Coordinated Optimal Dispatch of Distribution Networks and Aggregated Customer-Side Flexible Resources

Driven by the dual-carbon goals, the high-proportion integration of distributed renewable energy into distribution networks poses significant challenges to operational flexibility due to the inherent intermittency and uncertainty of renewable sources. While direct control of flexible resources is possible, it often entails high costs and lacks mechanisms to incentivize proactive participation. This paper investigates the flexible optimal operation of distribution networks with the active participation of aggregated user-side flexible resources. A two-layer day-ahead optimization framework is proposed. At the lower layer, user-side flexible resource participants employ a deep learning-based intelligent decision-making model to formulate their clearing strategies rapidly, eliminating the need for detailed physical models and iterative calculations. At the upper layer, the distribution network operator (DNO) establishes a multi-objective optimization model that simultaneously minimizes comprehensive operational costs and the net load fluctuation rate to enhance flexibility. The model coordinates distributed generation, energy storage, and user-side resources via a time-of-use pricing mechanism. The fast non-dominated sorting genetic algorithm (NSGA-II) is adopted to obtain the Pareto-optimal set, from which the optimal solution is selected using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Case studies on a modified IEEE 33-bus distribution system demonstrate that the proposed method effectively guides the demand response of user-side resources. The results confirm significant improvements in the economic operation of the distribution network, along with enhanced flexibility evidenced by increased net load adequacy and a reduced net load fluctuation rate, thereby improving the system’s accommodation capability for renewable energy.