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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. [ABSTRACT FROM AUTHOR]

These two-part paper series aim to optimize the rates of inspection-based maintenance or routine-test on power system equipment. Part I introduced a novel probabilistic model using Markov chains for the inspection-based maintenance and derived the relationships between the transition rates. Part II demonstrates how the proposed method can effectively address budget and data availability constraints. It also presents the derived relationships between the optimal preventive maintenance rate and risk indices. The results presented in this paper are based on an industrial project conducted to optimize the preventive maintenance period of high-voltage equipment in Iran. Additionally, this companion paper provides practical insights, including implementation remarks, and a comprehensive dataset for failure and defect analysis, which proves useful for future research and applications. • A case study on a real sub-transmission substation for the proposed model. • Derived relations between the optimal preventive maintenance rate and risk indices. • Analysis of the simultaneity in the inspection of series components. • How to overcome data and budget limitations by the proposed sensitivity analysis. • Practically, how to use the available data to optimize the inspection rate. [ABSTRACT FROM AUTHOR]