44 resultados para Rectifiability of demand


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The prospect of a European Supergrid calls for research on aggregate electricity peak demand and Europe-wide Demand Side Management. No attempt has been made as yet to represent a time-related demand curve of residential electricity consumption at the European level. This article assesses how active occupancy levels of single-person households vary in single-person household in 15 European countries. It makes use of occupancy time-series data from the Harmonised European Time Use Survey database to build European occupancy curves; identify peak occupancy periods; construct time-related electricity demand curves for TV and video watching activities and assess occupancy variances of single-person households.

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To achieve CO2 emissions reductions the UK Building Regulations require developers of new residential buildings to calculate expected CO2 emissions arising from their energy consumption using a methodology such as Standard Assessment Procedure (SAP 2005) or, more recently SAP 2009. SAP encompasses all domestic heat consumption and a limited proportion of the electricity consumption. However, these calculations are rarely verified with real energy consumption and related CO2 emissions. This paper presents the results of an analysis based on weekly head demand data for more than 200 individual flats. The data is collected from recently built residential development connected to a district heating network. A methodology for separating out the domestic hot water use (DHW) and space heating demand (SH) has been developed and compares measured values to the demand calculated using SAP 2005 and 2009 methodologies. The analysis shows also the variance in DHW and SH consumption between both size of the flats and tenure (privately owned or housing association). Evaluation of the space heating consumption includes also an estimation of the heating degree day (HDD) base temperature for each block of flats and its comparison to the average base temperature calculated using the SAP 2005 methodology.

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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 substation 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.

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‘Sustainable’ or ‘green’ commercial buildings are frequently seen as a growth sector in the property investment market. This research examines the emergence of sustainable commercial buildings in both the UK and overseas. The empirical part of the paper is based on a telephone survey of 50 UK corporate (private sector) occupiers taking leased and owner–occupied office space, which was carried out during the period of April to November 2008. The survey focused on actual moves made within the previous two years, or moves that were imminent during 2006–2008. The research suggests that although there is an emerging and increasing demand for sustainable offices in the UK, other factors such as location and availability of stock continue to remain more important than sustainability in determining occupiers’ final choice of office. Occupiers who moved to a Building Research Establishment Environmental Assessment Method (BREEAM)‐rated building, and were in business sectors with strong environmental and corporate responsibility policies, placed more emphasis on sustainability than other groups in the final choice of office, but location and availability remained paramount.

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For decades regulators in the energy sector have focused on facilitating the maximisation of energy supply in order to meet demand through liberalisation and removal of market barriers. The debate on climate change has emphasised a new type of risk in the balance between energy demand and supply: excessively high energy demand brings about significantly negative environmental and economic impacts. This is because if a vast number of users is consuming electricity at the same time, energy suppliers have to activate dirty old power plants with higher greenhouse gas emissions and higher system costs. The creation of a Europe-wide electricity market requires a systematic investigation into the risk of aggregate peak demand. This paper draws on the e-Living Time-Use Survey database to assess the risk of aggregate peak residential electricity demand for European energy markets. Findings highlight in which countries and for what activities the risk of aggregate peak demand is greater. The discussion highlights which approaches energy regulators have started considering to convince users about the risks of consuming too much energy during peak times. These include ‘nudging’ approaches such as the roll-out of smart meters, incentives for shifting the timing of energy consumption, differentiated time-of-use tariffs, regulatory financial incentives and consumption data sharing at the community level.

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Reduced flexibility of low carbon generation could pose new challenges for future energy systems. Both demand response and distributed storage may have a role to play in supporting future system balancing. This paper reviews how these technically different, but functionally similar approaches compare and compete with one another. Household survey data is used to test the effectiveness of price signals to deliver demand responses for appliances with a high degree of agency. The underlying unit of storage for different demand response options is discussed, with particular focus on the ability to enhance demand side flexibility in the residential sector. We conclude that a broad range of options, with different modes of storage, may need to be considered, if residential demand flexibility is to be maximised.

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This article reports the results of an experiment that examined how demand aggregators can discipline vertically-integrated firms - generator and distributor-retailer holdings-, which have a high share in wholesale electricity market with uniform price double auction (UPDA). We initially develop a treatment where holding members redistribute the profit based on the imposition of supra-competitive prices, in equal proportions (50%-50%). Subsequently, we introduce a vertical disintegration (unbundling) treatment with holding-s information sharing, where profits are distributed according to market outcomes. Finally, a third treatment is performed to introduce two active demand aggregators, with flexible interruptible loads in real time. We found that the introduction of responsive demand aggregators neutralizes the power market and increases market efficiency, even beyond what is achieved through vertical disintegration.

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Often, firms have no information on the specification of the true demand model they are faced with. It is, however, a well established fact that trial-and-error algorithms may be used by them in order to learn how to make optimal decisions. Using experimental methods, we identify a property of the information on past actions which helps the seller of two asymmetric demand substitutes to reach the optimal prices more precisely and faster. The property concerns the possibility of disaggregating changes in each product’s demand into client exit/entry and shift from one product to the other.

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It is increasingly important to know about when energy is used in the home, at work and on the move. Issues of time and timing have not featured strongly in energy policy analysis and in modelling, much of which has focused on estimating and reducing total average annual demand per capita. If smarter ways of balancing supply and demand are to take hold, and if we are to make better use of decarbonised forms of supply, it is essential to understand and intervene in patterns of societal synchronisation. This calls for detailed knowledge of when, and on what occasions many people engage in the same activities at the same time, of how such patterns are changing, and of how might they be shaped. In addition, the impact of smart meters and controls partly depends on whether there is, in fact scope for shifting the timing of what people do, and for changing the rhythm of the day. Is the scheduling of daily life an arena that policy can influence, and if so how? The DEMAND Centre has been linking time use, energy consumption and travel diary data as a means of addressing these questions and in this working paper we present some of the issues and results arising from that exercise.

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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.

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Clustering methods are increasingly being applied to residential smart meter data, providing a number of important opportunities for distribution network operators (DNOs) to manage and plan the low voltage networks. Clustering has a number of potential advantages for DNOs including, identifying suitable candidates for demand response and improving energy profile modelling. However, due to the high stochasticity and irregularity of household level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper we present in-depth analysis of customer smart meter data to better understand peak demand and major sources of variability in their behaviour. We find four key time periods in which the data should be analysed and use this to form relevant attributes for our clustering. We present a finite mixture model based clustering where we discover 10 distinct behaviour groups describing customers based on their demand and their variability. Finally, using an existing bootstrapping technique we show that the clustering is reliable. To the authors knowledge this is the first time in the power systems literature that the sample robustness of the clustering has been tested.