@Article{ee.2026.074324,
AUTHOR = {Qianpeng Hao, Qiang Li, Changyuan Yu, Deqing Zhang, Wenze Li, Yaowen Liu, Chao Wang, Chengran Song, Xiyu Feng, Xingchao Guo},
TITLE = {Assessment and Scheduling Priority of Industrial Load Regulation Capability for Demand Response in New-Type Power Systems},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/26946},
ISSN = {1546-0118},
ABSTRACT = {Driven by the “Carbon Peak and Carbon Neutrality” strategic goals, high penetration of renewable energy poses severe challenges to power system flexibility. Unlocking the adjustable potential of demand-side industrial loads has become a critical pathway for constructing new-type power systems. To address the limitations of existing research, including single-dimensional characterisation of adjustable potential, insufficient consideration of both best and worst solutions in evaluation methods, and a lack of cluster coordination perspectives, this paper proposes a multi-dimensional adjustable potential assessment and priority ranking method for industrial loads. Firstly, based on the Affinity Propagation (AP) and k-means secondary clustering algorithms, interruptible and transferable regulation behaviour patterns are identified from massive historical load data, and multi-dimensional characteristic indicators covering capacity, time, and rate are extracted. Secondly, a comprehensive weight determination model integrating Best-Worst Method (BWM), improved CRITIC method, and game theory combination weighting is constructed, and the Set Pair Analysis-Variable Fuzzy Set (SPA-VFS) theory is introduced to establish a comprehensive assessment method for industrial users’ adjustable potential. Finally, empirical analysis is conducted taking industrial users in a typical region as an example. The results demonstrate that the proposed method can effectively distinguish the regulation capability differences among different users. The comprehensive adjustable potential value of User 1 reaches 0.833, which is significantly superior to other industrial users, and the ranking results show better consistency and discrimination compared with TOPSIS and linear weighting methods. Meanwhile, the introduction of “time” and “rate” dimensions has a significant impact on the evaluation results. After removing these dimensions, the ranking of User 5 and User 6 is reversed, verifying the necessity of multi-dimensional feature characterisation. This study provides theoretical support and decision-making basis for industrial loads to participate in multi-time-scale dispatching of power systems.},
DOI = {10.32604/ee.2026.074324}
}