Adaptive DE Algorithm for Novel Energy Control Framework Based on Edge Computing in IIoT Applications

• Published in: IEEE transactions on industrial informatics Vol. 17; no. 7; pp. 5118 - 5127
• Main Authors: Xu, Zhengwei, Han, Guangjie, Zhu, Hongbo, Liu, Li, Guizani, Mohsen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Adaptive control; Adaptive mixed differential evolution (AMDE) algorithm; Algorithms; Automatic control; Batteries; battery energy storage systems (BESSs); Clustering algorithms; Economics; Edge computing; Electric power distribution; Electrical loads; energy control framework; Energy storage; Equipment costs; Evolutionary algorithms; Evolutionary computation; Industrial applications; Industrial Internet of Things; Load modeling; Mutation; Optimization; Smart grid; Storage systems; Time of use electricity pricing
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2020.3007644

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.