随着5G技术的全面部署,基站能耗问题日益凸显,逐渐兴起的基站微电网系统因其清洁能源供电特性逐渐成为研究热点。然而,基站微电网的能源供应具有不确定性,需应对复杂的边缘计算任务,使得基站微电网能量与计算资源的协同优化成为亟待研究的关键问题。为此,文章提出一种基于联邦竞争深度Q网络(federated learning dueling deep Q-network
Yang Yanhong,Ma Tengfei,Li Haitao,Liu Yiran,Tang Chenghong,Pei Wei.Federated double DQN based multi-energy microgrid energy management strategy considering carbon emissions[J].Global Energy Interconnection,2023.
Li Tong,Yu Li,Ma Yibo,Duan Tong,Huang Wenzhen,Zhou Yan,Jin Depeng,Li Yong,Jiang Tao.Carbon emissions of 5G mobile networks in China[J].Nature Sustainability,2023.
Meng Fanxi,Zhang Quan,Lin Yaolin,Zou Sikai,Fu Jiyao,Liu Baochang,Wang Wei,Ma Xiaowei,Du Sheng.Field study on the performance of a thermosyphon and mechanical refrigeration hybrid cooling system in a 5G telecommunication base station[J].Energy,2022.
Nakabi Taha Abdelhalim,Toivanen Pekka.Deep reinforcement learning for energy management in a microgrid with flexible demand[J].Sustainable Energy, Grids and Networks,2021.
Sharma Deepti,Singhal Sanyam,Rai Amrita,Singh Amandeep.Analysis of power consumption in standalone 5G network and enhancement in energy efficiency using a novel routing protocol[J].Sustainable Energy, Grids and Networks,2021.
Tushar Wayes,Saha Tapan K.,Yuen Chau,Smith David,Poor H. Vincent.Peer-to-Peer Trading in Electricity Networks: An Overview[J].IEEE Transactions on Smart Grid,2020.