Using multi-objective sparrow search algorithm to establish active distribution network dynamic reconfiguration integrated optimization

This study contributes to establish the dynamic reconfiguration integrated optimization model of active distribution network (ADN) and proposes a novel solving approach based on multi-objective sparrow search algorithm. Distributed generation and time-varying load have an important impact on promoting sustainable development and reducing energy loss. Therefore, this study aims to investigate the ADN integrated optimization problem in consideration of distributed generation and time-varying load to improve the ADN power quality, economic and energy benefits. First, a multi-objective sparrow search algorithm is proposed aiming at the multiobjective, multi-constraint, non-linear and high-dimensional ADN integrated optimization problem, and the superiority of the proposed algorithm is verified. Second, the mathematical model of ADN integrated optimization is constructed. Finally, multi-scenario test is conducted in the classic test system to verify the effectiveness of proposed method, and the compromise solution is determined through the technique for order of preference by similarity to ideal solution (TOPSIS). The result shows that the proposed method effectively reduces the power loss and node voltage deviation by 75.76% and 70.06%. Accordingly, this study is significance for improving the operational stability of ADN, increasing the penetration rate of renewable energy and promoting economic production.

Automated summary

This study establishes a dynamic reconfiguration integrated optimization model for active distribution network (ADN) and proposes a novel solving approach using multi-objective sparrow search algorithm. By considering distributed generation and time-varying load, the study aims to improve the power quality, economic benefits, and energy benefits of ADN. Experimental results show that the proposed method effectively reduces power loss and node voltage deviation.