort analysis of overheating was evaluated for risk of passive retrofit combinations. • This holistic approach facilitates the passive retrofit design and decision-making process. [ABSTRACT FROM AUTHOR]

Energy storage can store surplus electricity generation and provide power system flexibility. A Generation Integrated Energy Storage system (GIES) is a class of energy storage that stores energy at some point along with the transformation between the primary energy form and electricity. The investigation of the economic and financial merits of novel energy storage systems and GIES is relevant as these technologies are in their infancy, and there are multiple technological, economic, and financial uncertainties and opportunities. This paper presents and applies a state-of-the-art model to compare the economics and financial merits for GIES (with pumped-heat energy storage) and non-GIES (with a Lithium-ion battery) systems coupled with wind generation in the United Kingdom. The deterministic, risk, and sensitivity analyses show that, for GIES's economics, the key driver is the generator capital cost; for non-GIES, the energy storage capital cost is the most important factor. A Monte Carlo analysis shows that the levelized cost of electricity values for GIES and non-GIES are 0.05 £/kWh - 0.12 £/kWh and 0.07 £/kWh - 0.11 £/kWh, respectively, for a 100 MW wind power generator and 100 MWh energy storage. The internal rate of return values for GIES and non-GIES are uncertain and range between 2%-22% and 5%–14%, respectively. • State-of-the-art cash flow model for generation integrated energy storage (GIES). • Examined the technical, economic, and financial inputs with uncertainties. • First financial and economic comparison of GIES and non-GIES systems. • A UK study with wind energy and pumped thermal energy storage. • Wind farms without energy storage have better economic and financial performance. [ABSTRACT FROM AUTHOR]

Between 2009 and 2017 the share of wind and solar energy sources in the GB electricity generation mix increased from 2.5% to 17%. Due to the variable nature of these renewable sources, large thermal power stations designed for constant base-load operation have been required to operate more flexibly to compensate for fluctuations in renewable generation. This flexible operation results in increased thermal stress and reduced efficiency causing increased operation, maintenance and fuel costs for these assets. In this paper we present the results of what is, to the best of our knowledge, the first empirical study on the impact of renewables generation on startups, ramping and part-loading (collectively, 'cycling') of base-load generators. We develop regression models using half-hourly generation data from 2009 to 2017 that capture the impact of increased renewable penetration while taking into account confounding factors including seasonality and demand. We find that with 2009-levels of renewable generation, cycling in 2017 would have been less severe, with 20% fewer startups. We also present estimates for cycling under National Grid Future Energy Scenarios to 2030 with implications for investment in generation assets. Additionally, the dataset derived in this research is made available and comprises the first open-access dataset on cycling. • A dataset for base-load cycling in the GB power system from 2009 to 2017 is presented. • Base-load cycling is shown to have become stronger during the period 2009–2017. • Regression models for cycling variables are estimated using power system variables. • The models are used to predict cycling under Future Energy Scenarios to 2030. [ABSTRACT FROM AUTHOR]

This paper assesses how operational flexibility and the curtailment of renewable energy are connected using a unit commitment and economic dispatch model that includes operational characteristics of conventional power plants and system constraints. A Great Britain test system is analysed under different scenarios of wind (onshore and offshore) and solar installed capacity, showing that an increase in curtailment is mostly expected as wind deployment increases. This curtailment reaches 17% of the annual available variable renewable electricity generation at high wind and solar installed capacities and is mainly driven by the inertial requirement. The best approach to reducing curtailment is, therefore, to reduce the inertia floor by relaxing Rate of Change of Frequency limits. For the assumed curtailment costs, onshore wind presents a stronger correlation with overall curtailment than offshore wind and solar, albeit influenced by the levels of solar installed capacity. Significant reductions in curtailment can be achieved if wind contributes to system balancing requirements. This emphasizes the importance of ensuring that variable renewables are technically able to contribute to system balancing, wherever feasible, and of improving access to revenue streams that incentivise flexible operation of variable renewable generation. • The inertia floor is the main driver of curtailment in this work. • This curtailment can reach 17% of annual available renewable generation. • Onshore wind has stronger correlation with curtailment than offshore wind or solar. • Curtailment is needed to ensure reserve services when the inertia floor is lowered. • Curtailment can be reduced by 88% if wind contributes to system inertia and reserve. [ABSTRACT FROM AUTHOR]