面向清洁能源跟踪的空调分组协同方法

武昕; 梁凯鑫; 焦点; 尤兰

doi:10.7500/AEPS20190505012

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面向清洁能源跟踪的空调分组协同方法

学术研究 | 更新时间：2025-06-16

- 面向清洁能源跟踪的空调分组协同方法
- Collaborative Method of Air Conditioner Grouping for Clean Energy Tracking
- 电力系统自动化-方正平台 2020年44卷第11期页码：68-77
- 作者机构：
  
  华北电力大学电气与电子工程学院，北京市 102206
- 作者简介：
  
  武昕(1986—)，女，通信作者，博士，副教授，硕士生导师，主要研究方向：智能用电及信息处理、电力系统通信与信号处理技术。E-mail：wuxin07@ncepu.edu.cn
  梁凯鑫(1994—)，女，硕士研究生，主要研究方向：智能用电与负荷管理。E-mail：13264242979@163.com
  焦点(1996—)，男，硕士研究生，主要研究方向：智能电力信息处理与非侵入负载监测。E-mail：a1321907679@163.com
- 基金信息：
  
  中央高校基本科研业务费专项资金资助项目(2018MS001)
- DOI：10.7500/AEPS20190505012
  中图分类号：
- 收稿日期：2019-05-05，
  
  修回日期：2019-10-22，
  
  网络出版日期：2020-04-16，
  
  纸质出版日期：2020-06-10
- 稿件说明：
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武昕,梁凯鑫,焦点等.面向清洁能源跟踪的空调分组协同方法[J].电力系统自动化,2020,44(11):68-77.

WU Xin,LIANG Kaixin,JIAO Dian,et al.Collaborative Method of Air Conditioner Grouping for Clean Energy Tracking[J].Automation of Electric Power Systems,2020,44(11):68-77.
武昕,梁凯鑫,焦点等.面向清洁能源跟踪的空调分组协同方法[J].电力系统自动化,2020,44(11):68-77. DOI： 10.7500/AEPS20190505012.

WU Xin,LIANG Kaixin,JIAO Dian,et al.Collaborative Method of Air Conditioner Grouping for Clean Energy Tracking[J].Automation of Electric Power Systems,2020,44(11):68-77. DOI： 10.7500/AEPS20190505012.

摘要

需求侧空调资源丰富、调控灵活并且具有储能功能，通过合理的聚合管控可对波动性强的清洁能源进行跟踪消纳。为了构建实现电网、聚合商与负荷之间双向互动的信息交互结构，研究了集群空调的分组协同管控方法。拥有云平台的家电商直接作为聚合商沟通电网与需求侧，利用云平台分别与空调和电网进行互动，将物理上分散的空调资源虚拟地聚合为可控整体；聚合商对空调分组管控，各组协同完成消纳任务以保证跟踪精度综合最优。因此，建立了聚合空调组可调节能力评估方法，以用户舒适度和空调物理参数为约束构建了消纳容量分配最优化问题，并通过滑模控制模型控制聚合空调完成跟踪任务。通过实验验证了所提算法在能够有效跟踪清洁能源的同时，对空调的干预更小，保证了用户舒适度。

Abstract

Air conditioner resources on demand side are abundant and adjustable and have the function of energy storage. It can track and consume highly volatile clean energy through reasonable aggregation regulation. In order to establish an information interaction structure that achieves two-way interactions between power grid

aggregators and loads

a method of group cooperative management for aggregated air conditioners is studied. As aggregators

appliance companies with cloud platforms coordinate power grid and demand side

and use cloud platforms to interact with air conditioners and power grid separately

which aggregates the physically dispersed air conditioner resources into a controllable entity. The aggregators manage and control air conditioners in groups

and each group cooperates to complete the consumption task to ensure that the tracking accuracy is comprehensive and optimal. An evaluation mechanism for air conditioner capacity is established. The optimization problem of accommodation capacity allocation is constructed with the constraints of users’ comfort and physical parameters of air conditioner. The aggregated air conditioners are controlled by the sliding-mode model to complete load tracking service. Through simulation

the experiments verify that the algorithm can effectively track the output of clean energy with less intervening in the air conditioner operation and ensuring users’ comfort.

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references

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