| 魏子梁,郑庆荣,汤卓凡,张燚虎,陈树怡,耿 阳,林波荣.基于仿真模型库的规模化空调负荷需求响应潜力快速评估方法[J].电力需求侧管理,2025,27(4):71-77 |
| 基于仿真模型库的规模化空调负荷需求响应潜力快速评估方法 |
| Rapid evaluation method of large-scale air conditioning demand response potential based on simulation model library |
| 投稿时间：2025-03-03 修订日期：2025-04-26 |
| DOI：10. 3969 / j. issn. 1009-1831. 2025. 04. 011 |
| 中文关键词: 需求响应 暖通空调 模型库 全局温度调节 |
| 英文关键词: demand response HVAC model library global temperature adjustment |
| 基金项目:国家电网公司总部科技项目（5400-202340383A-2-3-XG） |
| 作者 | 单位 | | 魏子梁 | 清华大学 建筑学院,北京 100084；生态规划与绿色建筑教育部重点实验室（清华大学）,北京100084 | | 郑庆荣 | 国网上海市电力公司,上海 200030；上海市智能电网需求响应重点实验室,上海 200030 | | 汤卓凡 | 国网上海市电力公司,上海 200030；上海市智能电网需求响应重点实验室,上海 200030 | | 张燚虎 | 清华大学 建筑学院,北京 100084；生态规划与绿色建筑教育部重点实验室（清华大学）,北京100084 | | 陈树怡 | 清华大学 建筑学院,北京 100084；生态规划与绿色建筑教育部重点实验室（清华大学）,北京100084 | | 耿 阳 | 清华大学 建筑学院,北京 100084；生态规划与绿色建筑教育部重点实验室（清华大学）,北京100084 | | 林波荣 | 清华大学 建筑学院,北京 100084；生态规划与绿色建筑教育部重点实验室（清华大学）,北京100084 |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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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