Adaptive distributed cloud edge collaborative load control strategy for load management

LI Siwei; JIN Li; YU Long; DU Lishi; YUE Liang; ZHANG Xirun

doi:10.11959/j.issn.1000-0801.2024192

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Adaptive distributed cloud edge collaborative load control strategy for load management

Research and Development | 更新时间：2024-09-23

- Adaptive distributed cloud edge collaborative load control strategy for load management
- Telecommunications Science Vol. 40, Issue 8, Pages: 52-62(2024)
- 作者机构：
  
  1.天津大学智能电网教育部重点实验室，天津 300072
  2.北京中电飞华通信有限公司，北京 100071
- 作者简介：
- 基金信息：
  
  Science and Technology Project of State Grid Corporation of China(5400-202320223A-1-1-ZN)
- DOI：10.11959/j.issn.1000-0801.2024192
  CLC： TM734
- Received：28 February 2024，
  
  Revised：2024-07-19，
  
  Published：20 August 2024
- 稿件说明：
移动端阅览
李思维,靳莉,于龙等.面向多元可控负荷调控的云边协同负荷资源分配策略[J].电信科学,2024,40(08):52-62.

LI Siwei,JIN Li,YU Long,et al.Adaptive distributed cloud edge collaborative load control strategy for load management[J].Telecommunications Science,2024,40(08):52-62.
李思维,靳莉,于龙等.面向多元可控负荷调控的云边协同负荷资源分配策略[J].电信科学,2024,40(08):52-62. DOI： 10.11959/j.issn.1000-0801.2024192.

LI Siwei,JIN Li,YU Long,et al.Adaptive distributed cloud edge collaborative load control strategy for load management[J].Telecommunications Science,2024,40(08):52-62. DOI： 10.11959/j.issn.1000-0801.2024192.

摘要

针对多元可控负荷资源进行可控负荷管理时需要占用大量计算资源，且无法实现自动功率精准控制的问题，提出了一种面向多元可控负荷调控的云边协同负荷资源分配策略。首先，设计了云边协同调控架构，整合处理各种多元可控负荷资源数据；其次，考虑不同边缘节点计算任务的相似度，以所有计算任务的时间开销最小为优化目标，给出云端计算资源分配策略，合理分配计算资源；最后，通过基于自适应交叉—变异概率的遗传算法进行计算资源分配的求解。实验结果表明，所提算法在任务完成时间和执行成本上具有较为明显的优势，并且任务数量越多，计算资源越小时优势越明显，可以显著提升计算效率，降低计算耗时。

Abstract

To solve the problem that the controllable load management of multiple controllable load resources requires a lot of computing resources and can not achieve accurate automatic power control

a cloud-edge cooperative load resource allocation strategy for multiple controllable load regulation was proposed. Firstly

the collaborative control framework of cloud edge was designed to integrate and process the data of various controllable load resources. Secondly

considering the similarity of computing tasks of different edge nodes

the optimization goal was to minimize the time cost of all computing tasks

and the cloud computing resource allocation strategy was given to allocate computing resources reasonably. Finally

the computational resource allocation was solved by genetic algorithm based on adaptive cross-mutation probability. Finally

the calculation of resource allocation was solved using a genetic algorithm based on adaptive crossover mutation probability. The experimental results show that the algorithm proposed has significant advantages in task completion time and execution cost

and these advantages become more pronounced as the number of tasks increases and computing resources decrease. It can significantly improve computing efficiency and reduce computing time.

关键词

Keywords

references

周颖 , 龚桃荣 , 陈宋宋 , 等 . 面向新型电力负荷管理的分层分区动态调控架构展望 [J ] . 电力信息与通信技术 , 2023 , 21 ( 4 ): 51 - 58 .

ZHOU Y , GONG T R , CHEN S S , et al . Prospect of hierarchical and partitioned dynamic regulation architecture for new power load management [J ] . Electric Power Information and Communication Technology , 2023 , 21 ( 4 ): 51 - 58 .

国家发展改革委 , 国家能源局 . 电力负荷管理办法(2023年版) [J ] . 电力需求侧管理 , 2023 , 25 ( 6 ): 122 - 125 .

National Development and Reform Commission , National Energy Administration . Measures for power load management (2023 edition) [J ] . Power Demand Side Management , 2023 , 25 ( 6 ): 122 - 125 .

WANG W , YU T , HUANG Y P , et al . The situation and suggestions of the new energy power system under the background of carbon reduction in China [J ] . Energy Reports , 2021 ( 7 ): 1477 - 1484 .

程林 , 万宇翔 , 张放 , 等 . 基于负荷聚合商业务的空调服务运作模式及控制策略 [J ] . 电力系统自动化 , 2018 , 42 ( 18 ): 8 - 16 .

CHENG L , WAN Y X , ZHANG F , et al . Operation mode and control strategy for air-conditioning service based on business of load aggregator [J ] . Automation of Electric Power Systems , 2018 , 42 ( 18 ): 8 - 16 .

侯慧 , 徐焘 , 肖振锋 , 等 . 计及可调控负荷的发用电一体化综合优化调度 [J ] . 电网技术 , 2020 , 44 ( 11 ): 4294 - 4301 .

HOU H , XU T , XIAO Z F , et al . Generation and load integrated optimal scheduling considering adjustable load [J ] . Power System Technology , 2020 , 44 ( 11 ): 4294 - 4301 .

张春芳 . 面向智能制造的边缘计算网络资源分配方法 [D ] . 大连 : 大连海事大学 , 2022 .

ZHANG C F . Resource allocation method of edge computing networks for intelligent manufacturing [D ] . Dalian : Dalian Maritime University , 2022 .

刘建明 , 林弘宇 , 陶雄强 . 物联网与能源互联网 [M ] . 北京 : 电子工业出版社 , 2021 .

LIU J M , LIN H Y , TAO X Q . Internet of things and energy Internet [M ] . Beijing ： Publishing House of Electronics Industry , 2021 .

CHEN L J , ZHU X , CAI J L , et al . Multi-time scale coordinated optimal dispatch of microgrid cluster based on MAS [J ] . Electric Power Systems Research , 2019 , 177 : 105976 .

CHANG H H , CHEN H , ZHANG J Z , et al . Decentralized deep reinforcement learning meets mobility load balancing [J ] . IEEE/ACM Transactions on Networking , 2023 , 31 ( 2 ): 473 - 484 .

程钎 , 陈羽 , 孙伶雁 . 考虑服务配置的细粒度电力任务云边协同优化调度策略 [J ] . 电力系统保护与控制 , 2023 , 51 ( 7 ): 53 - 62 .

CHENG Q , CHEN Y , SUN L Y . Cloud-edge collaborative optimization scheduling strategy for fine-grained power tasks considering service configuration [J ] . Power System Protection and Control , 2023 , 51 ( 7 ): 53 - 62 .

黄杰 , 肖志清 , 毛冬 . 面向电力物联网的云边数据协同方法 [J ] . 电力信息与通信技术 , 2022 , 20 ( 1 ): 35 - 42 .

HUANG J , XIAO Z Q , MAO D . Cloud-edge data collaboration method for power IoTs [J ] . Electric Power Information and Communication Technology , 2022 , 20 ( 1 ): 35 - 42 .

孔祥玉 , 马玉莹 , 艾芊 , 等 . 新型电力系统多元用户的用电特征建模与用电负荷预测综述 [J ] . 电力系统自动化 , 2023 , 47 ( 13 ): 2 - 17 .

KONG X Y , MA Y Y , AI Q , et al . Review on electricity consumption characteristic modeling and load forecasting for diverse users in new power system [J ] . Automation of Electric Power Systems , 2023 , 47 ( 13 ): 2 - 17 .

陈兆雁 , 柴宜 , 平常 , 等 . 基于5G边缘计算的可调节负荷互动技术研究 [J ] . 华电技术 , 2021 , 43 ( 3 ): 9 - 13 .

CHEN Z Y , CHAI Y , PING C , et al . Research on adjustable load interaction technology based on 5G edge computing [J ] . Huadian Technology , 2021 , 43 ( 3 ): 9 - 13 .

郭芬 . 面向虚拟机的云平台资源部署与调度研究 [D ] . 广州 : 华南理工大学 , 2015 .

GUO F . Research on deploying and scheduling for cloud resources based on virtual machines [D ] . Guangzhou : South China University of Technology , 2015 .

HEIMOVSKI G B , TURCHETTI R C , WICKBOLDT J A , et al . FT-Aurora: a highly available IaaS cloud manager based on replication [J ] . Computer Networks , 2020 ( 168 ): 107041 .

KANAMARU S , KAWAMURA K , TANAKA S , et al . Solving constrained slot placement problems using an Ising machine and its evaluations [J ] . IEICE Transactions on Information and Systems , 2021 , E104.D( 2 ): 226 - 236 .

樊玉琦 , 王铮 , 杨洋 , 等 . 快速构建BIT*搜索树的机器人路径规划 [J ] . 网络安全技术与应用 , 2023 ( 10 ): 38 - 41 .

FAN Y Q , WANG Z , YANG Y , et al . Robot path planning for rapidly constructing BIT* search tree [J ] . Network Security Technology & Application , 2023 ( 10 ): 38 - 41 .

张晓笛 , 王金昌 , 杨仲轩 , 等 . 基于状态空间法的阶梯型变截面水平受荷桩分析方法 [J ] . 岩土工程学报 , 2023 , 45 ( 9 ): 1944 - 1952 .

ZHANG X D , WANG J C , YANG Z X , et al . Analytical solutions for laterally loaded step-tapered piles by state space method [J ] . Chinese Journal of Geotechnical Engineering , 2023 , 45 ( 9 ): 1944 - 1952 .

KONG W B , CHEN J Z , SONG Y B , et al . Sobel edge detection algorithm with adaptive threshold based on improved genetic algorithm for image processing [J ] . International Journal of Advanced Computer Science and Applications , 2023 , 14 ( 2 ): 557 - 562 .

NA R S , LI Q , WU L J . Particle swarm optimization with negative gradient [J ] . Advanced Materials Research , 2012 , 468-471 : 2550 - 2553 .

过羽丰 . 基于改进遗传算法的配电网络规划研究 [D ] . 成都 : 西华大学 , 2012 .

GUO Y F . Distribution network planning based on improved genetic algorithm [D ] . Chengdu : Xihua University , 2012 .

季航旭 , 姜苏 , 赵宇海 , 等 . 分布式环境中的多作业执行调度策略与优化 [J ] . 计算机工程与科学 , 2021 , 43 ( 6 ): 951 - 961 .

JI H X , JIANG S , ZHAO Y H , et al . Scheduling and optimization of multi-job execution in distributed environment [J ] . Computer Engineering & Science , 2021 , 43 ( 6 ): 951 - 961 .

PAN J H , WANG H , YANG X G . A random particle swarm optimization algorithm with application [J ] . Advanced Materials Research , 2013 , 634-638 : 3940 - 3944 .

岑柏霖 . 改进GA-BP算法在电力系统负荷预测模型中的应用研究 [J ] . 自动化与仪器仪表 , 2023 ( 11 ): 53 - 57 .

CEN B L . Application of improved GA-BP algorithm in power system load forecasting model [J ] . Automation & Instrumentation , 2023 ( 11 ): 53 - 57 .

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