[1]孟庆龙,王文强,李为林,等.商业建筑HVAC电力需求响应研究与分析[J].郑州大学学报(工学版),2021,42(5):92-99.[doi:10.13705/j.issn.1671-6833.2021.05.012]
　Meng Qinglong,Wang Wenqiang,Li Weilin,et al.HVAC Demand Response in Commercial Buildings: A Review[J].Journal of Zhengzhou University (Engineering Science),2021,42(5):92-99.[doi:10.13705/j.issn.1671-6833.2021.05.012]
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商业建筑HVAC电力需求响应研究与分析()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42
期数:
2021年5期
页码:
92-99
栏目:
出版日期:
2021-09-10

文章信息/Info

Title:
HVAC Demand Response in Commercial Buildings: A Review
作者:
孟庆龙1,王文强1,2,李为林3,熊成燕1,李洋1,任效效1
1.长安大学 建 筑 工 程 学 院，陕 西 西 安 710061； 2.中 国 启 源 工 程 设 计 研 究 院 有 限 公 司，陕 西 西 安 710018； 3.郑州大学 土木工程学院，河南 郑州 450001
Author(s):
Meng Qinglong1; Wang Wenqiang1,2; Li Weilin3; Xiong Chengyan1; Li Yang1; Ren Xiaoxiao1;
1.School of Civil Engineering， Chang’an University， Xi’an 710061， China； 2.China Qiyuan Engineering Corporation， Xi’an 710018， China； 3.School of Civil Engineering， Zhengzhou University， Zhengzhou 450001， China
关键词:
Keywords:
HVAC;  demand response;  building to grid;  potential prediction;  response strategy
DOI:
10.13705/j.issn.1671-6833.2021.05.012
文献标志码:
A
摘要:
针对空调系统参与电网需求响应所面临的问题,从暖通空调( HVAC)系统特性的角度,对建筑电网下多种能源交互的HVAC 需求响应( demand response,DR) 进行综述性研究与分析。 概述了 HVAC需求响应的定义和分类,并论述了利用模型预测控制算法、遗传算法等多种算法预测 HVAC 需求响应潜力的方法。 针对区域温度重设、提高送风温度、重设冷冻水水温等 DR 策略的原理与适用性进行归纳分析。 分析表明:对于实施 DR 后用户热舒适度提升的 DR 项目,可考虑在系统运行时采用该策略来降低能耗；主动储能策略与常规的 DR 策略结合使用能有效解决 DR 事件的负荷反弹问题；对于拥有较大可调度空调负荷的用户,应该考虑提供 DR 辅助服务。
Abstract:
Aiming at the problems of air conditioning system participating in power grid demand response, the demand response (DR) of multi energy interaction in building power grid is comprehensively studied and analyzed from the perspective of HVAC system characteristics. The definition and classification of HVAC demand response are summarized, and the methods of using model predictive control (MPC) algorithm, genetic algorithm (GA) and other algorithms to predict the potential of HVAC demand response are discussed. The principles and applicability of DR strategies such as resetting regional temperature, increasing air supply temperature, resetting chilled water temperature and so on are summarized and analyzed. The analysis shows that for DR projects where the user′s thermal comfort is improved after the implementation of DR， this strategy can be considered to reduce energy consumption during daily system operation, and the combination of active energy storage strategy and conventional DR strategy can effectively solve the load rebound problem of DR events. Therefore auxiliary services should be considered for those users with large adjustable air-conditioning load.

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更新日期/Last Update: 2021-10-11