Day-Ahead and Intra-Day Two-Stage Coordinated Optimal Scheduling for Active Distribution Networks with Cluster Integration of Thermal Storage Electric Heating Systems

Thermal storage electric heating, as a common flexible load resource, holds significant potential for reducing system operating costs and promoting the integration of renewable energy. To address issues such as the volatility of distributed power output, load forecasting errors, and the temporal differences in the operational characteristics of various dispatchable resources in active distribution networks (ADNs), this paper proposes a two-stage coordinated optimal dispatch strategy for ADNs that incorporates clustered thermal-storage electric heating systems. Firstly, based on the electro-thermal conversion and thermal storage characteristics of thermal-storage electric heating, a model is established for the thermal demand and load characteristics of electric heating users' buildings. Secondly, a two-stage optimization strategy encompassing day-ahead and intra-day scheduling is developed. In the day-ahead scheduling stage, an optimization model is constructed with the objective of minimizing the comprehensive operating costs of the ADN. In the intra-day scheduling stage, a rolling optimization method is employed to adjust the day-ahead schedule by considering both system economy and the deviation costs of unit outputs, while coordinating fast- and slow-response resources across different time scales. Finally, the proposed strategy is validated using a modified IEEE 33-node test system. Case study results demonstrate that the proposed strategy effectively balances user-side comfort and grid-side economic benefits, achieving coordinated optimization across multiple time scales and enhancing system stability and operational efficiency.