Abstract
Many studies have examined the potential of the flexible loads within the power systems, taking advantage of Demand Response (DR) programs and Data Mining (DM) to optimize the system response and achieve sustainability through load management. The authors propose aggregation models to study their impact on the power system, using unsupervised DM technique which is Clustering (k-means). The scientific contribution of this paper is related to providing peak-reduction using clustering for load aggregation. A case study has been provided, consisting of three scenarios based on the load aggregation model. The results indicate the system responses to the different scenarios and illustrate the features of each load aggregation model. Furthermore, the results demonstrate how using DR programs combined with DM can effectively provide benefits to the system stability.
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