A bottom-up modeling approach is presented that uses a Markov chain Monte Carlo (MCMC) method to develop demand profiles. The demand profiles are combined with the electrical characteristics of the appliance to create detailed time-varying models of residential loads suitable for the analysis of smart grid applications and low-voltage (LV) demand-side management. The results obtained demonstrate significant temporal variations in the electrical characteristics of LV customers that are not captured by existing load profile or load model development approaches. The software developed within this work is made freely available for use by the community.

To fully assess the impact of possible demand-side management (DSM) actions on a system-wide scale, detailed low-voltage (LV) load models are required. This paper describes the development of a flexible, bottom-up approached profiling tool that transforms user activity profiles into load models. That will allow studying the grid and the influence of the implementation of DSM. The proposed profiling tool allows for variations in the three main areas that will determine the energy demand: the user behaviour, user electrical loads and ambient conditions. Index Terms--Demand side management, electrical load model, power system analysis, residential load category

A detailed study of the potential impact of low voltage (LV) residential demand-side management (DSM) on the cost and greenhouse gas (GHG) emissions is presented. The proposed optimisation algorithm is used to shift non-critical residential loads, with the wet load category used as a case study, in order to minimise the total daily cost and emissions due to generation. This study shows that it is possible to reshape the total power demand and reduce the corresponding cost and emissions to some extent. It is also shown that, when the baseload generating mix is dominated by coal-fired generation, the daily profiles of GHG emissions and cost conflict, such that further optimisation of the cost leads to an increase in emissions.

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This article presents a comprehensive statistical analysis of data obtained from a wide range of literature on the most widely used appliances in the UK residential load sector, as well as a comprehensive technology and market survey conducted by the authors. The article focuses on the individual appliances and begins by consideration of the electrical operations performed by the load. This approach allows for the loads to be categorised based on the electrical characteristics, which is particularly important for implementing load-use statistics in power system analysis. In addition to this, device ownership statistics and probability density functions of power demand are presented for the main residential loads. Although the data presented is primarily intended as a resource for the development of load profiles for power system analysis, it contains a large volume of information that provides a useful database for the wider research community.

This paper presents a Markov chain approach for developing low-voltage (LV) residential load models for use in the analysis of future smart grids. The motivation is to obtain a better understanding of load use and user behaviour at the LV level which will allow future electricity network performance to be improved by understanding the impact of energy demand transformations through the application of demand-side management (DSM) strategies at the LV side. The developed load models highlight the variations between individual and aggregate load model representation and show that the composition of LV residential users should be carefully considered when analysing LV networks.Index Terms-demand-side management, load modelling, markov processes, power demand, smart grids.

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