For electricity–gas–heat multiple energy systems, this paper proposes a hybrid timescale coordinated scheduling model for the distribution network, serving as a capacity enhancement strategy to improve system flexibility and the hosting capability for distributed renewable energy. Considering user preferences and comfort levels, an integrated load demand response mechanism is introduced across electricity, gas, and heat sectors to fully exploit the scheduling potential of various demand-side resources. Taking into account the physical flow constraints of the multi-energy distribution network, a unified day-ahead scheduling and intraday bilevel optimization strategy is established. In the day-ahead stage, the objective is to minimize system operating and energy interaction costs, while in the intraday stage, a bilevel optimization model is formulated based on the distinct transmission characteristics of electricity, gas, and heat flows to refine the scheduling plan. To address the nonlinear and nonconvex constraints in the model, second-order cone relaxation and incremental piecewise linearization methods are employed, ensuring computational feasibility and global optimality. A case study on an improved IEEE 33-node multi-energy distribution network demonstrates that, compared with the scenario without demand response, incorporating integrated electricity–gas–heat demand response reduces the total operating cost by more than 1.6% under different renewable penetration levels. The proposed approach achieves flexible load adjustment, peak shaving, and valley filling through time-of-use pricing, significantly enhancing system economy and operational stability. The results verify the accuracy, efficiency, and engineering feasibility of the proposed method for multi-energy system optimization, scheduling, and planning. • This paper proposes a hybrid timescale coordinated scheduling model for the distribution network, serving as a capacity enhancement strategy to improve system flexibility and the hosting capability for distributed renewable energy. • A unified day-ahead scheduling and intraday bilevel optimization strategy is established. In the day-ahead stage, the objective is to minimize system operating and energy interaction costs, while in the intraday stage, a bilevel optimization model is formulated based on the distinct transmission characteristics of electricity, gas, and heat flows to refine the scheduling plan. • To address the nonlinear and nonconvex constraints in the model, second-order cone relaxation and incremental piecewise linearization methods are employed, ensuring computational feasibility and global optimality. A case study on an improved IEEE 33-node multi-energy distribution network demonstrates that, compared with the scenario without demand response, incorporating integrated electricity–gas–heat demand response reduces the total operating cost by more than 1.6% under different renewable penetration levels.
Ye et al. (Tue,) studied this question.