Study on Optimization Strategy of Ground Source Heat Pump System Based on Multi-Unit

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Energy Procedia Procedia 00 152(2017) (2018)000–000 33–38 Energy www.elsevier.com/locate/procedia

CUE2018-Applied Energy andLow Forum 2018: Low carbon andsystems, Applied Energy Symposium andSymposium Forum 2018: carbon cities and urbancities energy urban energy systems, 5–7 June 2018, Shanghai, China Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems, CUE2018, 5–7 June 2018, Shanghai, China CUE2018, 5–7 June 2018, Shanghai, China 15th InternationalStrategy Symposiumof onGround District Heating and Cooling Study onThe Optimization Source Heat Pump

Study on Optimization Strategy of Ground Source Heat Pump Based on Multi-Unit Assessing the System feasibility of using the heat demand-outdoor System Based on Multi-Unit temperature function forLiuaaa*,long-term heatcc demand forecast Qingrong Haikui Jinbb,district Yingjun Ruan a*

Qingrong Liu *, Haikui Jin , Yingjun Ruan *, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre

Associate Professor, College Shanghai University of Electric Power,c Shanghai and 200090, a,b,c of Energy and aMechanical Engineering, a b c China a* b Associate Professor, College of Energy Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China Master Student, College of Energy andand Mechanical Engineering, Shanghai University of Electric Power, Shanghai andand 200090, China b c Master Student, College of Energy and Mechanical Engineering, Shanghai University of Electric Power, ShanghaiChina and 200090, China Associate Professor, School of Mechanical Engineering, Tongji University, Shanghai and 200092, a IN+ Center forcAssociate Innovation, Technology andofPolicy Research - Instituto Tongji Superior Técnico, Av. Rovisco Pais 1, 1049-001 Professor, School Mechanical Engineering, University, Shanghai and 200092, China Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France

I. Andrić

Abstract Abstract This paper is based on the measured data of ground source heat pump system in a building. And the operation of groundAbstract This paper is system based on the measured of ground heat pump in a building. And the groundsource heat pump is evaluated and data analyzed. Then,source the hourly load ofsystem case building is simulated byoperation e-QUESTofsoftware, source heat pump system ischaracteristic evaluated and analyzed. Then, theishourly loadbyofK-Means case building is simulated by e-QUEST software, and the time-by-hour load curve of typical day analyzed clustering algorithm. By constructing District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasingthe the and the time-by-hour load characteristic of typical day optimal is analyzed by K-Means clustering algorithm. constructing internal energy model thecurve unitsector. and taking performance coefficient of heating andBy refrigeration as the greenhouse gasconsumption emissions from theofbuilding Thesethe systems require high investments which are returned through the heat internal energy consumption model the unit and takingmodel the optimal performance coefficient of heating and refrigeration as the goal, thetooptimal operating loadofratio optimization established. Finally, the demand operation of units sales.and Due the changed climate conditions and building is renovation policies, heat in strategy the future couldunder decrease, goal, and theload optimal operating load ratio optimization model is established. Finally, thethe operation strategy of unitsmodel. under The the typical daily curve of three kinds of typical daily load curves is optimized by using operation optimization prolonging the investment return period. typical daily load curve of of three kinds ofdifferent typical daily loadofcurves is optimized byheating using the operation optimization model. The optimal operation strategy the unit in periods the refrigeration and season is obtained, and by comparing The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand optimal strategy offound the unit inthis different periods of the refrigeration heating season ratio is obtained, and by comparing with the operation measured data, of it isAlvalade, that method can effectively theand energy efficiency of the ground-source forecast. The district located in Lisbon (Portugal),improve was used as a case study. The district is consisted ofheat 665 with the measured data, it the is found thatofthis method canand effectively improve the energy efficiency ratio of the ground-source heat pump system and achieve purpose saving energy reducing consumption. buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district pump system and achieve the purpose of saving energy and reducing consumption. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Copyright 2018 Elsevier All rights reserved. compared© results fromLtd. a dynamic heat demand model, previously developed and validated by the authors. Copyright ©with 2018 Elsevier Ltd. All rights reserved. Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the is scientific committee of Applied Energy and 2018: Low The results that when only weather change considered, the margin could Symposium be acceptable forForum some applications Selection andshowed peer-review under responsibility of the scientific committee of of theerror CUE2018-Applied Energy Symposium and Selection andand peer-review under responsibility of the scientific committee of Applied Energy Symposium and Forum 2018: Low carbon cities urban energy systems, CUE2018. Forum 2018: carbon citieswas andlower urbanthan energy systems. (the error inLow annual demand 20% for all weather scenarios considered). However, after introducing renovation carbon cities and urban energy systems, CUE2018. scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). Keywords: Ground source heat pump system, The internal energy consumption model, The typical daily load curve The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the Keywords: Ground source heat pump system, The internal energy consumption model, The typical daily load curve decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the 1.coupled Introduction scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and 1. Introduction improve the accuracy of heat demand estimations.

In large building energy systems, the use of ground-source heat pump systems is becoming more and more large building energy systems, thepump use systems of ground-source heat systems becoming moreand andheating. more common. Generally, ground-source heat are operated by pump multiple units toisprovide cooling © In 2017 The Authors. Published by Elsevier Ltd. common. Generally, ground-source pumpCommittee systems are operated by multipleSymposium units to provide cooling andand heating. Peer-review under responsibility of theheat Scientific of The 15th International on District Heating * Corresponding author. Tel.:+1-590-040-304-7; Cooling.

* Corresponding author. Tel.:+1-590-040-304-7; E-mail address: [email protected] E-mail address: Keywords: Heat [email protected] demand; Forecast; Climate change

1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102and Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the Applied Energy Symposium and Forum 2018: Low carbon cities Selection peer-review under responsibility the scientific Selection peer-review responsibility of the scientific committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urbanand energy systems, under CUE2018. and urban energy systems, CUE2018. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems. 10.1016/j.egypro.2018.09.055

Qingrong Liu et al. / Energy Procedia 152 (2018) 33–38 Qingrong Liu et al./ Energy Procedia 00 (2018) 000–000

34 2

The ground source heat pump system is part of the load operation state in most time periods [1]. There is a serious imbalance in the load rate of multiple units, which leads to the low performance of the units. According to the actual operation performance of the unit under partial load rate, this paper establishes the mathematical model of unit internal energy consumption, and carries on the clustering analysis to the simulated annual hourly load [2]. Under the condition of satisfying the load side demand, through the energy consumption model inside the unit, the load distribution of the multi-type typical daily load obtained by clustering is optimized, so that the unit has higher operation efficiency and the optimal operation strategy of the heat pump unit is determined [3-4]. 2. Operation case analysis 2.1. Introduction to energy systems The project case is a national green building, he total height of 18m, the base area of 6,411 ㎡, the total building area of 45086 ㎡；The aboveground area is 12128 ㎡ and the underground area is 32958 ㎡. The cooling and heat sources of the building are mainly divided into ground source heat pumps and chillers in the exhibition area. In some areas, frequency conversion multiline system and single air conditioning are used in some areas. The air conditioning system in the exhibition hall is composed of two screw chillers and two screw ground-source heat pump units, with a total refrigerating capacity of 5636kw. The refrigeration capacity of ground-source heat pump system is 2274Kw,and the heating capacity of ground-source heat pump system is 2290kW. The detailed parameters of the energy system are shown in tables 2-1. Table 2-1. The detailed parameters of the energy system. Unit Type

Number

Cooling Capacity （kW）

Heating Capacity （kW）

Standard Heating performance coefficient

Standard cooling performance coefficient

1

Screw ground source heat pump unit

2

1137.1

1145.2

4.48

5.93

2

Screw type watercooled chiller

2

1681.2

/

/

5.60

2.2. Analysis of Ground-source heat pump system operation Based on the measured data at present, the effect of ground-source heat pump system in this project is analyzed preliminarily.Field research and data collection were carried out on the building. The temperature difference between the user side and the ground side in the heating season is shown in figure 1. The temperature difference between the user side and the ground side in the cooling season is shown in figure 2. 30

40 35 30

20 15 10 5

Backwater temperature at the source side Water supply temperature at the source side Temperature difference of backwater supply on the ground side

25

T（℃）

T（℃）

25

20

Backwater temperature at the source side

15

Water supply temperature at the source side

10 5

0

0

Heating season

Fig. 1. Backwater temperature of source side in heating season

-5

Temperature difference of backwater supply on the ground side 9/1 9/3 9/5 9/7 9/9 9/11 9/13 9/15 9/17 9/19 9/21 9/23 9/25 9/27 9/29

Cooling season

Fig. 2. Backwater temperature of source side in cooling season

From Fig. 1, we can see that in the heating season, the temperature of the supply and return water on the ground source is kept at 25℃, and the average temperature difference is no more than 1 ℃. The temperature difference fluctuates greatly under the individual days, and the maximum temperature difference is 2.3 ℃. The main reason is



Qingrong Liu et al. / Energy Procedia 152 (2018) 33–38 Qingrong Liu et al./ Energy Procedia 00 (2018) 000–000

35 3

that the slow flow rate of water supply in the ground source pump group results in full heat transfer and the difference in temperature becomes larger. According to figure 2, the temperature of feed and backwater is about 30-35 ℃, the temperature difference of feed and backwater is less than 1 ℃, the maximum temperature difference is 1.1 ℃, and the minimum temperature difference is negative. It can be concluded that there is a phenomenon of cold volume pouring in the ground side, which may be mainly due to the excessive accumulation of underground heat, which leads to the difficulty of extracting the cold quantity. It can be seen from the above chart that there are some problems in the operation strategy or the selection of units. The temperature difference between the user side and the ground side in the cooling season and heating season is relatively low, showing the mode of "small temperature difference and large flow rate". Figures 3 and 4 show the refrigeration and heating performance coefficients of some nodes of a ground source heat pump system. COP （Ground source heat pump unit in summer）

5

7

Heat pump unit COP

Heat pump unit COP

6

4 3 2 1

COP （Ground source heat pump unit in winter）

6 5 4 3 2

1 0

0

Fig. 3. Performance coefficient of GSHP unit in summer(COP) Fig. 4 . Performance coefficient of GSHP unit in winter(COP)

From figures 4 and 5, we can see that the cop of ground-source heat pump units in winter and summer is far lower than the performance coefficient of rated refrigeration and heating. The average heating performance coefficient of ground source heat pump unit in winter is 3.37, and the average refrigeration performance coefficient of ground source heat pump unit in summer is 2.59. From the above conclusions, it can be seen that the energy efficiency ratio of ground source heat pump system in this building is low in actual operation. 3. Load simulation and clustering analysis 3.1. Dynamic load simulation of buildings based on e-QUEST In this paper, the load simulation software of case building based on e-quest is developed on the basis of doe-2[5]. The software can simulate many kinds of buildings. Then the local annual meteorological data are imported to calculate the dynamic load of the building year by year. Figures 5 and 6 are the building 3-d diagram and the annual hourly load diagram of the building, respectively. Building load（kW /x103）

4

Cooling load

0 -2 -4 -6 -8

Fig. 5. 3-D diagram of the building

Heating load

2

1

2001

4001 Hourly 6001

8001

Fig. 6 . Annual hourly load of buildings

Qingrong Liu et al. / Energy Procedia 152 (2018) 33–38 Qingrong Liu et al./ Energy Procedia 00 (2018) 000–000

36 4

From the image above, the maximum cooling load in summer is 6965.27 kW and the maximum heat load in winter is 2916.51kW. the accumulative cooling load in summer is 4638963kw. the annual cooling load per unit area is 115.97kW/㎡·a, and the accumulated heat load in winter is 1065278kw. the annual heat load per unit area is 26.63kw//㎡·a, and the load unbalance rate is as follows. 4.63; The unbalance of the heat and cold loads mentioned above is mainly due to the fact that the buildings are located in the southern region, Less heat load required in winter. 3.2. Clustering analysis The daily load curve of heating season and cooling season is analyzed by k-means algorithm, and the calinskiharabasz index is assigned by matlab to evaluate the number of clusters. The evaluation of the number of clusters is obtained through the evaluation of the number of clusters, the k-means function is called by matlab, and the index of Calinski-Harabasz(CH) is assigned to evaluate the number of clusters. The optimal number of clusters is 3, and figure 7 and 8 are the clustering calendars and the daily load curves of each type, respectively[6]. In figure 7, blocks of the same color represent the same type of load in the cooling and heating season, and different loads are represented by different symbols, for example, H1 represents the first load curve of the heating season. All kinds of load curves are different in load range, And has its own characteristics. For example, the building load of the second type of daily load curve in the heating season is maintained at 1000-3000 kW, and the construction load of the second type of daily load curve of the cooling season is maintained at 4000-7000 kW. for the above mentioned daily load curves of the refrigeration and heating seasons, According to the characteristics of different daily load curves, the optimal distribution of units is carried out in order to make the units run efficiently. 3500

H2 H2 H2 H2

H2 H2 H2 H2

H2 H2 H2 H2

FRI 0 0 0 0

SAT 0 0 0 0

SUN MON TUE WED THU C2 C2 0 C3 C3 C3 C2 0 C2 C2 C2 C3 0 C2 C3 C2 C2 0 C2 C2 C2

3000

FRI C2 C2 C2 C2 C2

SAT C2 C2 C2 C3

2000 1500 1000

SAT H3 H3 H3 H3

SUN MON TUE WED THU 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C1 C1 0 C1 C3 C3

MARCH/Heating SUN MON TUE WED THU H2 H2 H2 H3 0 H2 H2 H2 H3 0 H1 H1 H1 H3 0 H1 H1 H1 H3 0 H1 H1 H1

0 0 0 0

H1 H1 0 0

H1 H1 0 0

H1 H1 0 0

SAT 0 0 0 C1

JULY/Cooling FRI H2 H2 H1 H1

SAT H3 H3 H3 H3

APRIL/Heating SUN MON TUE WED THU

FRI 0 0 0 C1

SUN MON TUE WED THU C1 C1 C3 C2 C2

0 0 0 0

C1 C2 C2 C2

C1 C2 C3 C2

C3 C2 C3 C2

SAT H1 H1 H1 0 0

SUN MON TUE WED THU 0 C2 C2 C2 C3 0 C3 C3 C3 C2 0 C2 C2 C2 C2 0 C3 C2 C2 C3 0 C2 C2 C2

C2 C3 C3 C1 0

0 0 0 0 0

C2 C1 C1 C1

C2 C1 C1 C3

FRI

C2 C1 C1 C3

C2 C1 C1 0

SAT C2 C3 C2 C1 0

FRI C1 C1 C2 C3 C2

SAT C1 C1 C3 C2 C2

SUN MON TUE WED THU 0 0 0 0 0 0 0 0 0 0 0 H1 H1 H1 0 0 H1 H1 H1 0 0 H1 H1

SAT 0 0 0 0

SAT C3 C2 C2 C2

SUN MON TUE WED THU H1 H3 0 H1 H1 H1 H3 0 H2 H2 H2 H3 0 H2 H2 H2 H3 0 H2 H2 H2

FRI H1 H1 H2 H2 H2

SAT H3 H3 H3 H3 H3

4000 3000 2000

500

1000 0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

8000

Daily load Curve of the second kind of cooling season（C2)

7000

6000

5000 4000 3000 2000 1000 0

Daily load Curve of the first kind of cooling season （C1)

5000

7000

DECEMBER/Heating FRI C2 C2 C2 C2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

6000

2000

8000

7000

Daily load Curve of the third kind of heating season （H3)

2500

0

1000

8000

1000

FRI 0 0 H1 H1

1500

0

3500 3000

2000

500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1500

NOVEMBER/Heating

AUGUST/Cooling FRI H1 H1 H1 0 0

SUN MON TUE WED THU

2500

Building load （kW）

FRI H2 H2 H2 H1

Daily load Curve of the second kind of heating season（H2)

3000

Building load （kW）

TUE WED THU H2 H2 H2 H2 H2 H2 H2 H2 H2 H1 H1 H1

0

OCTOBER/Cooling

Building load （kW）

SUN MON 0 H3 0 H3 0 H3 0 H3 0

JUNE/Cooling

3500

Daily load Curve of the first kind of heating season （H1）

2500

500

FEBRUAY/Heating

H1 H1 0 0

SEPTEMBER/Cooling

SUN MON TUE WED THU 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Building load （kW）

0 0 0 0

SAT H3 H3 H3 H3 H3

Building load （kW）

H2 H2 H2 H2 H2

MAY FRI H2 H2 H2 H2 H2

Building load（kW）

JANUARY/Heating SUN MON TUE WED THU

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Daily load Curve of the third kind of cooling season（C3)

6000

5000 4000 3000 2000 1000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Fig. 7. Clustering calendar chart

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Fig. 8 . Load curve of each type

4. Operation optimization analysis 4.1. Construction of operation optimization model In order to optimize the ratio of heat pump unit, it is necessary to build a mathematical model of the internal energy consumption mechanism of the heat pump unit[7]. This paper mainly considers the energy consumption of the heat pump unit, and does not need to study the internal structure of the heat pump unit in detail. Therefore, the black-box model of heat pump unit is adopted in this paper, which is the running data collected by gordan scholars for a long time[8]. The mathematical model of the unit model is obtained by mathematical analysis of the data, which becomes the gordan model. The expression is as follows: 1

𝐶𝐶𝐶𝐶𝐶𝐶

= −1 + (

𝑇𝑇𝑐𝑐𝑖𝑖𝑖𝑖

𝑇𝑇𝑒𝑒𝑜𝑜𝑜𝑜𝑜𝑜

1

) + ( )(

W=

𝑄𝑄

𝑄𝑄𝑒𝑒

𝐶𝐶𝐶𝐶𝐶𝐶

𝑞𝑞𝑒𝑒 𝑇𝑇𝑐𝑐𝑖𝑖𝑖𝑖 𝑇𝑇𝑒𝑒𝑜𝑜𝑜𝑜𝑜𝑜

− 𝑞𝑞𝑐𝑐 ) + 𝑓𝑓𝐻𝐻𝐻𝐻

（1） （2）



Qingrong Liu et al. / Energy Procedia 152 (2018) 33–38 Qingrong Liu et al./ Energy Procedia 00 (2018) 000–000

𝑓𝑓𝐻𝐻𝐻𝐻 =

37 5

𝑞𝑞 𝑇𝑇𝑖𝑖𝑖𝑖 𝑇𝑇𝑖𝑖𝑖𝑖 𝑞𝑞 𝑞𝑞𝑐𝑐 1 1 + 𝑒𝑒 𝑐𝑐 +( 𝑐𝑐𝑜𝑜𝑜𝑜𝑜𝑜𝑒𝑒 −𝑞𝑞𝑐𝑐 )( + ) 𝑀𝑀𝑒𝑒 𝑇𝑇𝑜𝑜𝑜𝑜𝑜𝑜 𝑀𝑀𝑐𝑐 𝑀𝑀𝑒𝑒 𝑇𝑇𝑒𝑒 𝑒𝑒 𝑀𝑀𝑐𝑐 𝑇𝑇𝑒𝑒𝑜𝑜𝑜𝑜𝑜𝑜

（3）

𝑇𝑇𝑐𝑐𝑜𝑜𝑜𝑜𝑜𝑜 = 𝑇𝑇𝑐𝑐𝑖𝑖𝑖𝑖 + (𝑄𝑄 + 𝑊𝑊)/(𝑐𝑐 · 𝐺𝐺)

𝑊𝑊 = 𝑄𝑄𝑒𝑒 [(𝐿𝐿𝑑𝑑 + 𝐶𝐶1 ) 𝐿𝐿𝑑𝑑 =

𝑇𝑇𝑐𝑐𝑖𝑖𝑖𝑖

𝑇𝑇𝑒𝑒𝑜𝑜𝑜𝑜𝑜𝑜

𝑄𝑄𝑠𝑠

𝑄𝑄𝑒𝑒

（4）

− 𝐶𝐶2 𝐿𝐿𝑑𝑑 − 𝐶𝐶3 ]

（5） （6）

Where 𝑄𝑄𝑒𝑒 is a full load refrigeration (heat) quantity under rated working conditions, kW. 𝑄𝑄𝑠𝑠 is the refrigeration (heat) quantity of the unit under actual working condition, kW. 𝐿𝐿𝑑𝑑 is the operating load rate of the unit, %. 𝑇𝑇𝑐𝑐𝑖𝑖𝑖𝑖 is the inlet cooling water temperature of the condenser, ℃. 𝑇𝑇𝑐𝑐𝑜𝑜𝑜𝑜𝑜𝑜 is the cooling water temperature at the outlet of the condenser, ℃. 𝑇𝑇𝑒𝑒𝑜𝑜𝑜𝑜𝑜𝑜 is the evaporator outlet freezing water temperature, ℃. 𝐺𝐺 is the mass flow of cooling water, kg/s. C is the specific heat of the cooling water. 𝑞𝑞𝑐𝑐 is the heat loss inside the condenser, kW. 𝑞𝑞𝑒𝑒 is the heat loss inside the evaporator, kW. 𝑓𝑓𝐻𝐻𝐻𝐻 is dimensionless. 𝑀𝑀𝑒𝑒 is the heat transfer coefficient of the evaporator. 𝑀𝑀𝑐𝑐 is the heat transfer coefficient of the condenser. 𝐶𝐶1 、𝐶𝐶2 、𝐶𝐶3 is an undetermined constant. 1）Energy consumption model of heat pump unit in refrigeration season According to the operating data of the unit, the unknown parameters in the energy consumption model of the unit are obtained. The refrigeration energy consumption model of the heat pump unit is formulated as formula 7. （7） 𝑊𝑊𝐶𝐶 = 𝑄𝑄𝐶𝐶 [(𝐿𝐿𝐶𝐶 + 0.667) × 1.103 − 1.005𝐿𝐿𝐶𝐶 − 0.651] Where 𝑊𝑊𝐶𝐶 is heat pump unit energy consumption in refrigeration season, kW. 𝑄𝑄𝐶𝐶 is a full load refrigerating capacity under rated working conditions, kW. 𝐿𝐿𝐶𝐶 is the operating load rate of the unit, %. 2）Energy consumption model of heat pump unit in heating season According to the operating data of the unit, the unknown parameters in the energy consumption model of the unit are obtained. The heating energy consumption model of the heat pump unit is formulated as formula 8. （8） 𝑊𝑊𝐻𝐻 = 𝑄𝑄𝐻𝐻 [(𝐿𝐿𝐻𝐻 + 0.0927) × 1.10 − 0.935𝐿𝐿𝐻𝐻 − 0.041] Where 𝑊𝑊𝐻𝐻 is heat pump unit energy consumption in heating season, kW. 𝑄𝑄𝐻𝐻 is a full load heating capacity under rated working conditions, kW. 𝐿𝐿𝐻𝐻 is the operating load rate of the unit, %. 3）Objective function and constraint condition of energy consumption model The optimal cooling and heating performance coefficient of several units in a ground-source heat pump system is the goal, and the corresponding function is established. The cooling (heat) quantity provided by the heat pump system needs to meet the cooling and heat load requirements of the building, which is a constraint condition. 4.2. Analysis of Ground-source heat pump system operation From the above analysis, it can be seen that there are some problems in the selection of the existing ground source heat pump system in this building, in order to effectively improve the operation efficiency of the ground source heat pump system. In combination with the established mathematical model, the optimal operating load matching scheme is obtained by optimizing the operating load ratio of the new matching ground source heat pump unit. The new type selection of the unit is shown in Table 4-1. Table 4-1. The detailed parameters of the new heat pump unit. Unit Type

Number

Cooling Capacity （kW）

Heating Capacity （kW）

Standard Heating performance coefficient

Standard cooling performance coefficient

1

Ground source heat pump unit A

1

1137.1

1145.2

4.48

5.93

2

Ground source heat pump unit B and C

2

568.5

572.6

4.48

5.93

Three kinds of daily load curves in heating season are obtained by k-means clustering, and the nonlinear structure of matlab is optimized. The operation mode of the units under various daily load curves can be adjusted according to the daily effort and the principle of load matching in the following diagram.

Qingrong Liu et al. / Energy Procedia 152 (2018) 33–38 Qingrong Liu et al./ Energy Procedia 00 (2018) 000–000 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00

Heating performance coefficient （COP）

Heating performance coefficient /COP

38 6

Unit A

5.00 100%

4.50 4.00 3.50

2.00

7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 T

Fig. 10 . Operation optimization of the second kind of daily load

Unit C

5.00 4.50

100%

4.00

95.79%

71.44%

3.50

57.77%

3.00

96.56%

92.61%

73.64% 65.07% 70.66%

88.03% 62.20%

100% 64.39%

100% 59.68%

62.73%

39.69%

82.47% 76.94%

51.75% 51.77% 39.15%

39.85%

2.50 2.00

1.50 1.00

89.63% 93.43% 100% 91.27% 86.85% 82.46% 85.39% 74.34% 82.54% 79.04% 88.48% 72.56% 73.80% 82.53% 79.14% 65.07% 58.84% 68.18% 66.18% 63.29% 63.46% 58.18% 58.66% 54.09% 52.01%

2.50

7:00 8:00 9:00 10:0011:0012:0013:0014:0015:0016:0017:0018:0019:00 T

Fig. 11. Operation optimization of the third kind of daily load

Energy consumption（kW·h）

Heating performance coefficient /COP

Unit B

Unit C

3.00

Operating load rate of unit C

Fig. 9. Operation optimization of the first kind of daily load

Unit B

6000

Optimized energy consumption

Actual energy consumption

17.57%

5000 4000 3000

13.64%

2000 1000 0

2.25%

type 1 type 2 type 3 Three types of daily load in heating season

Fig. 12 . Energy saving of units before and after optimization

According to the above figure, the three kinds of daily load curves are optimized to get the operating load ratio and the performance coefficient of each unit respectively, and the energy efficiency ratio of the heat pump unit after the optimized operation is improved to a certain extent. From figure 12, we can see that the energy saving rate of the first type of daily load curve is 2.25%, that of the second kind of daily load curve is 17.57% after the operation optimization, and that of the third kind of daily load curve is 13.64% after operation optimization. 5. Conclusions 1）After optimization, the energy saving rate of the unit is obviously increased, and the average energy saving rate of the unit under the multi-type daily load curve is 11.15%. 2）After optimization, the load rate of each unit tends to be more balanced, which reduces the running hours of the unit under low load rate, and effectively improves the operating energy efficiency coefficient of a single unit. References [1] Bernier M A. Ground-coupled heat pump system simulation[J]. ASHRA Trans.2001,107(1):605-616. [2] Liu Z, Xu W, Zhai X, et al. Feasibility and performance study of the hybrid ground-source heat pump system for one office building in Chinese heating dominated areas[J]. Renewable Energy, 2017, 101:1131-1140. [3] Ma Z, Xia L. Model-based Optimization of Ground Source Heat Pump Systems ☆[J]. Energy Procedia, 2017, 111:12-20. [4] Lin J, Zhang Y, Meng J, et al. Optimization and study on average temperature equipment in ground source heat pump incubator system[J]. Journal of Chinese Agricultural Mechanization, 2016. [5] Samanta A, Dutta A, Neogi S. Passive design and performance evaluation of building using e-quest:a case study[J]. 2013. [6] Hamerly G, Elkan C. Alternatives to the k-means algorithm that find better clusterings[J]. 2002:600-607. [7] Madessa H B, Torger B, Bye P F, et al. Parametric Study of a Vertically Configured Ground Source Heat Pump System[J]. Energy Procedia, 2017, 111:1040-1049. [8] Fan R, Gao Y, Hua L, et al. Thermal performance and operation strategy optimization for a practical hybrid ground-source heat-pump system[J]. Energy & Buildings, 2014, 78(4):238-247.