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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....
This paper introduces the reasons for big data analytics in distribution network studies and potential benefits it could give. Summary of the most common data mining methods used in power system studies is also given, followed by a comparative analysis. A use case is shown at the end in order to present some examples of extraction of useful information from raw data stored in a real distribution utility's...
Real-time load composition knowledge will dramatically benefit demand-side management (DSM). Previous works disaggregate the load via either intrusive or nonintrusive load monitoring. However, due to the difficulty in accessing all houses via smart meters at all times and the unavailability of frequently measured high-resolution load signatures at bulk supply points, neither is suitable for frequent...
Accurate prediction of load composition at bulk supply points can significantly improve power system planning, electricity market analysis and demand side management. This paper discusses an artificial neural network (ANN) based approach to forecasting load composition at the bulk supply bus based on RMS measurement of voltage, real and reactive power and local forecasted weather. Probabilistic distributions...
Large deployment of distributed generation into distribution systems brings new challenges regarding the shift from the passive to the active control paradigm. These challenges have been extended to the field of dynamic equivalence. Developing effective reduced order models for active distribution network cells for dynamic and stability studies require a careful evaluation of the techniques that have...
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