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During peak electricity demand periods, prices in wholesale markets can be up to nine times higher than during off-peak periods. This is because if a vast number of users is consuming electricity at the same time, power plants with higher greenhouse gas emissions and higher system costs are typically activated. In the UK, the residential sector is responsible for about one third of overall electricity demand and up to 60% of peak demand. This paper presents an analysis of the 2014–2015 Office for National Statistics National Time Use Survey with a view to derive an intrinsic flexibility index based on timing of residential electricity demand. It analyses how the intrinsic flexibility varies compared with wholesale electricity market prices. Findings show that spot prices and intrinsic flexibility to shift activities vary harmoniously throughout the day. Reflections are also drawn on the application of this research to work on demand side flexibility. [ABSTRACT FROM AUTHOR]

Recent research and policy studies on the low-carbon future highlight the importance of flexible electricity demand. This might be problematic particularly for residential electricity demand, which is related to simultaneous consumers' practices in the household. This paper analyses issues of simultaneity in residential electricity demand in Spain. It makes use of the 2011 Spanish Time Use Survey data with comparisons from the previous Spanish Time Use Survey and the Harmonised European Time Use Surveys. Findings show that media activities are associated with the highest levels of continuity and simultaneity, particularly in the early and late parts of the evening during weekdays. [ABSTRACT FROM AUTHOR]

A critical element of assessing a building's suitability for Demand Side Response (DSR) is understanding its turndown potential to ensure that DSR participation will be financially viable. While research has been undertaken on site level DSR estimation methods, there is currently no research that compares the outcomes of these methods. This paper compares four non-domestic energy estimation methods used for understanding the DSR potential of electrical appliances in a building to provide insights about uncertainty levels based on input requirements. Each method is deployed to estimate the DSR potential of HVAC chiller assets at two UK hotels over two years. The results show the methods have a range of error levels from the highest Mean Average Percentage Error (MAPE) of 159% to the lowest MAPE of 39%. The input requirements followed a general trend of more complex informational inputs resulting in lower error values. The outcomes of this research enable users to make informed decisions in selecting DSR estimation methods based on information availability and acceptable estimation error levels. [ABSTRACT FROM AUTHOR]

The creation of a Europe-wide electricity market combined with the increased intermittency of supply from renewable sources calls for an investigation into the risk of aggregate peak demand. This paper makes use of a risk model to assess differences in time-use data from residential end-users in five different European electricity markets. Drawing on the Multinational Time-Use Survey database, it assesses risk in relation to the probability of electrical appliance use within households for five European countries. Findings highlight in which countries and for which activities the risk of aggregate peak demand is higher and link smart home solutions (automated load control, dynamic pricing and smart appliances) to different levels of peak demand risk. [ABSTRACT FROM AUTHOR]

The perspective European Supergrid would consist of an integrated power system network, where electricity demands from one country could be met by generation from another country. This paper makes use of a bi-linear fixed-effects model to analyse the determinants for trading electricity across borders among 34 countries connected by the European Supergrid. The key question that this paper aims to address is the extent to which the privatisation of European electricity markets has brought about higher cross-border trade of electricity. The analysis makes use of distance, price ratios, gate closure times, size of peaks and aggregate demand as standard determinants. Controlling for other standard determinants, it is concluded that privatisation in most cases led to higher power exchange and that the benefits are more significant where privatisation measures have been in place for a longer period. [ABSTRACT FROM AUTHOR]

Residential electricity demand in most European countries accounts for a major proportion of overall electricity consumption. The timing of residential electricity demand has significant impacts on carbon emissions and system costs. This paper reviews the data and methods used in time use studies in the context of residential electricity demand modelling. It highlights key issues which are likely to become more topical for research on the timing of electricity demand following the roll-out of smart metres. [ABSTRACT FROM AUTHOR]

Abstract: The recent roll-out of smart metering technologies in several developed countries has intensified research on the impacts of Time-of-Use (TOU) pricing on consumption. This paper analyses a TOU dataset from the Province of Trento in Northern Italy using a stochastic adjustment model. Findings highlight the non-steadiness of the relationship between consumption and TOU price. Weather and active occupancy can partly explain future consumption in relation to price. [Copyright &y& Elsevier]

Providing homeowners with real-time feedback on their electricity consumption through a dedicated display device has been shown to reduce consumption by approximately 6-10 %. However, recent advances in smart grid technology have enabled larger sample sizes and more representative sample selection and recruitment methods for display trials. By analyzing these factors using data from current studies, this paper argues that a realistic, large-scale conservation effect from feedback is in the range of 3-5 %. Subsequent analysis shows that providing real-time feedback may not be a cost effective strategy for reducing carbon emissions in Australia, but that it may enable additional benefits such as customer retention and peak-load shift. [ABSTRACT FROM AUTHOR]

One of the most common demand side management programs consists of time-of-use (TOU) tariffs, where consumers are charged differently depending on the time of the day when they make use of energy services. This paper assesses the impacts of TOU tariffs on a dataset of residential users from the Province of Trento in Northern Italy in terms of changes in electricity demand, price savings, peak load shifting and peak electricity demand at sub-station level. Findings highlight that TOU tariffs bring about higher average electricity consumption and lower payments by consumers. A significant level of load shifting takes place for morning peaks. However, issues with evening peaks are not resolved. Finally, TOU tariffs lead to increases in electricity demand for substations at peak time. [ABSTRACT FROM AUTHOR]

Abstract: Over the last few years, load growth, increases in intermittent generation, declining technology costs and increasing recognition of the importance of customer behaviour in energy markets have brought about a change in the focus of Demand Response (DR) in Europe. The long standing programmes involving large industries, through interruptible tariffs and time of day pricing, have been increasingly complemented by programmes aimed at commercial and residential customer groups. Developments in DR vary substantially across Europe reflecting national conditions and triggered by different sets of policies, programmes and implementation schemes. This paper examines experiences within European countries as well as at European Union (EU) level, with the aim of understanding which factors have facilitated or impeded advances in DR. It describes initiatives, studies and policies of various European countries, with in-depth case studies of the UK, Italy and Spain. It is concluded that while business programmes, technical and economic potentials vary across Europe, there are common reasons as to why coordinated DR policies have been slow to emerge. This is because of the limited knowledge on DR energy saving capacities; high cost estimates for DR technologies and infrastructures; and policies focused on creating the conditions for liberalising the EU energy markets. [Copyright &y& Elsevier]

[Display omitted] • Combining social practice theory, energy related activity modelling and novel machine learning approaches. • Neural networks can reproduce long-term dependencies in occupant behavior. • Advantages of longitudinal mobility data and detailed activity data are combined in one dataset. • Attention and long short-term memory based models are compared with Markov chains. • New data for consistent simulation of household electricity, heat and mobility demand. Models simulating household energy demand based on different occupant and household types and their behavioral patterns have received increasing attention over the last years due the need to better understand fundamental characteristics that shape the demand side. Most of the models described in the literature are based on Time Use Survey data and Markov chains. Due to the nature of the underlying data and the Markov property, it is not sufficiently possible to consider long-term dependencies over several days in occupant behavior. An accurate mapping of long-term dependencies in behavior is of increasing importance, e.g. for the determination of flexibility potentials of individual households urgently needed to compensate supply-side fluctuations of renewable based energy systems. The aim of this study is to bridge the gap between social practice theory, energy related activity modelling and novel machine learning approaches. The weaknesses of existing approaches are addressed by combining time use survey data with mobility data, which provide information about individual mobility behavior over periods of one week. In social practice theory, emphasis is placed on the sequencing and repetition of practices over time. This suggests that practices have a memory. Transformer models based on the attention mechanism and Long short-term memory (LSTM) based neural networks define the state of the art in the field of natural language processing (NLP) and are for the first time introduced in this paper for the generation of weekly activity profiles. In a first step an autoregressive model is presented, which generates synthetic weekly mobility schedules of individual occupants and thereby captures long-term dependencies in mobility behavior. In a second step, an imputation model enriches the weekly mobility schedules with detailed information about energy relevant at home activities. The weekly activity profiles build the basis for multiple use cases one of which is modelling consistent electricity, heat and mobility demand profiles of households. The approach developed provides the basis for making high-quality weekly activity data available to the general public without having to carry out complex application procedures. [ABSTRACT FROM AUTHOR]

Understanding what constitutes peaks and identifying areas of effective load shifting intervention becomes vital to the balancing of demand and supply of electricity. Whilst there is information about the aggregate level of consumption of electricity, little is known about residential peak demand and what levels of flexibility might be available. Specifically, methodologies linking people's activities and residential electricity load profiles are typically under-investigated. The overall aim of this paper is to introduce methodologies which capture the variation in sequences of activities taking place at times of peak electricity demand. The paper introduces a set of analytical tools which can be deployed when examining time use survey data in energy demand research. It presents the state of the art with modelling load profiles based on time use data and design methodological modifications to improve modelling around peak periods. It is demonstrated how the methodologies presented in the paper can be applied to specific understanding of distributional effects of Time of Use tariffs. The paper discusses issues associated with validation between synthetic data, survey data and electricity metered data and concludes with policy implications and some observations for future research. • Effects of Time of Use tariffs can be analysed using sequence analysis. • Demand flexibility depends on sequences of activities. • Clustering methods can be applied in demand side flexibility research. [ABSTRACT FROM AUTHOR]

The article presents author's comments on the European Risk Forum (ERF) focused on improving the risk management in the European Union (EU) and offers recommendations for the European Commission to increase the use of risk management in EU legislation. It mentions that the risk management among financial regulators and banking institutions should be considered by observing risk assessment and risk management, including the European Commission.

This paper presents an exploratory analysis of microgeneration installer businesses in the UK during a period of intense change in policies supporting microgeneration from 2010 to 2012. The research examines the influence of installer businesses on rates of uptake and standards of installation, and the interplay between business practices and the policy environment. The research developed new detailed datasets through a nationwide survey, to which 388 installers responded, and follow-up interviews with 22 installers. Focusing on solar photovoltaics and air source heat pumps installed in households, the results show the fundamental dependence of installer businesses on government financial incentives and on the quality assurance scheme in operation. Market confidence was compromised by the sharp reduction in the Feed-In Tariff (FIT) for residential solar PV in 2012 and long delays to the equivalent Renewable Heat Incentive for residential installations. Nevertheless, more modest FIT levels have reduced the risk of sub-optimal installations and inappropriate specification of microgeneration systems. The findings underline the need for consistent policy to allow installer businesses and their supply chains to develop and mature, and thus facilitate commercial deployment of microgeneration of high quality, raise its competiveness with incumbent forms of energy supply and contribute to decarbonisation goals. [ABSTRACT FROM AUTHOR]

• Occupant behavior (OB) models that can reproduce spatial variation were developed. • Existence of spatial variation in OB was confirmed using global Moran's index test. • Two spatial representations were formulated as smooth functions. • Three spatial logistic regression models were developed and evaluated. • The designed models are superior to the conventional logistic regression model. Numerous occupant behavior (OB) models that simulate occupancy, activity and action at home have been developed to improve the accuracy and quality of energy demand estimations. Previous studies have revealed that the consideration of inter-occupant diversity improves the performance of OB models. However, existing models ignore spatial variation in OBs or partially consider it using a simple method without evaluating whether it is sufficient. Moreover, the modeling method to reproduce the spatial variation is missing. This study aims to develop a modeling method that can effectively reproduce spatial variation in OBs using American time use data. The global Moran's index test confirmed that spatial variations exist in OBs; however, they differ by time of day and activities for studied population. Subsequently, two spatial variation representations were generated using the ordinary kriging and spatial autoregressive methods. Finally, three spatial logistic regression models that consider spatial variations were developed and evaluated. The developed models generated smaller errors and higher inter-occupant diversity than the conventional logistic regression models at the state level. The established method is applicable to any country and region. Using higher spatial resolution and richer time use datasets may further improve OB models to model region-specific characteristics of building energy demand. [ABSTRACT FROM AUTHOR]

Abstract: This study presents the findings of applying a Discrete Demand Side Control (DDSC) approach to the space heating of two case study buildings. High and low tolerance scenarios are implemented on the space heating controller to assess the impact of DDSC upon buildings with different thermal capacitances, light-weight and heavy-weight construction. Space heating is provided by an electric heat pump powered from a wind turbine, with a back-up electrical network connection in the event of insufficient wind being available when a demand occurs. Findings highlight that thermal comfort is maintained within an acceptable range while the DDSC controller maintains the demand/supply balance. Whilst it is noted that energy demand increases slightly, as this is mostly supplied from the wind turbine, this is of little significance and hence a reduction in operating costs and carbon emissions is still attained. [Copyright &y& Elsevier]

In order to have the best possible chance of achieving 'decent work' and 'climate action' as laid forth in the UN Sustainable Development Goals, government and policy makers must pay close attention to current time-use patterns, as well as the way these might change in the near future. Here we contribute to the existing literature on time-use behaviour through a systematic exploration of the relationship between working patterns and energy consumption from the perspective of time-use. Our starting point is the premise that different work arrangements impact the timing of energy demand not only in workplaces, but also at home. Using the data from the 2014–2015 UK time-use survey, we were able to capture patterns of time-use behaviours and to assess their relationship with daily energy consumption. We propose a systematic time-use-based approach for estimating residential energy consumption with regards to activity timing, activity location, activity coordination, and appliance type. We use this method to discover patterns in residential activities and energy consumption, as well as the causal relationship between residential energy consumption and work patterns. In this study, we unpack the heterogeneity in the work–energy relationship, particularly when comparing full-time and part-time workers. Our results suggest that full-time employees have a higher potential to reduce their energy use compared to part-time employees. We also discover a non-linear change in total energy consumption for respondents with varying levels of work time. Energy consumption reductions associated with differences in work schedules are greatest during the first few hours of the workday, but then level off. Our findings suggests that time-use data can provide useful insights for evaluating and possibly designing energy and labour-market policies. [ABSTRACT FROM AUTHOR]

[Display omitted] • The profile DSR estimation method features a better balance of error compared with previous methods. • Clustering of detailed usage data to create profiles is a viable approach for improving DSR estimation. • Profiles can provide greater account of estimation uncertainty compared to existing deterministic methods. In principle, Demand Side Response (DSR) is increasingly seen as a critical component of a low-carbon electricity network with renewables as main sources of generation. In practice, DSR has been slow to emerge in most electricity markets of developed and developing countries. One of the main reasons for the slow penetration of DSR is the difficulty to assess the flexibility potential of individual sites. This paper develops a new DSR estimation method which uses detailed profiles information based on data on Heating, Ventilation and Air Conditioning (HVAC) chillers in five UK hotels between 2013 and 2017 and applies a combination of clustering analysis and subsequent stochastic sampling using cluster-weighted date-based predictors. Findings show that the profile DSR estimation method features a better balance of error compared with previous methods. [ABSTRACT FROM AUTHOR]

Concerns about climate change, pollution and energy security have prompted policies aiming at replacing fossil fuels (in heating and cooling, and transportation) with electricity, presumably generated from renewable sources. Climate change itself is expected to increase the demand for cooling in buildings, which is generally met with electricity-powered air conditioning. We use hourly electricity demand from a sample of Italian residences over a full year to examine how sensitive residential demand is to temperature. Our regression model includes a rich set of household-by-time fixed effects to control for dwelling characteristics and equipment, family composition, work and business schedules, demand for lighting, and seasonal habits other than temperature. These allows us to separate the effect of temperature from the demand for lighting and from other seasonal effects that may be correlated with temperature, but are not temperature. We find that demand stays unchanged within a relatively narrow range (and is thus relatively flat) up to temperatures of about 24.4 °C, and increases sharply with temperature thereafter. We find that temperature accounts for a very small share of daily electricity demand. Only on exceptionally hot summer days can temperature account for 12% of hourly electricity use. • The residential sector is an important component of daily electricity load. • We have hourly electricity loads from some 400 homes in Italy over 2011. • We seek to separate demand for lighting, plus seasonal and daily behaviors and habits from temperature-related demand. • Temperature accounts for only a small share of electricity usage. • Electricity consumption increases sharply with temperature at temperatures of 24.4 °C and higher. [ABSTRACT FROM AUTHOR]