# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 6 Conclusions
These results confirm the robustness and effectiveness of the proposed dynamic reconfiguration framework across different distribution system sizes and operating conditions. The results show that the improved EO algorithm and the proposed feasible solution judgment model have significantly better multi-objective optimization performance than traditional methods, and effectively improve the feasibility rate of the solution. The time division method improves the adaptability and accuracy of reconstruction scheduling while maintaining the integrity of load temporal characteristics. Overall, the proposed method demonstrates good comprehensive performance in saving system active power losses, improving power supply reliability, and ensuring DG consumption. The proposed method demonstrates strong adaptability, fast convergence, and superior performance across multiple test systems. However, several limitations remain. First, the effectiveness of the method relies on the accuracy of load and DG forecasting, which can be affected by measurement errors or sudden fluctuations. Second, only technical indicators such as power loss and voltage deviation are considered in the optimization objectives, while economic cost and environmental impacts (e.g., CO2 emissions) are not yet included. Furthermore, while this study considers time-varying DG output in a segmented manner, it does not yet explicitly model fast fluctuations, such as those induced by cloud transients in PV-rich areas. To improve robustness in such scenarios, future extensions of the model may incorporate high-resolution uncertainty modeling and stochastic scenario generation techniques [46; 47; 48; 49], enabling adaptive reconfiguration under rapidly changing operating conditions. Nevertheless, the reduction in power losses and the enhanced integration of distributed generation suggest that the proposed method may contribute to lowering operational energy costs and carbon emissions. Although these benefits are not explicitly modeled in this study, they highlight the practical value of the framework in promoting energy efficiency and environmental sustainability. In future work, we plan to extend the proposed framework to larger and more complex distribution networks, then address these limitations by incorporating more realistic system constraints and uncertainties into the modeling process. Furthermore, we aim to extend the proposed framework by introducing multi-objective optimization that balances technical, economic, and environmental performance, and to validate its applicability on real-world distribution networks. **Author Contributions:** Conceptualization, C.W. ; methodology, C.W. and Y.Z. ; validation, Y.Z. ; investigation, Y.Z. ; data curation, Y.Z. ; writing--original draft preparation, Y.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the National Natural Science Foundation of China (No. 62072363) and the Natural Science Foundation of Shaanxi Province (No. 2019 JM-167). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors. **Conflicts of Interest:** The authors declare no conflicts of interest.