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浙江大学学报(工学版)
浙江大学学报(工学版)  2021, Vol. 55 Issue (6): 1175-1184    DOI: 10.3785/j.issn.1008-973X.2021.06.019
电气工程     
考虑主动储能的HVAC需求响应策略实验与模拟
孟庆龙(),任效效,王文强,李洋,熊成燕
长安大学 建筑工程学院，陕西 西安 710061
HVAC demand response strategy experiment and simulation considering active energy storage
Qing-long MENG(),Xiao-xiao REN,Wen-qiang WANG,Yang LI,Cheng-yan XIONG
School of Civil Engineering, Chang’an University, Xi’an 710061, China
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摘要：

为了增强电网的稳定性和充分利用集中式空调（HVAC）系统在夏季参与“削峰”需求响应的潜力，提出考虑主动储能的需求响应（DR）策略. 利用全尺寸变风量HVAC实验平台，对该策略与区域温度重设策略进行物理实验和TRNSYS仿真模拟. 结果表明，与区域温度重设（GTA）策略相比，主动储能策略能够在降低对用户热舒适度影响的同时为电网提供稳定的削峰负荷. 对于整个供冷季而言，相比所有天数均采用非储能常规运行策略，主动储能策略下空调系统节约一定的运行成本；相较于在DR时采用GTA策略且非DR时采用非储能常规运行策略，主动储能策略的节费优势更大.
关键词： 集中式空调（HVAC）;  需求响应;  主动储能;  空气源热泵;  TRNSYS    
Abstract:

A demand response (DR) strategy which considers active energy storage was proposed in order to enhance the stability of the power grid and fully use heating, ventilation and air-conditioning (HVAC) system in the summer to participate in the "peak shaving" demand response. A full-scale variable air volume HVAC experimental platform was used to conduct physical experiments and TRNSYS simulations on the strategy and the regional temperature reset strategy. Results show that active energy storage strategy can provide stable peak load for the power grid while reducing the impact on users’ thermal comfort compared with the global temperature adjustment (GTA) strategy. For the whole cooling season, the air-conditioning system saved operation cost under the active energy storage strategy while non-energy storage conventional operation strategy was adopted all the days. Cost-saving rates were more obvious compared to the GTA strategy during DR and conventional operation strategy of non-energy storage during non-DR.
Key words: heating, ventilation and air-conditioning (HVAC)    demand response    active energy storage    air-source heat pump    TRNSYS
收稿日期: 2020-06-02 出版日期: 2021-07-30
CLC:  TU 831  
基金资助: 陕西省重点研发计划资助项目（2020NY-204）；山东省可再生能源建筑应用技术重点实验室开放课题资助项目（JDZDS02）
作者简介: 孟庆龙（1979—），男，副教授，从事建筑节能技术的研究. orcid.org/0000-0003-0645-1032. E-mail： mengqinglong@chd.edu.cn
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孟庆龙
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熊成燕

引用本文:

孟庆龙,任效效,王文强,李洋,熊成燕. 考虑主动储能的HVAC需求响应策略实验与模拟[J]. 浙江大学学报(工学版), 2021, 55(6): 1175-1184.

Qing-long MENG,Xiao-xiao REN,Wen-qiang WANG,Yang LI,Cheng-yan XIONG. HVAC demand response strategy experiment and simulation considering active energy storage. Journal of ZheJiang University (Engineering Science), 2021, 55(6): 1175-1184.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.06.019    或    https://www.zjujournals.com/eng/CN/Y2021/V55/I6/1175

图 1  GTA与ACES需求响应策略的实施流程
图 2  全尺寸HVAC实验平台的三维示意图
设备名称 数量 qV /（m3·h?1） P /kW
ASHP 1 ? 9.8
循环水泵 1 8 0.78
AHU 1 5000 2.2
VAV-box 3 208~1800 ?
表 1  全尺寸HVAC实验平台的主要设备参数
图 3  考虑主动储能的水系统原理
策略 运行模式 运行时段 储能时段 释能时段
ST1 常规非储能 9:00—18:00 — —
ST2 常规储能 9:00—18:00 9:00—10:00 17:00—18:00
ST3 GTA 9:00—18:00 — —
ST4 ACES 8:00—18:00 8:00—9:00 14:00—16:00
ST5 改进储能常规 8:00—18:00 8:00—9:00 16:00—18:00
ST6 ACES+GTA 8:00—18:00 8:00—9:00 14:00—16:00
表 2  6种策略运行模式
图 4  6种策略时均功率与对应小时室外温度统计量的变化
图 5  空调系统各单元耗电量及室外空气平均温度的变化
图 6  考虑主动储能的HVAC需求响应策略仿真系统
图标 模块 作用
Type56 建立三维建筑能量模型，得到建筑冷热负荷和室内空气不同参数随时间变化的具体值
Type151 将空气处理到送风状态点后输送至末端的房间内，通过变风量末端进行风量调节
Type0 计算人员、设备、光照形成的建筑冷热负荷，编辑建筑冷热负荷与冷冻水流量的关系，输出冷冻水的回水温度和流量
Type2 通过储能罐进出水温差控制冷冻水泵的开关
Type14 利用线性插值生成一天内连续时间的强制函数离散数据，作为设备运行状态的控制信号
Type150 延时输出潜热释放率，模拟建筑热惯性，使得建筑模型得到优化
表 3  主要仿真模块的介绍
图 7  3种电价曲线
图 8  空气源热泵的实测与仿真功率值对比
图 9  风机和水泵的实测与仿真功率对比
典型日 tout /℃
全天区间 前1天区间 DR时段均值
Day1 24.3~36.5 19.2~32.2 36.1
Day2 25.7~36.1 23.2~31.7 35.7
Day3 23.2~35.1 24.3~36.6 34.8
表 4  3个典型日的室外温度变化
图 10  GTA策略实际室温与对应空调系统功耗的变化
图 11  ACES+GTA策略实际室温与对应空调系统功耗变化
温度区间 影响等级 温度区间 影响等级
[26，26.5] 0 [27，27.5] 2
[26.5，27] 1 [27.5，28] 3
表 5  室内温度变化对热舒适度的影响等级
温度区间 影响等级 GTA ACES+GTA
[26，26.5] 0 16.7% 55.8%
[26.5，27] 1 8.3% 6.7%
[27，27.5] 2 8.3% 11.7%
[27.5，28] 3 66.7% 25.8%
表 6  2种DR策略对用户热舒适度的影响
策略 $ {W}_{{\rm{d.DR}}} $ /（kW·h） $ {\alpha }_{{\rm{d.save}}} $ /% Wsave /（kW·h） $ {E}_{{\rm{DR}}} $ /元
GTA 82.0 12.4 11.5 35.2
ACES+GTA 84.8 9.4 17.9 55.7
表 7  2种策略日耗电量、负荷削减量及补贴费用对比
策略 电价类型 $ {F}_{{\rm{d.DR}}} $ /元 $ {C}_{{\rm{d.run}}} $ /元 $ {F}_{{\rm{h}}.{\rm{DR}}} $ /元 $ {\alpha }_{{\rm{DR.save}}} $ /%
GTA TOU 63.1 27.9 6.6 60.6
GTA RTP 68.2 33.0 6.5 55.2
GTA CPP 64.1 28.9 6.7 62.9
ACES+GTA TOU 65.3 9.6 3.1 81.9
ACES+GTA RTP 70.8 15.1 2.5 82.7
ACES+GTA CPP 66.5 10.8 3.2 82.1
表 8  3种电价下2种策略日运行成本的对比
类型 用电量/（kW·h） TOU /元 RTP /元 CPP /元
非储能 5 128.74 4 019.49 4 169.50 4 131.25
储能 5 147.15 3 744.79 3 951.44 3 856.55
表 9  储能与非储能常规运行策略的运行费用对比
1 LI W, XU P, LU X, et al Electricity demand response in China: status, feasible market schemes and pilots[J]. Energy, 2016, 114: 981- 994
doi: 10.1016/j.energy.2016.08.081
2 DONG J, XUE G, LI R Demand response in China: regulations, pilot projects and recommendations–a review[J]. Renewable and Sustainable Energy Reviews, 2016, 59: 13- 27
doi: 10.1016/j.rser.2015.12.130
3 WANG J, BLOYD C N, HU Z, et al Demand response in China[J]. Energy, 2010, 35 (4): 1592- 1597
doi: 10.1016/j.energy.2009.06.020
4 YANG C J Opportunities and barriers to demand response in China[J]. Resources, Conservation and Recycling, 2017, 121: 51- 55
doi: 10.1016/j.resconrec.2015.11.015
5 许鹏, 陈永保, 李伟林. 建筑需求响应控制及应用技术[M]. 北京: 中国建筑工业出版社, 2020.
6 程媛, 刘士友, 程小明 定频空调负荷参与需求响应的研究现状[J]. 电力科学与工程, 2020, 36 (1): 9- 17
CHENG Yuan, LIU Shi-you, CHENG Xiao-ming Research status of fixed-frequency air-conditioning load participating in demand response[J]. Electric Power Science and Engineering, 2020, 36 (1): 9- 17
doi: 10.3969/j.ISSN.1672-0792.2020.01.002
7 MOTEGI N, PIETTE M A, WATSON D S, et al. Introduction to commercial building control strategies and techniques for demand response[R]. California: Lawrence Berkeley National Laboratory, 2007.
8 WANG S, TANG R Supply-based feedback control strategy of air-conditioning systems for direct load control of buildings responding to urgent requests of smart grids[J]. Applied Energy, 2017, 201: 419- 432
doi: 10.1016/j.apenergy.2016.10.067
9 WANG S, GAO D, TANG R, et al Cooling supply-based HVAC system control for fast demand response of buildings to urgent requests of smart grids[J]. Energy Procedia, 2016, 103: 34- 39
doi: 10.1016/j.egypro.2016.11.245
10 TANG R, WANG S, GAO D C, et al A power limiting control strategy based on adaptive utility function for fast demand response of buildings in smart grids[J]. Science and Technology for the Built Environment, 2016, 22 (6): 810- 819
doi: 10.1080/23744731.2016.1198214
11 周磊. 空调负荷的动态需求响应理论及其应用研究[D]. 南京: 东南大学, 2017.
ZHOU Lei. Dynamic demand response theory of air conditioning load and its application research[D]. Nanjing: Southeast University, 2017.
12 杨济如, 石坤, 崔秀清, 等 需求响应下的变频空调群组削峰方法[J]. 电力系统自动化, 2008, 42 (24): 44- 52
YANG Ji-ru, SHI Kun, CUI Xiu-qing, et al Peak clipping method for variable frequency air conditioning group under demand response[J]. Power System Automation, 2008, 42 (24): 44- 52
13 张天伟, 谢畅, 王蓓蓓, 等 美国商业建筑空调负荷控制策略及其在自动需求响应系统中的整合应用[J]. 电力需求侧管理, 2016, 18 (6): 60- 64
ZHANG Tian-wei, XIE Chang, WANG Bei-bei, et al The commercial building air conditioning load control strategies and its comprehensive application in automated demand response system of the U. S[J]. Power Demand Side Management, 2016, 18 (6): 60- 64
doi: 10.3969/j.issn.1009-1831.2016.06.015
14 孔赟, 潘棋, 王蓓蓓, 等 空调自动需求响应控制策略在美国商业建筑中的应用案例分析[J]. 电力需求侧管理, 2017, 19 (1): 60- 64
KONG Yun, PAN Qi, WANG Bei-bei, et al An appli- cation case study of automatic demand response control strategy for air conditioning in commercial buildings in the United States[J]. Power Demand Side Management, 2017, 19 (1): 60- 64
doi: 10.3969/j.issn.1009-1831.2017.01.016
15 XU P, HAVES P Case study of demand shifting with thermal mass in two large commercial buildings[J]. ASHRAE Transactions, 2006, 112: 572- 580
16 CUI B, GAO D C, XIAO F, et al Model-based optimal design of active cool thermal energy storage for maximal life-cycle cost saving from demand management in commercial buildings[J]. Applied Energy, 2017, 201: 382- 396
doi: 10.1016/j.apenergy.2016.12.035
17 CUI B, GAO D C, WANG S, et al Effectiveness and life-cycle cost-benefit analysis of active cold storages for building demand management for smart grid applications[J]. Applied Energy, 2015, 147: 523- 535
doi: 10.1016/j.apenergy.2015.03.041
18 陈东文, 刘育权, 李勇, 等 用于削减工业园区用电功率峰值的蓄冷空调系统的规划建模与优化[J]. 电力自动化设备, 2017, 37 (6): 94- 100
CHEN Dong-wen, LIU Yu-quan, LI Yong, et al Planning modeling and optimization of cold storage air conditioning system for reducing peak power consumption in industrial parks[J]. Electric Power Automation Equipment, 2017, 37 (6): 94- 100
19 BAETEN B, ROGIERS F, HELSEN L Reduction of heat pump induced peak electricity use and required generation capacity through thermal energy storage and demand response[J]. Applied Energy, 2017, 195 (1): 184- 195
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