Under the smart power systems, determining the amount of Demand Response Resources(DRRs) potential is considered as a crucial issue due to affecting in all energy policy decisions. In this paper, the potential of DRRs in presence of cooling and heating equipment are ide More
Under the smart power systems, determining the amount of Demand Response Resources(DRRs) potential is considered as a crucial issue due to affecting in all energy policy decisions. In this paper, the potential of DRRs in presence of cooling and heating equipment are identified using k-means clustering algorithm as a data mining technique. In this regard, the energy consumption dataset are categorized in different clusters by k-means algorithm based upon variations of energy price and ambient temperature during peak hours of hot (Spring and Summer) and cold (Autumn and Winter) periods. Then, the clusters with the possibility of cooling and heating equipment’s commitment are selected. After that, the confidence interval diagram of energy consumption in elected clusters is provided based upon energy price variations. The nominal potential of DRRs, i.e. flexible load, will be obtained regarding the maximum and minimum differences between the average of energy consumption in upper and middle thresholds of the confidence interval diagram. The energy consumption, ambient temperature and energy price related to BOSTON electricity network over a six-year horizon time is utilized to evaluate the proposed model.
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