Collaborative Optimization Scheduling Method for Distribution Networks and Microgrids Based on Analytical Target Cascading

The growing penetration of distributed generation has led to an increasing number of microgrids being integrated into distribution networks, which introduces considerable difficulties in reconciling the divergent economic interests of multiple stakeholders while maintaining information privacy. To address these issues, this study proposes a collaborative optimization dispatching model for distribution network–microgrid systems based on Analytical Target Cascading (ATC). A bi-level optimization framework is established, consisting of an upper-level active–reactive power coordination model for the Active Distribution Network (ADN) and a lower-level joint dispatch model for multiple microgrids incorporating load-side demand response. The ATC algorithm is employed to decompose the centralized optimization problem into independent subproblems, enabling iterative coordination between levels while preserving stakeholder privacy. Four scenarios are designed and simulated on an IEEE 33-node system for comparative analysis. The results demonstrate that the proposed method significantly reduces total system operating costs and improves voltage profiles compared with independent ADN operation. The ATC-based distributed solution achieves a cost deviation of only 0.46% from the centralized optimum, while safeguarding proprietary information of all participants. The approach exhibits good convergence and numerical stability, confirming its engineering applicability in multi-stakeholder collaborative scheduling.