Quantitative Assessment Method for Industrial Demand Response Potential Integrating STL Decomposition and Load Step Characteristics

Zhuo-Wei Yang, Kai Chang, Ming-Di Shao, Hao Lei and Zhi-Wei Liu ()
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Zhuo-Wei Yang: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Kai Chang: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Ming-Di Shao: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Hao Lei: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Zhi-Wei Liu: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2025, vol. 18, issue 13, 1-21

Abstract: With the increasing penetration of renewable energy, power grids face significant challenges in balancing fluctuating renewable generation with flexible demand-side resources. Industrial loads, characterized by substantial consumption and high adjustability, provide critical flexibility to address these challenges; however, existing methods for quantifying their response potential lack sufficient accuracy and comprehensive uncertainty characterization. This study proposes an integrated quantitative assessment framework combining Seasonal-Trend decomposition using Loess (STL), load-step feature extraction, and Gaussian Process Regression (GPR). Historical industrial load data are first decomposed using STL to isolate trend and periodic patterns, while mathematically defined load-step indicators quantify intrinsic adjustability. Concurrently, a multi-dimensional willingness index reflecting past response behaviors and participation records comprehensively characterizes user response capabilities and inclinations. A GPR-based nonlinear mapping between extracted load features and response potential enables precise quantification and robust uncertainty estimation. Case studies verify the effectiveness of the proposed approach, achieving an assessment accuracy of 91.4% and improved confidence interval characterization compared to traditional methods. These findings demonstrate the framework’s significant capability in supporting precise flexibility utilization, thereby enhancing operational stability in power grids with high renewable energy penetration.

Keywords: industrial demand response; load step characteristics; Gaussian process regression; potential assessment (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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