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Decision-Making tree analysis for industrial load classification in demand response programs

Dehghan Dehnavi, S ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1109/TIA.2020.3032932
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2021
  4. Abstract:
  5. 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. © 1972-2012 IEEE
  6. Keywords:
  7. Analytic hierarchy process ; Decision trees ; Sales ; Analytic hierarchy process (ahp) ; Decision making tree ; Demand response programs ; Incentive-based programs ; Industrial customer ; Industrial loads ; Industrial processs ; Participation factors ; Decision making
  8. Source: IEEE Transactions on Industry Applications ; Volume 57, Issue 1 , 2021 , Pages 26-35 ; 00939994 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/9237157