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Decision-Making Tree Analysis for Industrial Load Classification in Demand Response Programs

Industrial loads play an important role in the success of demand response programs (DRPs). However, these programs may compromise the consumers' convenience, which can overshadow their real-world practicality. In response, this article provides a two-level decision-making tree approach to effectively determine the participation abilities of different industrial processes in DRPs considering various features and abilities of these customers. The level I of this framework introduces several classifying variables by which a basic criterion is extracted to classify different industrial processes applying the analytic hierarchy process (AHP). A participation factor is then introduced in level II of the suggested decision tree to estimate the participation level of different classes attained in level I. Finally, a desirability coefficient is formulated, offering the system operators an efficient indicator to verify the attractiveness of different incentive-based programs in the viewpoint of industrial customers. Implementing the presented framework on industrial customers of a region in Iran, it is shown that applying this method lends the decision-makers a hand in practically and effectively introducing DRPs for industrial customers.