浏览全部资源
扫码关注微信
[ "余竞航(1995- ),男,国网江苏省电力有限公司信息通信分公司工程师,主要研究方向为电力信息技术" ]
[ "赵一辰(1996- ),男,现就职于国网江苏省电力有限公司信息通信分公司,主要研究方向为电力信息技术" ]
[ "宋浒(1986- ),男,博士,国网江苏省电力有限公司信息通信分公司高级工程师,主要研究方向为电力信息技术" ]
网络出版日期:2024-01,
纸质出版日期:2024-01-20
移动端阅览
余竞航, 赵一辰, 宋浒. 基于强化学习的边缘计算智能电网资源调度算法[J]. 电信科学, 2024,40(1):115-122.
Jinghang YU, Yichen ZHAO, Hu SONG. Edge computing smart grid resource scheduling algorithm based on reinforcement learning[J]. Telecommunications science, 2024, 40(1): 115-122.
余竞航, 赵一辰, 宋浒. 基于强化学习的边缘计算智能电网资源调度算法[J]. 电信科学, 2024,40(1):115-122. DOI: 10.11959/j.issn.1000-0801.2024005.
Jinghang YU, Yichen ZHAO, Hu SONG. Edge computing smart grid resource scheduling algorithm based on reinforcement learning[J]. Telecommunications science, 2024, 40(1): 115-122. DOI: 10.11959/j.issn.1000-0801.2024005.
智能电网是一种能够进行智能管理和优化的电力网络。网络虚拟化技术可以有效提高智能电网的资源利用率和可靠性,从而满足不同用户的差异化需求。在资源有限的情况下,传统的虚拟网络嵌入算法无法根据电力系统的资源使用情况和用户需求来动态调整虚拟资源的分配和映射。为解决这一问题,将边缘计算和虚拟化技术相结合,引入了一种基于强化学习的虚拟网络资源调度算法。仿真结果表明,该虚拟网络资源调度算法在提高电网的可靠性和资源利用率方面优于其他3种调度算法。
A smart grid is a power network capable of intelligent management and optimization.Network virtualization technology can effectively improve the resource utilization and reliability of smart grids and meet the differentiated needs of different users.In the case of limited resources
traditional virtual network embedding algorithms cannot dynamically adjust the allocation and mapping of virtual resources according to the resource usage and user needs of the power system.To solve this problem
edge computing and virtualization technology was combined and a virtual network resource scheduling algorithm based on reinforcement learning was introduced.The simulation results show that the proposed virtual resource scheduling algorithm is better than the other three scheduling algorithms in improving power grid reliability and resource utilization.
ZAFAR A , CHE Y B , AHMED M , et al . Enhancing power generation forecasting in smart grids using hybrid autoencoder long short-term memory machine learning model [J ] . IEEE Access , 2023 ( 11 ): 118521 - 118537 .
BONDOK A H , MAHMOUD M , BADR M M , et al . Novel evasion attacks against adversarial training defense for smart grid federated learning [J ] . IEEE Access , 2023 ( 11 ): 112953 - 112972 .
ALACA O , EKTI A R , WILSON A , et al . Detection of grid-signal distortions using the spectral correlation function [J ] . IEEE Transactions on Smart Grid , 2023 , 14 ( 6 ): 4980 - 4983 .
SIKRI A , SELIM B , KADDOUM G , et al . RIS-aided wireless sensor network in the presence of impulsive noise and interferers for smart-grid communications [J ] . IEEE Communications Letters , 2023 , 27 ( 9 ): 2501 - 2505 .
FRANCES A , RAMIREZ D , UCEDA J . Blackbox small-signal modeling of grid-connected inverters in asymmetrical power grids [J ] . IEEE Transactions on Power Electronics , 2023 , 38 ( 10 ): 13064 - 13073 .
HOSSEINZADEHTAHER M , ZARE A , KHAN A , et al . AI-based technique to enhance transient response and resiliency of power electronic dominated grids via grid-following inverters [J ] . IEEE Transactions on Industrial Electronics , 2023 , 71 ( 3 ): 2614 - 2625 .
ZHANG H X , YANG Y J , SHANG B D , et al . Joint resource allocation and multi-part collaborative task offloading in MEC systems [J ] . IEEE Transactions on Vehicular Technology , 2022 , 71 ( 8 ): 8877 - 8890 .
ZHANG P Y , ZHANG Y , KUMAR N , et al . Dynamic SFC embedding algorithm assisted by federated learning in space-air-ground integrated network resource allocation scenario [J ] . IEEE Internet of Things Journal , 2023 , 10 ( 11 ): 9308 - 9318 .
WANG C , LIU L , JIANG C X , et al . Incorporating distributed DRL into storage resource optimization of space-air-ground integrated wireless communication network [J ] . IEEE Journal of Selected Topics in Signal Processing , 2021 , 16 ( 3 ): 434 - 446 .
刘峻朋 , 夏玮玮 , 刘晗 , 等 . 面向电力业务质量保障的 NR-U与Wi-Fi频谱共享 [J ] . 电信科学 , 2023 , 39 ( 7 ): 11 - 22 .
LIU J P , XIA W W , LIU H , et al . NR-U and Wi-Fi spectrum sharing for quality guaranteeing of power services [J ] . Telecommunications Science , 2023 , 39 ( 07 ): 11 - 22 .
郭健 . 基于 SDN 架构的云网融合技术研究与实践 [J ] . 电信工程技术与标准化 , 2022 , 35 ( 3 ): 27 - 32 .
GUO J . Research and practice of cloud network integration technology based on SDN architecture [J ] . Telecom Engineering Technics Standardization , 2022 , 35 ( 3 ): 27 - 32 .
李洵 , 廖臣 , 杨箴 , 等 . 基于云计算的电网虚拟化调度系统研究 [J ] . 电子设计工程 , 2019 , 27 ( 12 ): 138 - 141 , 146 .
LI X , LIAO C , YANG Z , et al . Research on power grid virtualization scheduling system based on cloud computing [J ] . Electronic Design Engineering , 2019 , 27 ( 12 ): 138 - 141 , 146 .
许广路 . 物联网技术在智能电网中的应用 [J ] . 科技创新与应用 , 2022 , 12 ( 5 ): 182 - 184 .
XU G L . Application of internet of things technology in smart grid [J ] . Technology Innovation and Application , 2022 , 12 ( 5 ): 182 - 184 .
刘明月 , 涂崎 , 汪洋 , 等 . 移动云计算卸载技术研究现状及其在电网中的应用 [J ] . 电力信息与通信技术 , 2021 , 19 ( 1 ): 49 - 56 .
LIU M Y , TU Q , WANG Y , et al . Research on mobile cloud computation offloading technology and its application in power grid [J ] . Electric Power Information and Communication Technology , 2021 , 19 ( 1 ): 49 - 56 .
DOU H E , XU Z C , JIANG X , et al . Mobile edge computing based task offloading and resource allocation in smart grid [C ] // Proceedings of the 2021 13th International Conference on Wireless Communications and Signal Processing (WCSP) . Piscataway:IEEE Press , 2021 : 1 - 5 .
CAO H T , HU H , QU Z C , et al . Heuristic solutions of virtual network embedding:a survey [J ] . China Communications , 2018 , 15 ( 3 ): 186 - 219 .
MELO M , SARGENTO S , KILLAT U , et al . Optimal virtual network embedding:energy aware formulation [J ] . Computer Networks , 2015 ( 91 ): 184 - 195 .
SHANBHAG S , KANDOOR A R , WANG C , et al . VHub:single-stage virtual network map through hub location [J ] . Computer Networks , 2015 ( 77 ): 169 - 180 .
HAERI S , TRAJKOVIRć L . Virtual network embedding via Monte Carlo tree search [J ] . IEEE Transactions on Cybernetics , 2017 , 48 ( 2 ): 510 - 521 .
DOLATI M , HASSANPOUR S B , GHADERI M , et al . DeepViNE:Virtual network embedding with deep reinforcement learning [C ] // Proceedings of the IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) . Piscataway:IEEE Press , 2019 : 879 - 885 .
ZHANG P Y , YAO H P , LIU Y J . Virtual network embedding based on computing,network,and storage resource constraints [J ] . IEEE Internet of Things Journal , 2017 , 5 ( 5 ): 3298 - 3304 .
YU M L , YI Y , REXFORD J , et al . Rethinking virtual network embedding:Substrate support for path splitting and migration [J ] . ACM SIGCOMM Computer Communication Review , 2008 , 38 ( 2 ): 17 - 29 .
0
浏览量
233
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构