Rapid evaluation method of large-scale air conditioning demand response potential based on simulation model library

魏子梁; 郑庆荣; 汤卓凡; 张燚虎; 陈树怡; 耿 阳; 林波荣

doi:10.3969/j.issn.1009-1831.2025.04.011

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Rapid evaluation method of large-scale air conditioning demand response potential based on simulation model library

更新时间：2026-03-05

- Rapid evaluation method of large-scale air conditioning demand response potential based on simulation model library
- Vol. 27, Issue 4, (2025)
- 作者机构：
  
  1. 清华大学建筑学院,北京,100084
  2. 生态规划与绿色建筑教育部重点实验室（清华大学）,北京,100084
  3. 国网上海市电力公司,上海,200030
  4. 上海市智能电网需求响应重点实验室,上海,200030
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2025.04.011
  CLC：
- Published：2025
- 稿件说明：
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魏子梁, 郑庆荣, 汤卓凡, et al. Rapid evaluation method of large-scale air conditioning demand response potential based on simulation model library[J]. 2025, 27(4).
DOI：

魏子梁, 郑庆荣, 汤卓凡, et al. Rapid evaluation method of large-scale air conditioning demand response potential based on simulation model library[J]. 2025, 27(4). DOI： 10.3969/j.issn.1009-1831.2025.04.011.

摘要

由于可再生能源在能源系统中的占比不断增加

亟需挖掘建筑空调系统的需求响应潜力

因此建立空调负荷精细化模型对刻画空调负荷特性、评估和预测空调负荷柔性调节潜力具有重要意义。首先

按照房间类型、房间位置、室内热扰作息进行分类

并结合两种空调设备

建立了房间级别的精细化空调负荷仿真模型库

共涵盖144个房间模型。然后

基于EnergyPlus仿真平台

采用全局温度调节的策略进行批量化模拟

生成了涵盖107万d、30种工况、需求响应时长超过180万h的空调负荷需求响应数据集。模拟结果表明

房间级别的精细化空调负荷模型能够有效区分不同房间；接待大厅、普通客房、高档客房、健身房和餐厅具备较高的需求响应潜力

宾馆类建筑中此类房间占比较大

因此往往潜力更大。精细化空调负荷模型库能够快速提前了解大规模空调负荷的需求响应潜力

能够为制订需求响应政策和制度提供数据支撑。

Abstract

Given the increasing proportion of renewable energy in the power system

there is an urgent need to explore the demand response potential of building air conditioning systems. Establishing a refined model of air conditioning load is of great significance for reflecting the characteristics of air conditioning load

assessing

and predicting the flexible adjustment potential of air conditioning load.First

classifying according to room type

room location

and indoor thermal gain activities

and combined with two types of air conditioning equipment

a refined air conditioning load simulation model library at the room level has been established

including 144 types of rooms.Then

based on the EnergyPlus platform

a batch simulation is carried out using the strategy of global temperature adjustment

generating an air conditioning load demand response dataset consisting of 1 070 000 days

30 conditions and over 1 800 000 hours of demand response. The simulation results show that the refined air conditioning load model at the room level can effectively distinguish different rooms

and reception halls

general hotel rooms

high-end hotel rooms

gyms

and canteens have higher demand response potential

and hotel-type buildings have a larger proportion of such rooms

thus often having greater potential. The refined air conditioning load model library can quickly understand the demand response potential of large-scale air conditioning load in advance

providing insightful guidance for formulating demand response policies and systems.

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