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Integrated Energy Systems (IES) can effectively promote the utilization of renewable energy. Reducing the impact of uncertainties and achieving energy supply-demand balance has always been a key issue in energy scheduling. However, existing research often focuses only on energy prices when designing demand response strategies, neglecting user response characteristics, particularly the influence of environmental temperature and user consumption levels. Therefore, this paper proposes an IES optimization scheduling model that considers user load response characteristics, analyzing changes in user response loads under different environmental and consumption levels. Additionally, consumption levels and environmental parameters are introduced to build an interactive response model. Different incentive measures are designed based on load types to guide users in adjusting their electricity usage behavior, optimizing the supply-demand curve. Furthermore, to address source-load uncertainties, a two-stage robust optimization model is constructed and solved iteratively using the column-and-constraint generation (C&CG) algorithm. The results indicate that considering user response characteristics reduces the peak-to-valley differences in electrical, thermal, and cooling loads by 18.6%, 7.0%, and 13.3% respectively, and reduces total costs by 8.9%. This model can ensure reliable operation while improving the economic efficiency of the system.
Xiong et al. (Sun,) studied this question.