Multi-scale electrical load modelling for demand-side management

Collin, Adam J.; Tsagarakis, George; Kiprakis, Aristides; McLaughlin, Stephen

doi:10.1109/isgteurope.2012.6465866

Cited by 11 publications

(3 citation statements)

References 15 publications

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“…The overall deviation in domestic demand (and behaviour) was studied in Ref. [32], which was based on a detailed model of domestic demand using real data from Time of Use Survey (TUS) [33], in order to keep human behaviour as realistic as possible. Fig.…”

Section: Real World Data and Tcl Loads' Population Analysismentioning

confidence: 99%

“…In both cases the operating cycles are determined by the compound dynamics of the system, and an equivalent (one compartment) simplified model of the system needs to be developed for computational purposes, to avoid slow simulation but also enable the use of a model as designed above. This becomes clear by looking (30), (31) or (32), (33), which after adding the stochastic elements of cold loads would be much harder to compute.…”

Section: Equivalent Thermal Models For Multi-compartment Tclsmentioning

confidence: 99%

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Human in the loop heterogeneous modelling of thermostatically controlled loads for demand side management studies

Kleidaras

Kiprakis

Thompson

2018

Energy

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Index Terms:Demand side management Demand response Thermostatically controlled loads Markov models a b s t r a c t Demand Response (DR) is a Smart Grid technology aiming to provide demand regulation for dynamic pricing and ancillary services to the grid. Thermostatically controlled loads (TCLs) are among those with the highest potential for DR. Some of the challenges in modelling TCLs is the various factors that affect their duty cycle, mainly human behaviour and external conditions, as well as heterogeneity of TCLs (load parameters). These add an element of stochasticity, with detrimental impact on the aggregated level. Most models developed so far use Wiener processes to represent this behaviour, which in aggregated models, such as those based on Coupled Fokker-Planck Equations (CFPE), have a negligible effect as "white noise". One of the main challenges is modelling the effect of external factors on the state of TCLs' aggregated population and their impact in heterogeneity during operation. Here we show the importance of those factors as well as their detrimental effect in heterogeneity using cold loads as a case study. A bottom up detailed model has been developed starting from thermal modelling to include these factors, real world data was used as input for realistic results. Based on those we found that the duty cycle of some TCLs in the population can change significantly and thus the state of the TCLs' population as a whole. Subsequently, the accuracy of aggregation models assuming relative homogeneity and based on small stochasticity (i.e. Wiener process with typical variance 0.01) is questionable. We anticipate similar realistic models to be used for real world applications and aggregation methods based on them, especially for cold loads and similar TCLs, where external factors and heterogeneity in time are significant. DR control frameworks for TCLs should also be designed with that behaviour in mind and the developed bottom up model can be used to evaluate their accuracy.

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Section: Real World Data and Tcl Loads' Population Analysismentioning

confidence: 99%

Section: Equivalent Thermal Models For Multi-compartment Tclsmentioning

confidence: 99%

Human in the loop heterogeneous modelling of thermostatically controlled loads for demand side management studies

Kleidaras

Kiprakis

Thompson

2018

Energy

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“…There are several studies that collect residential data, including energy consumption, household demongraphics, etc. Some examples include 1) American Time Use Survey [2], that includes demographic informa-tion regarding residential households across the U.S. but no energy consumption data (works using similar datasets from France [3], U.K. [4] and Spain [5], 2) Residential Energy Consumption Survey [6] include residential energy consumption information but the data granularity is very coarse and demographic information is limited, 3) MIT REDD [7] and Smart* [8] datasets include detailed energy consumption data but no demographics information for a very limited set of houses. As can be seen, the previous datasets do not provide a good opportunity to combine energy and demographic analysis in a fine-grained manner.…”

Section: Introductionmentioning

confidence: 99%

Demographical Energy Usage Analysis of Residential Buildings

Sokolova

Akşanlı

2019

Journal of Energy Resources Technology

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Residential energy consumption constitutes a significant portion of the overall energy consumption. There are significant amount of studies that target to reduce this consumption, and these studies mainly create mathematical models to represent and regenerate the energy consumption of individual houses. Most of these models assume that the residential energy consumption can be classified and then predicted based on the household size. As a result, most of the previous studies suggest that the household size can be treated as an independent variable which can be used to predict energy consumption. In this work, we test this hypothesis on a large residential energy consumption dataset that also includes demographic information. Our results show that other variables such as income, geographic location, house type, and personal preferences strongly impact energy consumption and decrease the importance of the household size because the household size can explain only 26.55% of the electricity consumption variation across the houses.

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