Title: Energy consumption optimisation based on mobile edge computing in power grid internet of things nodes

Authors: Hongbin Sun; Mingjun Liu; Zhejun Qing; Xiaofeng Li; Lixue Li

Addresses: School of Electrical Engineering, Changchun Institute of Technology, Jilin Changchun, China ' Baishan Power Supply Company, State Grid Jilin Electric Power Company Limited, Jilin Changchun, China ' Baishan Power Supply Company, State Grid Jilin Electric Power Company Limited, Jilin Changchun, China ' Baishan Power Supply Company, State Grid Jilin Electric Power Company Limited, Jilin Changchun, China ' School of Electrical Engineering, Shanghai Jiaotong University, Shanghai, China

Abstract: With deployment and application of the power grid internet of things (IoT), all the nodes need more and more resources which bring a great challenge to the power grid system. Mobile edge computing (MEC) and rational use of renewable energy in the grid are effective way to solve above problem of grid resource allocation. In this paper, we propose a MEC framework and an offloading policy that considers renewable energy. The model fully considers the stochasticity of renewable energy arrival randomness and task offloading. By analysing the renewable energy's dedicate character and adopt alternating direction method of multipliers (ADMM) algorithm, the method introduce a task computing offloading method based on renewable energy is proposed. The algorithm realises the optimisation strategy of the power grid internet node delay and grid energy consumption minimisation. The simulation results show that the proposed algorithm performance exceeds the two baseline computation and offloading strategies, which can effectively optimise the grid energy consumption of the nodes.

Keywords: internet of things; IoT; power grid; renewable energy; optimisation strategy; mobile edge computing; MEC.

DOI: 10.1504/IJWGS.2020.109468

International Journal of Web and Grid Services, 2020 Vol.16 No.3, pp.238 - 253

Received: 09 Dec 2019
Accepted: 12 Feb 2020
Published online: 09 Sep 2020 *

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