Improved adaptive gray wolf genetic algorithm for photovoltaic intelligent edge terminal optimal configuration. (October 2021)

• Record Type: Journal Article
• Authors: Ge, Leijiao; Liu, Jiaheng; Wang, Bo; Zhou, Yue; Yan, Jun; Wang, Ming
• Is Part Of: Computers & electrical engineering. Volume 95(2021)
• Publication Date: 2021-10
• Subjects: Distributed photovoltaic -- Photovoltaic intelligent edge terminal -- Optimal configuration -- Improved adaptive genetic algorithm; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107394
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Photovoltaic (PV) intelligent edge terminals (IETs) integrate data acquisition, processing, storage and upload functions for intelligent operations of PV power stations. However, the cost of installing a PV IET at one PV station is relatively high. In order to achieve the goal of multiple distributed PV stations sharing one PV IET on the premise of ensuring reliability, the paper proposes a method for the optimal configuration of PV IETs. First of all, considering the economy and reliability of optimizing configuration of PV IET, a two-layer optimization model is established. After that, to solve the nonlinearity of the proposed model, an improved adaptive genetic algorithm and gray wolf optimization (IAGA-GWO) is proposed. Finally, through two application cases of PV IETs, it is proved that the optimized configuration method in this paper can reduce the cost under the premise of ensuring the reliability.