Review of Demand Response-based Optimal Scheduling of Electric and Thermal Integrated Energy Systems

With the changes in energy demand and structure, the integrated energy system faces a marked decline in flexibility adjustment capacity while meeting customer demand and securing energy supply. Demand response serves as an essential approach for the demand side to engage in grid stability coordination, improving the flexibility of the integrated energy system and compensating for the lack of system flexibility through demand-side synergistic optimization of the coupled and complementary forms of multi-energy. This study provides an overview of the current research status, classification of scheduling models, and model solution methods for demand response-based scheduling of electric and thermal integrated energy systems in recent years. Firstly, the current research status of demand response mechanisms at the domestic and international levels is analyzed. Based on the different classification criteria of current demand response mechanisms, demand response mechanisms are classified into two categories: based on the guiding method and the evaluation method of the user's contribution to the system. They are divided into tariff-type and incentive-type demand responses based on the guiding method and tariff-type non-direct evaluation and baseline and quasi-linear demand responses in direct evaluation. In particular, compared to tariff-based demand response, which is greatly affected by electricity price, incentive-based demand response does not involve the setting of tariffs and is more capable of fully mobilizing many consumers to actively participate. However, due to the current single incentive method of incentive-based demand response, it cannot fully realize its significant regulation potential. Compared to baseline demand response, quasi-linear demand response more effectively raises positive interaction between the source and load sides and facilitates new energy consumption during multi-source coordinated scheduling in the context of large-scale multi-user participation. However, the impact of uncertainty factors such as wind power on load collinearity has not yet been addressed in-depth and requires further study. Secondly, the composition and primary characteristics of the electric-thermal integrated energy system are analyzed. The analysis reveals that the integrated electric and thermal energy system is a power system with close multi-energy coupling. Based on this, the current research status of the three kinds of integrated electric and thermal energy system scheduling models, including the basic, flexibility, and stochastic models, classified based on differences in application scenarios, is elaborated. A comparative analysis of the adaptive scenarios, advantages, and disadvantages of the current demand response-based optimal scheduling models for electric-thermal integrated energy systems is conducted. Current scheduling model solution methods are mainly classified into two types: analytical methods and artificial intelligence methods. Analytical methods are divided into unified and hierarchical solutions based on the scheduling method. Comparative analysis indicates that, compared to the unified solution, the hierarchical solution maintains the independence of each subsystem and achieves a globally optimal solution. However, the repeated iterations required during solving reduce solving efficiency. Artificial intelligence algorithms are primarily divided into methods based on group optimization problems and machine learning algorithms. Although both achieve global optimization, machine learning algorithms demonstrate higher solution rates and robustness compared to methods based on group optimization problems, making them more commonly applied solution algorithms. However, the long offline training time for machine learning algorithms requires further optimization. Finally, the existing problems of demand response mechanisms and their future potential trends are summarized, and an outlook on the participation of demand response in the optimal dispatching of electric and thermal integrated energy systems is provided. This aims to provide a reference for future research on the optimal dispatching of electric and thermal integrated energy systems based on demand response. © 2025 Sichuan University. All rights reserved.