Adaptive de Algorithm for Novel Energy Control Framework Based on Edge Computing in IIoT Applications
Author | Xu, Zhengwei |
Author | Han, Guangjie |
Author | Zhu, Hongbo |
Author | Liu, Li |
Author | Guizani, Mohsen |
Available date | 2022-10-31T06:36:44Z |
Publication Date | 2021-07-01 |
Publication Name | IEEE Transactions on Industrial Informatics |
Identifier | http://dx.doi.org/10.1109/TII.2020.3007644 |
Citation | Xu, Z., Han, G., Zhu, H., Liu, L., & Guizani, M. (2020). Adaptive DE algorithm for novel energy control framework based on edge computing in IIoT applications. IEEE Transactions on Industrial Informatics, 17(7), 5118-5127. |
ISSN | 15513203 |
Abstract | With the development of the industrial Internet of Things and the advancements in wireless sensor networking technologies, the smart grid based on edge computing now is regarded as being essential for real-time monitoring and automatic control of the electricity generation and distribution. In this article, we propose a highly efficient energy control framework supported by edge computing to reduce energy waste and increase the benefit for industrial users. To this end, battery energy storage systems (BESSs) are currently being employed to store energy for stability of supply and quality of power. The optimal load patterns and corresponding energy storage capacities of the BESSs can be obtained through the framework, according to the energy market and the historical load data of industrial users. However, computing these requires considering the tradeoff between equipment cost, time-of-use electricity price, running expenses, and other related factors, which would be an NP-hard problem. To address this challenge, we also propose an adaptive mixed differential evolution algorithm with a novel mutation strategy. Experiments on real-world data demonstrate the effectiveness of the proposed algorithm and framework. |
Sponsor | Manuscript received June 22, 2020; accepted July 1, 2020. Date of publication July 7, 2020; date of current version April 2, 2021. This work was supported in part by the National Key Research and Development Program under Grant 2017YFE0125300, in part by the Jiangsu Key Research and Development Program under Grant BE2019648, and in part by the Project of Shenzhen Science and Technology Innovation Committee under Grant JCYJ20190809145407809. Paper no. TII-20-3056. (Corresponding author: Guangjie Han.) Zhengwei Xu, Guangjie Han, and Li Liu are with the Department of Information and Communication System, Hohai University, Changzhou 213022, China (e-mail: xuzhengweihhu@outlook.com; hanguangjie@gmail.com; liulihhuc@gmail.com). |
Language | en |
Publisher | IEEE Computer Society |
Subject | Adaptive mixed differential evolution (AMDE) algorithm battery energy storage systems (BESSs) edge computing energy control framework |
Type | Article |
Pagination | 5118-5127 |
Issue Number | 7 |
Volume Number | 17 |
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