ABSTRACT Incentive‐based integrated demand response (IBIDR) has been recognised as a powerful tool to mitigate supply–demand imbalance in energy systems. However, the temporal dynamics of user participation rates and the stochastic fluctuation of users' response power have been central challenges for multi‐energy service providers (MESPs) in designing effective incentive strategies. To address these challenges, this paper proposes an adaptive selection strategy for IBIDR considering the dynamic participation rate and stochastic response deviation. First, users' dynamic participation rate, simultaneously influenced by both imitative behaviour and spontaneous behaviour, is modelled by a Markov process, which improves the user model. Second, an adaptive selection mechanism is proposed based on users' historical response power deviation levels and participation rates, which improves the MESP model. Moreover, the improved user model and the enhanced MESP model are jointly expressed as a bi‐level stochastic programming problem. Utilising the Karush–Kuhn–Tucker (KKT) conditions and probability theory, the original bi‐level stochastic problem is reformulated into a tractable single‐level deterministic optimisation model. Finally, simulation results demonstrate that the proposed model effectively improves the accuracy of user response behaviour prediction and the efficiency of MESP's incentive strategies, thereby reducing MESP's total cost, mitigating response power deviations, and enhancing the utilities for users participating in IBIDR.
Ding et al. (Thu,) studied this question.