Open Access

ARTICLE

Data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

Tien-Wen Sung¹, Wei Li¹, Chao-Yang Lee^2,*, Yuzhen Chen¹, Qingjun Fang¹

1 Fujian Provincial Key Laboratory of Big Data Mining and Applications, College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, 350118, China
2 Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, 640301, Taiwan

* Corresponding Author: Chao-Yang Lee. Email:

(This article belongs to the Special Issue: Heuristic Algorithms for Optimizing Network Technologies: Innovations and Applications)

Computers, Materials & Continua 2025, 83(1), 407-434. https://doi.org/10.32604/cmc.2025.061694

Received 30 November 2024; Accepted 17 February 2025; Issue published 26 March 2025

Abstract

To transmit customer power data collected by smart meters (SMs) to utility companies, data must first be transmitted to the corresponding data aggregation point (DAP) of the SM. The number of DAPs installed and the installation location greatly impact the whole network. For the traditional DAP placement algorithm, the number of DAPs must be set in advance, but determining the best number of DAPs is difficult, which undoubtedly reduces the overall performance of the network. Moreover, the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the network. To address the above problems, this paper proposes a DAP placement algorithm, APSSA, based on the improved affinity propagation (AP) algorithm and sparrow search (SSA) algorithm, which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different environments. The algorithm adds an allocation mechanism to optimize the subnetwork in the SSA. APSSA is evaluated under three different areas and compared with other DAP placement algorithms. The experimental results validated that the method in this paper can reduce the network cost, shorten the average transmission distance, and reduce the load gap.

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Keywords

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