@Article{ee.2026.085090,
AUTHOR = {Yiming Ke, Kai Chen, Xuri Huang},
TITLE = {Demand Response of Energy-Intensive Industrial Loads: Synergistic Optimization of Differentiated Modeling and Incentive Strategies},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/27476},
ISSN = {1546-0118},
ABSTRACT = {The increasing penetration of renewable energy sources such as wind power, characterized by volatility and randomness, poses significant challenges to the secure and stable operation of the power system. Demand response is an effective means to address this issue. However, existing research lacks effective methods to accurately quantify the response potential of practical multi-type industrial loads that possess greater response potential. Firstly, this study analyses the production modes and load adjustment methods of three typical industrial loads, establishing a two-way mapping model between their response behaviours and the decision-making behaviours of the dispatching center. Secondly, for the Stackelberg game scenario in the peak-shaving market, a game-theoretic framework is constructed based on the assumption of participant rationality. Finally, by solving the Stackelberg equilibrium, the incentive price that maximizes response is inversely derived, enabling the advance quantification of demand response potential. Simulation results demonstrate that this model can, through dynamic adjustment of the incentive price, identify the optimal price point that maximizes industrial load demand response while preserving the revenue of the dispatching center as much as possible, facilitating the synergistic optimization and achieving a Pareto optimum for both parties. This study presents a novel solution for quantifying the response potential of practical multi-type industrial loads, thereby enabling the synergistic optimization between the distribution system and industrial participants.},
DOI = {10.32604/ee.2026.085090}
}