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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (6): 1175-1184    DOI: 10.3785/j.issn.1008-973X.2021.06.019
    
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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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     
Received: 02 June 2020      Published: 30 July 2021
CLC:  TU 831  
Fund:  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）
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Qing-long MENG
Xiao-xiao REN
Wen-qiang WANG
Yang LI
Cheng-yan XIONG
Cite this article:

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.

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


考虑主动储能的HVAC需求响应策略实验与模拟

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 
Fig.1 Implementation process of GTA and ACES demand response strategy
Fig.2 Three-dimensional diagram of full-scale HVAC experimental platform
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 ?
Tab.1 Main equipment parameters of full-scale HVAC experimental platform
Fig.3 Water system principle considering active energy storage
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
Tab.2 Six modes of strategy operation
Fig.4 Variation of average power and corresponding outdoor temperature statistics in six strategies
Fig.5 Variation of power consumption of each unit and average outdoor air temperature in air-conditioning system
Fig.6 HVAC DR strategy simulation system considering active energy storage
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
Tab.3 Introduction of main simulation module
Fig.7 Three price curves
Fig.8 Comparison of measured and simulated power values of air-source heat pump
Fig.9 Comparison of measured and simulated power values of fan and pump
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
Tab.4 Three typical daily outdoor temperature variations
Fig.10 Actual room temperature of GTA strategy and corresponding change of power consumption of air-conditioning system
Fig.11 ACES+GTA strategy actual room temperature and air-conditioning system power consumption changes
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
Tab.5 Influence grade of indoor temperature change on thermal comfort
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%
Tab.6 Influence of two DR strategies on users' thermal comfort
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
Tab.7 Comparison of daily power consumption load reduction and subsidy cost between two strategies
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
Tab.8 Comparison of operating costs of two strategies under three electricity prices
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
Tab.9 Comparison of operating costs between conventional operating strategies for energy storage and non-energy storage
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