基于联邦学习的分布式电采暖互动模式设计与展望

doi:10.3969/j.issn.2097-0706.2024.01.007

综合智慧能源 ›› 2024, Vol. 46 ›› Issue (1): 56-64.doi: 10.3969/j.issn.2097-0706.2024.01.007

基于联邦学习的分布式电采暖互动模式设计与展望

李彬¹(), 白雪峰¹^,^*(), 李志超¹(), 王仕俊²(), 刘淳²(), 程紫运²()

1.华北电力大学电气与电子工程学院，北京 102206
2.国网甘肃省电力公司发展事业部（经济技术研究院），兰州 730050

收稿日期:2023-01-02 修回日期:2023-03-31 出版日期:2024-01-25 发布日期:2023-05-05
通讯作者: *白雪峰（1997），男，硕士生，从事自动需求响应、电力通信技术等方面的研究，884973072@qq.com。
作者简介:李彬（1983），男，副教授，博士，从事电气信息技术、自动需求响应技术方面的研究，direfish@163.com；
李志超（1998），男，硕士生，从事虚拟电厂通信技术方面的研究，lizhichaochn@163.com；
王仕俊（1988），男，高级工程师，博士，从事电力工程建设方面的工作，angelofkill@126.com；
刘淳（1975），女，高级工程师，硕士，从事电力市场方面的工作，465913725@qq.com；
程紫运（1992），女，工程师，硕士，从事系统二次规划技术方面的工作，496443873@qq.com。
基金资助:
国家电网公司科技项目(SGGSJY00XMJS2100048)

Design and prospect of distributed electric heating interactive mode based on federated learning

LI Bin¹(), BAI Xuefeng¹^,^*(), LI Zhichao¹(), WANG Shijun²(), LIU Chun²(), CHENG Ziyun²()

1. School of Electric and Electronic Engineering， North China Electric Power University， Beijing 102206，China
2. Development Division of State Grid Gansu Electric Power Company（Economic and Technological Research Institute），Lanzhou 730050，China

Received:2023-01-02 Revised:2023-03-31 Online:2024-01-25 Published:2023-05-05
Supported by:
Science and Technology Project of State Grid Corporation of China(SGGSJY00XMJS2100048)

摘要/Abstract

摘要：

随着“双碳”目标的提出，以及“以电代煤”政策的贯彻落实，大量电采暖设备取代传统燃煤取暖投入运行并接入电网将成为必然趋势。大量电采暖设备可以作为需求侧可调资源进行新能源消纳，但是分布式电采暖所处地理区域较为分散，传统集中式管理的方式又存在隐私泄露、数据孤岛等问题。联邦学习作为一种分布式技术可在保护隐私的前提下支撑电采暖负荷互动，在分布式电采暖互动领域具有较强的适用性。分析了基于联邦学习的分布式电采暖互动需求，以及边缘缓存、隐私防护、通信传输优化和异构资源融合等技术在基于联邦学习的电采暖互动场景中的应用方式，并展望了未来基于联邦学习的分布式电采暖互动前景。

关键词: 电采暖互动, 联邦学习, 边缘缓存, 隐私保护, 通信传输优化, 异构融合技术

Abstract:

With the introduction of the dual-carbon target and the implementation of the " replacing coal with electricity " policy， substantial electric heaters are bound to be connected to the power grid and replace the traditional coal-fired heaters. The electric heater can be used as demand-side adjustable resources for new energy consumption. With regard to their management methods， distributed electric heaters are geographically scattered， while the traditional centralized heaters are vulnerable to privacy leakage and data islands. As a distributed technology， federated learning can support the interaction of electric heating loads under the premise of protecting privacy， and has strong applicability in the field of distributed electric heating interaction. In the analysis on the requirements of distributed electric heating interaction based on federated learning， the applications of edge caching， privacy protection， communication transmission optimization and heterogeneous resource fusion in the interactive modes are expounded. The prospect of distributed electric heating interactive modes based on federated learning is made.

Key words: electric heating interaction, federated learning, edge cache, privacy protection, communication transmission optimization, heterogeneous integration technology

中图分类号:

TK01

李彬, 白雪峰, 李志超, 王仕俊, 刘淳, 程紫运. 基于联邦学习的分布式电采暖互动模式设计与展望[J]. 综合智慧能源, 2024, 46(1): 56-64.

LI Bin, BAI Xuefeng, LI Zhichao, WANG Shijun, LIU Chun, CHENG Ziyun. Design and prospect of distributed electric heating interactive mode based on federated learning[J]. Integrated Intelligent Energy, 2024, 46(1): 56-64.

图/表 6

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基于联邦学习的分布式电采暖互动模式设计与展望

Design and prospect of distributed electric heating interactive mode based on federated learning

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