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

Li, L.-L., Xiong, J.-L., Tseng, M.-L., Yan, Z. and Lim, M. K. (2022) Using multi-objective sparrow search algorithm to establish active distribution network dynamic reconfiguration integrated optimization. Expert Systems with Applications, 193, 116445. (doi: 10.1016/j.eswa.2021.116445)

Abstract

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 multi-objective, 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.

• Item Type: Articles
• Additional Information: This work was supported by the key project of Tianjin Natural Science Foundation [Project No. 19JCZDJC32100] and the Natural Science Foundation of Hebei Province of China [Project No. E2018202282].
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lim, Professor Ming
• Authors: Li, L.-L., Xiong, J.-L., Tseng, M.-L., Yan, Z., and Lim, M. K.
• College/School: College of Social Sciences > Adam Smith Business School > Management
• Journal Name: Expert Systems with Applications
• Publisher: Elsevier
• ISSN: 0957-4174
• ISSN (Online): 1873-6793
• Published Online: 03 January 2022
• Copyright Holders: Copyright © 2021 Elsevier Ltd
• First Published: First published in Expert Systems with Applications 193: 116445
• Publisher Policy: Reproduced in accordance with the publisher copyright policy