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There is an increase in renewable energy sources connected to the electricity grid due to recent drives to achieve grid decarbonization milestones. However, such expansions cause grid balancing issues due to the renewable sources intermittency. Thus, grid operators introduced demand response  (DR)  schemes to mitigate this problem by controlling consumer load demands in exchange for incentives. Industries have enormous electricity demand making them ideal candidates for such programs. Nonetheless, non-intrusive demand load flexibility assessment for an industry&#x0027;s potential in DR programs remains a challenge. In this article, a data-driven framework for quantifying the DR potential of an industrial consumer is proposed. The framework uses smart electricity meter data to identify operational patterns to derive a flexibility boundary that quantifies the flexibility in the industrial consumer&#x0027;s system. The framework also evaluates the  DR  participation scenario to quantify the net benefit of trading the identified flexibility. A Case-study has been carried out for two industrial consumers (i.e., an electronics factory and a poultry feed factory). Initial energy behavioral analysis indicates three different energy use patterns for the electronics factory and six energy use patterns for the poultry feed factory. Evaluating the operational flexibility boundary for the clusters, the framework found two feasible clusters with DR potentials for the electronics factory and three feasible clusters for the poultry feed factory. The cost-benefit analysis indicates a potential energy cost reduction in the region of 5&#x0025;&#x2013;8&#x0025; for passive participation and as much as 12&#x0025;&#x2013;24&#x0025; for active participation. The framework could be adopted to evaluate wide scale industrial consumer&#x0027;s flexibility potential.

A Data-Driven Framework for Quantifying Demand Response Participation Benefit of Industrial Consumers