面向调控信息新鲜度保障的电力至简物联网资源优化

廖海君; 贾泽晗; 周振宇; 刘念; 王飞; 甘忠; 姚贤炯

doi:10.11959/j.issn.1000-436x.2022124

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面向调控信息新鲜度保障的电力至简物联网资源优化

学术通信 | 更新时间：2024-06-05

- 面向调控信息新鲜度保障的电力至简物联网资源优化
- Dispatching and control information freshness guaranteed resource optimization in simplified power Internet of things
- 通信学报 2022年43卷第7期页码：203-214
- 作者机构：
  
  1. 华北电力大学河北省电力物联网技术重点实验室，河北保定 071003
  2. 国网上海市电力公司电力调度控制中心，上海 200122
- 作者简介：
  
  [ "廖海君（1997- ），女，广西贵港人，华北电力大学博士生，主要研究方向为电力物联网、智能电网中多模态通信技术等" ]
  [ "贾泽晗（1998- ），男，河北保定人，华北电力大学硕士生，主要研究方向为电力物联网、智能电网中多模态通信技术等" ]
  [ "周振宇（1983- ），男，河北张家口人，博士，华北电力大学教授、博士生导师，主要研究方向为无线通信网络与新技术、物联网与现代传感技术、能源互联网信息通信技术等" ]
  [ "刘念（1981- ），男，安徽安庆人，博士，华北电力大学教授、博士生导师，主要研究方向为智能配用电、综合能源系统、信息物理系统等" ]
  [ "王飞（1973- ），男，河北保定人，博士，华北电力大学教授、博士生导师，主要研究方向为新型电力系统、风光发电功率预测、虚拟电厂、电力市场与需求响应等" ]
  [ "甘忠（1975- ），男，湖北鄂州人，国网上海市电力公司工程师，主要研究方向为电力系统通信、电力系统自动化与网络安全、继电保护等" ]
  [ "姚贤炯（1972- ），男，浙江镇海人，国网上海市电力公司工程师，主要研究方向为电力系统通信、光传输新技术、电力系统控制业务5G技术应用等" ]
- 基金信息：
  
  国家电网有限公司科技基金资助项目(52094021N010)
- DOI：10.11959/j.issn.1000-436x.2022124
  中图分类号： TN915
- 网络出版日期：2022-06，
  
  纸质出版日期：2022-07-25
- 稿件说明：
移动端阅览
廖海君, 贾泽晗, 周振宇, 等. 面向调控信息新鲜度保障的电力至简物联网资源优化[J]. 通信学报, 2022,43(7):203-214.

Haijun LIAO, Zehan JIA, Zhenyu ZHOU, et al. Dispatching and control information freshness guaranteed resource optimization in simplified power Internet of things[J]. Journal on communications, 2022, 43(7): 203-214.
廖海君, 贾泽晗, 周振宇, 等. 面向调控信息新鲜度保障的电力至简物联网资源优化[J]. 通信学报, 2022,43(7):203-214. DOI： 10.11959/j.issn.1000-436x.2022124.

Haijun LIAO, Zehan JIA, Zhenyu ZHOU, et al. Dispatching and control information freshness guaranteed resource optimization in simplified power Internet of things[J]. Journal on communications, 2022, 43(7): 203-214. DOI： 10.11959/j.issn.1000-436x.2022124.

摘要

信息新鲜度对分布式能源调控模型训练精度具有重要影响。信息新鲜度较差会导致训练模型损失值增加，降低调控可靠性与经济性，影响能量实时供需平衡。电力至简物联网能够为分布式能源调控提供即插即用、多模态融合的通信支撑，但仍面临跨域资源优化与模型训练适配性差、调控信息新鲜度难以保障等挑战。针对上述问题，提出基于调控信息新鲜度感知的通信与计算资源协同优化算法，通过赤字虚拟队列演进感知调控信息新鲜度偏差。在此基础上，利用深度Q网络学习信道分配与批量规模联合优化策略，最小化模型损失函数，保障调控信息新鲜度长期约束。仿真结果表明，相较于基于联邦深度强化学习的低时延资源分配算法与自适应联邦学习批量规模优化算法，所提算法使全局损失函数降低57.19%和24.60%，信息新鲜度提高35.34%和49.05%。

Abstract

Information freshness conducts an important impact on the training accuracy of the distributed energy dispatching and control model.Poor dispatching and control information freshness will increase the loss function of the training model

reduce the reliability and economy of dispatching and control

and effect the real-time balance of energy supply and demand.Simplified power Internet of things can provide plug-and-play and multi-mode fusion communication support for distributed energy dispatching and control

but it still faces challenges of the inadaptability between cross-domain resource optimization and model training

and the difficulty in guaranteeing dispatching and control information freshness.To solve the above challenges

an information freshness aware-based communication-and-computation collaborative optimization algorithm (IFAC

O) was proposed

and the information freshness deviation was regulated by the awareness of deficit virtual queue evolution.On this basis

IFAC

O leveraged deep Q network and dispatching and control information freshness awareness to learn the channel allocation and batch size joint optimization strategy

thereby minimizing model loss function while guaranteeing long-term dispatching and control information freshness constraints.Compared with the federated DRL based low-latency resource allocation algorithm and adaptive federated learning-based batch size optimization algorithm

IFAC

O can reduce global loss function by 63.29% and 38.88% as well as improve information freshness by 20.59% a

nd 57.69%.

关键词

Keywords

references

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