面向多元可控负荷调控的云边协同负荷资源分配策略

李思维; 靳莉; 于龙; 杜立石; 岳靓; 张喜润

doi:10.11959/j.issn.1000-0801.2024192

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面向多元可控负荷调控的云边协同负荷资源分配策略

研究与开发 | 更新时间：2024-09-23

- 面向多元可控负荷调控的云边协同负荷资源分配策略
- Adaptive distributed cloud edge collaborative load control strategy for load management
- 电信科学 2024年40卷第8期页码：52-62
- 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：
  
  [ "李思维（1988- ），男，天津大学智能电网教育部重点实验室博士生，北京中电飞华通信有限公司高级工程师，主要从事电力系统需求响应和优化运行与控制工作。" ]
  [ "靳莉（1981- ），女，北京中电飞华通信有限公司工程师，主要从事电力系统需求响应和优化运行与控制工作。" ]
  [ "于龙（1989- ），男，北京中电飞华通信有限公司工程师，主要从事电力系统需求响应和优化运行与控制工作。" ]
  [ "杜立石（1985- ），男，北京中电飞华通信有限公司工程师，主要从事电力系统负荷管理和电力系统优化运行与控制工作。" ]
  [ "岳靓（1995- ），女，北京中电飞华通信有限公司工程师，主要从事电力系统需求响应和优化运行与控制工作。" ]
  [ "张喜润（1981- ），男，北京中电飞华通信有限公司工程师，主要从事电力信息通信和电力负荷管理及优化调控工作。" ]
- 基金信息：
  
  国家电网公司总部科技项目(5400-202320223A-1-1-ZN)
- DOI：10.11959/j.issn.1000-0801.2024192
  中图分类号： TM734
- 收稿：2024-02-28，
  
  修回：2024-07-19，
  
  纸质出版：2024-08-20
- 稿件说明：
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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

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