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Abstract-This paper proposes a methodology for demand profiling, namely load decomposition, of aggregated residential load based on smart meter (SM) data. The methodology is applicable to both active and reactive load, following an assumption that SMs can monitor real-time active power consumption of individual appliances. Only a number of households in the aggregation are equipped with SMs in this study. The non-monitored users' load is decomposed using artificial neural network (ANN) trained with the available SM data. Information about load composition, in terms of load categories or load controllability, can be highly beneficial for various demand response (DR) applications. Different levels of SM coverage are considered in the study to illustrate the effect of the level of SM coverage on the accuracy of total aggregated load decomposition. The results show that the consumption of some load categories can be estimated with high confidence, even at lower levels of SM coverage.

This paper proposes a methodology for advanced demand side management (DSM) in the distribution network (DN), catering at the same time for the requirements of the network operator, transmission or distribution, the available flexibility of the demand side, and the preservation of network performance. The basic premise of the study is that the distribution network operator is providing flexibility to the transmission system operator through load shaping. Different, one or more, indicators can be chosen to assess preservation of network performance. In this paper, for illustrative purposes of the methodology, the steady state voltage stability index, namely the load margin, is chosen as the network performance indicator. This is evaluated before and after a DSM action, in order to analyse the possible effect of DSM on network loadability. Load at each DN bus is represented using a realistic composite load model comprising controllable and uncontrollable loads. The DSM is initially performed based on optimal power flow calculation to ensure that the distribution system load profile at the grid supply point follows the required (pre-specified) load profile during a 24 hour period. Following this, a particle swarm optimisation is used to modify DSM program in those time steps of the planning horizon where load margin is reduced. The methodology is illustrated on a number of case studies using modified IEEE 33 bus distribution network model. The results illustrate the extent to which the available flexibility of demand can be used in support of transmission network operation when the preservation of network performance and appropriate load modelling are considered.

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