Providing Robust and Low-Cost Edge Computing in Smart Grid:An Energy Harvesting Based Task Scheduling and Resource Management Framework  

作　　者：Xie Zhigang Song Xin Xu Siyang Cao Jing 

机构地区：[1]School of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,China [2]Engineering Optimization and Smart Antenna Research Institute,Northeastern University,Qinhuangdao 066004,China [3]College of Computer Science and Engineering,Northeastern University,Shenyang 110819,China [4]School of Mathematics and Information Science&Technology,Hebei Normal University of Science and Technology,Qinhuangdao 066004,China

出　　处：《China Communications》2025年第2期226-240,共15页中国通信(英文版)

基　　金：supported in part by the National Natural Science Foundation of China under Grant No.61473066;in part by the Natural Science Foundation of Hebei Province under Grant No.F2021501020;in part by the S&T Program of Qinhuangdao under Grant No.202401A195;in part by the Science Research Project of Hebei Education Department under Grant No.QN2025008;in part by the Innovation Capability Improvement Plan Project of Hebei Province under Grant No.22567637H

摘　　要：Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.

关 键 词：edge computing energy harvesting energy storage unit renewable energy sampling average approximation task scheduling 

分 类 号：TM76[电气工程—电力系统及自动化]