The diversity of residential electricity demand – a comparative analysis of metered and simulated data

A comparative study between simulated residential electricity demand data and metered data from theUK Household Electricity Survey is presented. For this study, a high-resolution probabilistic model wasused to test whether this increasingly widely used modelling approach provides an adequate represen-tation of the statistical characteristics the most comprehensive dataset of metered electricity demandavailable in the UK. Both the empirical and simulated electricity consumption data have been analysedon an aggregated level, paying special attention to the mean daily load profiles, the distribution of house-holds with respect to the total annual demands, and the distributions of the annual demands of particularappliances. A thorough comparison making use of both qualitative and quantitative methods was madebetween simulated datasets and it’s metered counterparts. Significant discrepancies were found in thedistribution of households with respect to both overall electricity consumption and consumption ofindividual appliances. Parametric estimates of the distributions of metered data were obtained, and theanalytic expressions for both the density function and cumulative distribution are given. These can beincorporated into new and existent modelling frameworks, as well as used as tools for further analysis

Reducing Electricity Demand Charge for Data Centers with Partial Execution

Data centers consume a large amount of energy and incur substantial electricity cost. In this paper, we study the familiar problem of reducing data center energy cost with two new perspectives. First, we find, through an empirical study of contracts from electric utilities powering Google data centers, that demand charge per kW for the maximum power used is a major component of the total cost. Second, many services such as Web search tolerate partial execution of the requests because the response quality is a concave function of processing time. Data from Microsoft Bing search engine confirms this observation. We propose a simple idea of using partial execution to reduce the peak power demand and energy cost of data centers. We systematically study the problem of scheduling partial execution with stringent SLAs on response quality. For a single data center, we derive an optimal algorithm to solve the workload scheduling problem. In the case of multiple geo-distributed data centers, the demand of each data center is controlled by the request routing algorithm, which makes the problem much more involved. We decouple the two aspects, and develop a distributed optimization algorithm to solve the large-scale request routing problem. Trace-driven simulations show that partial execution reduces cost by $3\%--10.5\%$ for one data center, and by $15.5\%$ for geo-distributed data centers together with request routing.Comment: 12 page

Developing and testing a generic micro-combined heat and power model for simulations of dwellings and highly distributed power systems

This paper elaborates an approach to the modelling of domestic micro-combined heat and power (μ-CHP) using a building simulation tool that can provide a detailed picture of the environmental performance of both the μ-CHP heating system and the dwelling it serves. The approach can also provide useful data for the modelling of highly distributed power systems (HDPS). At the commencement of the work described in this paper no μ-CHP device model that was compatible with a building simulation tool was available. The development of such a model is described along with its calibration and verification. The simulation tool with the device model was then applied to the analysis of a dwelling with a Stirling engine-based heating system. Different levels of thermal insulation and occupancy types were modelled. The energy and environmental performance of the μ-CHP device was quantified for each case; additionally, the potential for its participation in the control and operation of an HDPS was assessed. Analysis of the simulation results indicated that the parasitic losses associated with the μ-CHP system balance of plant reduced the overall heating system efficiency by up to 40 per cent. Performance deteriorated with increasing levels of insulation in the dwelling, resulting in reduced thermal efficiency and increased cycling, though overall fuel use was reduced. The analysis also indicated that the device was generally available to participate in HDPS control for greater than 90 per cent of the simulation time. The potential length of the participation time ranged from 1 to 800+min and depended upon the state of the μ-CHP system thermal buffer and prevailing heat loads. Probabilities for different participation times and modes were calculated

An In Depth Study into Using EMI Signatures for Appliance Identification

Energy conservation is a key factor towards long term energy sustainability. Real-time end user energy feedback, using disaggregated electric load composition, can play a pivotal role in motivating consumers towards energy conservation. Recent works have explored using high frequency conducted electromagnetic interference (EMI) on power lines as a single point sensing parameter for monitoring common home appliances. However, key questions regarding the reliability and feasibility of using EMI signatures for non-intrusive load monitoring over multiple appliances across different sensing paradigms remain unanswered. This work presents some of the key challenges towards using EMI as a unique and time invariant feature for load disaggregation. In-depth empirical evaluations of a large number of appliances in different sensing configurations are carried out, in both laboratory and real world settings. Insights into the effects of external parameters such as line impedance, background noise and appliance coupling on the EMI behavior of an appliance are realized through simulations and measurements. A generic approach for simulating the EMI behavior of an appliance that can then be used to do a detailed analysis of real world phenomenology is presented. The simulation approach is validated with EMI data from a router. Our EMI dataset - High Frequency EMI Dataset (HFED) is also released

Electricity Consumption and Economic Growth Nexus: A Multivariate Analysis for Turkey

This study examines the short-run and long-run causality issues between electricity consumption and economic growth in Turkey for 1968–2006 period by using Granger causality models augmented with a lagged error-correction term. The bounds F–test for cointegration test yields evidence of a long-run relationship between employment ratio, electricity consumption per capita and real GDP per capita. The overall results from the three error-correction based Granger causality models show that there is an evidence of unidirectional short-run, long-run and strong causalities running from the electricity consumption per capita to real GDP per capita. But, there is no causal evidence from the real GDP per capita to electricity consumption per capita. In other words, “Growth hypothesis” is confirmed in Turkey. This suggests that electricity consumption plays an important role in economic growth.electricity consumption, economic growth, causality