Research on Pipeline Characteristics and Energy Saving of Distributed Secondary Pump System for District Cooling

Available online at www.sciencedirect.com Available online at www.sciencedirect.com

ScienceDirect ScienceDirect

Energyonline Procedia 00 (2018) 000–000 Available onlineatat www.sciencedirect.com Available www.sciencedirect.com Energy Procedia 00 (2018) 000–000

ScienceDirect ScienceDirect

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

Energy Procedia 158 Energy Procedia 00(2019) (2017)6405–6412 000–000 www.elsevier.com/locate/procedia

10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China

Research on Pipeline Characteristics and Energy Saving of The on 15thPipeline InternationalCharacteristics Symposium on District and Cooling Research andHeating Energy Saving of Distributed Secondary Pump System for District Cooling Distributed Secondary Pump System for District Cooling Assessing the feasibility of using the heat demand-outdoor Yijia Huo aa,Liang Cai bb*,Jianzhong Zhang cc Yijia Huo Cai *,Jianzhong temperature function for,Liang a long-term districtZhang heat demand forecast School of Energy and Environment, Southeast University, Nanjing, China a

b a School

School of Energy and Environment, Southeast University, Nanjing, China

a a b c c Architectural Design &,Research Institute Co., Ltd,Nanjing, Nanjing,China China , O. Le Corre School of Energy Environment, University, I. Andrića,b,c*, Nanjing A. Pina , P.and Ferrão J. Southeast Fournier ., B. Lacarrière cb

c Nanjing Architectural Design & Research Institute Co., Ltd, Nanjing, China IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 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 Abstract a

Abstract The distributed secondary pump system (DSPS) can reduce unnecessary throttling losses compared to the traditional centralized The distributed pump system (DSPS) can reduce throttling to the (DC) traditional centralized secondary pumpsecondary system (CSPS). Based on the graph theory unnecessary and Kirchhoff's law, alosses set of compared district cooling secondary pump Abstract secondary system (CSPS). Basednetwork on the graph theory and law, a set ofofdistrict (DC) secondary system waspump established. The pipeline characteristics andKirchhoff's energy consumption the twocooling systems under variablepump load system waswere established. TheThe pipeline network characteristics and energy consumption of DSPS the two under variable load conditions compared. calculation results show that underflow does not occur in andsystems more than 48.6% of pump Districtconsumption heating networks are commonly inthe theaccuracy literature one of the effective solutions decreasing the conditions were compared. calculation results show that underflow does not occur in DSPS andare more thanfor 48.6% of types, pump energy can be The saved. In orderaddressed to improve ofasthe study, themost load changes divided into four greenhouse emissions theInbuilding These require high investments which are through the heat energy consumption can befrom saved. order tosector. improve the systems accuracy ofand thethree study,user the load load changes, changes are divided into types, including fullgas load, single-user load change, two user load changes, for returned a total of 50four operating sales. Due toload, the changed climate conditions and renovation policies, infor the decrease, including full single-user load stability change, two userbuilding load and three userheat loadatdemand changes, a future total ofcould 50 operating conditions. For greater operational of DSPS underchanges, variable load, especially low load rate, some improvement prolonging the investment return period. conditions.have Forbeen greater operational stability of DSPS under variable load, pump especially at connection low load rate, improvement measures proposed in the operating range of the variable frequency and the modesome between the pump Thethe main scope ofoptimization this paper in is the to assess the feasibility of adopted using thefrequency heat demand – outdoor temperature function for measures have been proposed operating range the variable pump and thecan connection mode between thedemand pump and user. The results illustrate thatofthe optimization methods effectively reduce theheat maximum forecast. TheThe district of Alvalade, in Lisbon was as methods asave caseenergy study. The district isofconsisted of by 665 and the user. optimization illustrate that the(Portugal), adopted can effectively reduce hydraulic imbalance degree of results the located secondary pipeline networkoptimization and used further consumption the maximum pump buildings imbalance that15.6% vary in both construction period and typology. Three (low, consumption medium, high)ofand district hydraulic degree oftothe pipeline network andweather furtherscenarios save energy thethree pump by approximately compared that secondary before the optimization. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were approximately 15.6% compared to that before the optimization. compared with results from a dynamic heat demand Copyright © 2018 Elsevier Ltd. All rights reserved. model, previously developed and validated by the authors. ©The 2019 The Published by Elsevier Ltd. results showed that when only weather change is considered, the marginofof the error10could be acceptable for someon applications th International Copyright ©Authors. 2018 Elsevier Ltd. Allresponsibility rights reserved. Conference Applied Selection and peer-review under of the scientific committee This iserror an open accessdemand article under the CCthan BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) (the in annual was lower 20% weather scenarios considered). However, after introducing Selection and peer-review under responsibility of for the all scientific committee of the 10th International Conference onrenovation Applied Energy (ICAE2018). Peer-review under responsibility of theupscientific committee of ICAE2018 – Theand 10threnovation International Conference on Applied Energy. scenarios, the error value increased to 59.5% (depending on the weather scenarios combination considered). Energy (ICAE2018). The value of slope coefficient increased average within the range of 3.8% to 8% per decade, thatPump corresponds to the Keywords: District cooling; Centralized secondaryonpump system; Distributed secondary pumpup system; Hydraulic stability; energy decrease District in the cooling; number Centralized of heatingsecondary hours ofpump 22-139h during the heating season on the stability; combination of weather and consumption Keywords: system; Distributed secondary pump(depending system; Hydraulic Pump energy renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the consumption coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 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. * Corresponding author. Tel.: +86-131-4098-9588.

address:author. [email protected] * E-mail Corresponding Tel.: +86-131-4098-9588. Keywords: Heat demand; Forecast; Climate change E-mail address: [email protected] 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection peer-review under responsibility the scientific 1876-6102and Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10th International Conference on Applied Energy (ICAE2018). Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 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 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.199

Yijia Huo et al. / Energy Procedia 158 (2019) 6405–6412 Author name / Energy Procedia 00 (2018) 000–000

6406 2

1. Introduction District cooling (DC), with high energy efficiency and little pollutant emissionis widely applied and developing quickly [1-3]. Primary-secondary pump system with variable flow rate is usually adopted in the DC system with large scale, high resistance and considerable differences in the load characteristics or resistance of each loop. System economy is often used as an objective function to optimize system configuration or operation [4-6]. The secondary chilled water pumps are the second largest electricity consuming equipment among all major equipment in a DC system. Therefore, many optimization tasks have been carried out to reduce the energy consumption of DC transmission and distribution systems [7-11]. In conventional DC systems, centralized secondary pump system (CSPS) is applied (Fig.1) where secondary pumps are usually installed either inside the central chiller plant or in a pumping station immediately downstream of the chiller plant. In this system, the lift of the centralized secondary pump (CSP) is determined by the resistance of the least favorable loop and other loops have to rely on the balancing valve or adjusting valve, resulting in high pump energy consumption. In addition, the cooling host of the DC system is always running 24 hours a day in order to meet the needs of some buildings, so the system will inevitably run at a low load rate for a long time, making the system energy efficiency ratio lower. Distributed secondary pump system (DSPS), that is, the secondary pump is installed in each user heat exchange station (Fig. 2). The distributed secondary pump (DSP) only needs to overcome the resistance of each branch and its resistance to the central chiller plant, so that the pump energy consumption is greatly reduced. [12-14] Secondary

Chiller

Contro valve

Chiller

Secondary pump

Primary pump

Hea exchanger

Primary pump

Hea exchanger

Central chiller

Central chiller (a) CSPS

(b) Fig. 1. Simplified scheme of CSP and DSP DC system

DSPS changes the power distribution and the characteristics of the pipeline network relative to CSPS. The optimization of the system should not only consider the energy efficiency, but also consider the stability and safety of the system. In addition to reducing the energy consumption as much as possible, the interference between the branches should also be reduced. In this paper, a set of secondary cooling network system model was established based on the graph theory and Kirchhoff's law, aiming at simulating the changes of the pipe network characteristics and pump operation when the user load changes. Through this model, the degree of hydraulic imbalance and the energy consumption of secondary pumps of CSPS and DSPS respectively were calculated at different load rates. Moreover, further optimization measures were put forward in the operating range of the variable frequency pump as well as the connection modes between distribution network and end users. This work illustrates the advantages of DSPS and lays the foundation for the research on the system energy efficiency improvement of DC systems. 2. Mathematical model 2.1. Pipeline network model According to graph theory, for a closed pipeline network with m nodes and n branches, the relationship between each node and each branch can be described by a m n order correlation matrix as A. When the node is the start or



Yijia Huo et al. / Energy Procedia 158 (2019) 6405–6412 Author name / Energy Procedia 00 (2018) 000–000

6407 3

end of the branch, aij takes 1 or -1. If the node is not associated with the branch, the value of aij is zero. It can be proved that the rank of A is m-1, that is, any m-1 row of its associative matrix A is linearly independent. Deleting any row in A can give a fundamental incidence matrix Aa. The relationship between loops and branches in the pipeline network can be described by a p  n order loop matrix B, as Eq.(3). Where p is the number of basic loops in the pipeline network. When a branch is associated with a loop, depending on whether the two are in the same direction, bkj takes 1 or -1. When the branch is not associated with the loop, the value of bkj is zero. It can be seen that the rank of B is r = n-(m-1), that is, only r loops in p loops are mutually independent [15]. A matrix composed of any r loops in the loop matrix B is called an independent loop matrix Bf. According to the law of mass conservation and Kirchhoff's flow law, the algebraic sum of the flow into and out of any node in a fluid network is zero [16]. For a pipeline network with m nodes, the m-1 node traffic equation can be listed as Eq.(1). Where qj is the flow rate of branch j and Q is the n-order flow rate array of each branch. For any loop in a fluid network, the algebraic sum of the energy conversions occurring at each branch is zero [17]. Then the loop pressure balance equation of the pipeline network can be expressed as Eq.(2). Where hj, for a branch without a pump, equals the pressure loss of the branch; for a branch containing a pump, equals the algebraic sum of the pressure loss of the branch and the lift of the pump. Where H is an n-order array. The flow loss of each branch can be calculated as hi  si qi2 .

a Q 

n

a q ij

j

 0, i  1,2,, m  1

(1)

j 1

f 

n

b

kj q j

 0, k  1,2,, n  (m  1)

(2)

j 1

Combining Eq.(1) and Eq.(2), (m-1)+n-(m-1)=n independent equations are available. After giving each branch impedance and pump characteristic curve H=f(Q), the flow rate of n branches can be solved. 2.2. Variable frequency pump model Closed pipe network system characteristic curve, can be expressed as H  SQ 2 . Where S is the total impedance of the piping system. In the case of pump frequency conversion, the pump head H, and efficiency ηp can be fitted to the polynomials with respect to the flow rate Q and the rotational speed ratio k as Eqs. (3)-(4) using the least-squares method according to the samples provided by the manufacturer [18]. Where a1,a2,a3,b1, b2 and b3 are curve fitting coefficients. Pump power N is calculated as Eq. (5). The typical frequency converter efficiency ηf and motor efficiency ηm are shown in Eqs.(6)-(7) [19]. H  a1Q2  a2kQ  a3k 2

 p  b1

N

2

Q Q Q  b2    b3   k k   k

(3) 3

HQ 36001000 pmf

(4)

(5)

 f  0.5067  1.283k  1.42k 2  0.5842k 3

(6)

m  0.94187 （1  e 9.04k）

(7)

Yijia Huo et al. / Energy Procedia 158 (2019) 6405–6412 Author name / Energy Procedia 00 (2018) 000–000

6408 4

2.3. Hydraulic stability calculation model During the flow of fluid in the pipeline network, the actual value of the flow distribution must be different from the design value. The difference is the hydraulic imbalance. The degree of hydraulic imbalance is expressed by the ratio of the actual value of flow distribution to the design value. It is called hydraulic imbalance degree, that is calculated as Eq. (8). Obviously, the closer xi is to 1, the better the stability of the branch i is. For branch i, when the other branches in the pipe network are adjusted, the average value of hydraulic imbalance degree for branch i is as Eq. (9). The relative size of each branch x i value indicates that the stability of each branch is relatively good or bad. When adjusting branch i, the average hydraulic imbalance degree of other branches is as Eq. (10). The larger the y i is, the greater the influence of the branch i has on other branches. xi 

xi 

Qsi Qgi

(8)

x

i

 

i 1

yi

(9)

n 1 x j 1 j





n

x j i 1 j

(10)

n 1

3. Model establishment The DC system analyzed in this paper contains three users, assuming that each user's design flow is 500m3/h, and the design supply and return chilled water temperature is 5.5°C/12.5°C. The pressure loss of each user branch, the main pipe between users and the main pipe between user and central chiller plant are 5mH2O, 2.5mH2O and 5mH2O respectively. After the system form of DSPS and CSPS are applied respectively, the pump selection situation is shown in Table 1. Table 1. Secondary pump selection CSP

DSP1

DSP2

DSP3

Rated flow(m /h)

2020

790

485

550

Rated head(m)

35

32

24

20

Rated power(kW)

218.9

148.6

36.9

36.1

Rated efficiency (%)

88

84

86

83

3

From the point of view of type selection, for the same end-user, large-capacity and large-head CSP is the demand of CSPS, resulting in greater waste. However, the selection of the DSP in the DSPS is obviously smaller, saving initial investment and unnecessary throttling losses. 4. Result analysis In order to compare the hydraulic stability and energy consumption differences between DSPS and CSPS under various conditions, the end load changes are divided into 4 cases: case1: all users are in full load; case2-case22: single user load decreases from 90 % to 30%.; case23-case43: two user loads are reduced from 90% to 30%; case44case50:3 user loads are reduced from 90% to 30% at the same time. Hydraulic failure of each branch and pump operating parameters are calculated and analyzed separately.



Yijia Huo et al. / Energy Procedia 158 (2019) 6405–6412 Author name / Energy Procedia 00 (2018) 000–000

6409 5

4.1. Hydraulic imbalance analysis When the end-user load changes, the following conclusions can be obtained from Fig.2. 1) Under the design condition of 100% load rate, the flow of each branch can meet the user's demand, and both are in an over-current state. According to the distance of the branch from the central station, the flow rate of the branch increases from far to near. The branch maximum flow rate in CSPS and DSPS is 4.6% and 1.7% respectively. 2) When the load of one of the branches changes, calculate the xi and yi values of each branch. As Table.2 shows, in DSPS, the stability of the branch farther from the central chiller plant is better, and the stability becomes better as the load rate of the other branches decreases contrary to that in CSPS. Table 2 illustrates that the farthest branch has the greatest impact on other branches in CSPS and the degree of impact increases as the load rate decreases. However, in the DSPS, the influence of each branch on other branches is close, and that of the farthest branch is relatively large, and the degree of impact decreases with the decrease of load rate. From Table.3, CSPS is prone to inconsistent misalignment, near overcurrent, and distant undercurrent. The maximum underflow rate of the tributary is 13.6% under low load rate. The flow distribution in DSPS can meet the demand of all users, and the maximum overcurrent rate is about 4.6%. 3) When the load of the two branches changes, the underflow rate of the non-variable branch increases with the decrease of the branch load rate in the CSPS, and the underflow rate of the farthest branch is the largest. But the flow in the DSPS is uniform meeting the needs of users, the maximum over-flow ratio of non-variable branches is 6.0%. 4) When the load of three branches simultaneously changes, inconsistent maladjustment occurs in nonoperating conditions in CSPS. Under the low load rate, the current in the remote branch is severe. And the flow in DSPS still meets the user's demand with the over-flow rate ranging from 2.7% to 9.2%. x2 in CSPS

1.8

x3 in CSPS

x2 in DSPS

180

x3 in DSPS

1.4

1.3

1.4 1.2

1.2 1.0

x in DSPS

x in CSPS

1.6

1.1

0.8 0.6 0

10

20

30

40

CSP DSP Es

200

1.5

x1 in DSPS

50

100

80

160 140

60

120 100

60 20

40 20 0

1.0

40

80

Es(%)

x1 in CSPS

Pump energy consumption(kW)

2.0

10

20

30

Case

40

50

0

Case

Fig. 2. The hydraulic imbalance degree of different branches

Fig. 3. The pump energy consumption in different systems.

Table 2. Mean hydraulic imbalance degree of a branch when the load of other branches changes Load ratio

Average hydraulic imbalance degree

x1 in CSPS

x2 in CSPS

x3 in CSPS

x1 in DSPS

x2 in DSPS

x3 in DSPS

90%

1.040

0.983

1.098

1.045

1.043

1.039

80%

0.983

0.940

0.940

1.042

1.041

1.036

70%

0.963

0.928

0.916

1.039

1.038

1.033

60%

0.946

0.926

0.901

1.037

1.035

1.031

50%

0.930

0.915

0.891

1.035

1.033

1.029

40%

0.919

0.913

0.892

1.033

1.031

1.026

30%

0.916

0.929

0.912

1.031

1.029

1.025

Yijia Huo et al. / Energy Procedia 158 (2019) 6405–6412 Author name / Energy Procedia 00 (2018) 000–000

6410 6

Table 3. Average hydraulic imbalance degree of other branches when the branch load changes Load ratio

Average hydraulic imbalance degree of other branches

y1 in CSPS

y2 in CSPS

y3 in CSPS

y1 in DSPS

y2 in DSPS

y3 in DSPS

90%

1.100

1.090

0.988

1.042

1.043

1.044

80%

0.959

0.964

0.963

1.039

1.039

1.041

70%

0.940

0.942

0.939

1.036

1.036

1.038

60%

0.929

0.927

0.917

1.033

1.034

1.036

50%

0.923

0.917

0.897

1.031

1.032

1.034

40%

0.928

0.914

0.882

1.029

1.029

1.032

30%

0.951

0.926

0.880

1.028

1.028

1.030

4.2. Pump energy consumption analysis Under different load rates, the energy consumption of the pumps in each system are shown in Fig.3. It can be seen that the disperse arrangement of the secondary pump can effectively save the energy consumption. The pump energy consumption saved by DSPS relative to CSPS is called the energy saving rate and is expressed by Es, which is more than 48.6%, indicating the significant energy saving effect. 5. System Optimization Compared with CSPS, the hydraulic performance and energy saving performance of DSPS are obviously more superior. Whereas, there is still a certain degree of over-flow. And low pump operation efficiency and high energy consumption occur under low load rate. Therefore, optimization measures are applied in DSPS from two aspects of the pump operation interval and user inlet connection method. 5.1. Pump operation interval 1. Minimum speed ratio From Eq.(10). it can be seen that the energy consumption of the pump is closely related to the efficiency of the inverter and the efficiency of the motor. Research shows that when the speed ratio k is less than 0.4, the motor efficiency begins to decrease. When k is less than 0.3, the motor efficiency is significantly reduced, with large motor energy consumption and poor pump energy efficiency. Therefore, when the pump is running in variable frequency speed control, the range of k must not be lower than 0.5[20]. 2. Minimum flow operation mode Each frequency conversion secondary pump is provided with a minimum flow operation mode. When the flow of chilled water to be supplied to the user entrance is lower than the flow rate provided by the secondary pump at the lowest rotation speed, the electric bypass valve is opened to reduce the water supply by bypassing the chilled water to maintain its flow at the lowest speed. 5.2. Connection form at user entrance The connection form of the chilled water secondary pump water supply pipe and the user's entrance is divided into two forms: direct connection and indirect connection. According to the control mode, it is divided into two forms of mixed water connection and bypass connection. Taking into account the safety of the system, this paper uses a bypass chilled water supply indirect connection. When the chilled water flow required by the user is lower than the flow rate provided by the secondary pump at the lowest rotational speed, the water supply to the heat exchanger is reduced by bypassing the chilled water supply at the press-out end of the secondary pump. The electric two-way regulating valve adjusts the opening degree according to the signal of the differential pressure sensor between the supply and return pipe of the secondary pump



Yijia Huo et al. / Energy Procedia 158 (2019) 6405–6412 Author name / Energy Procedia 00 (2018) 000–000

6411 7

loop. The dynamic balance control valve is controlled by the pressure signal fed back by the pressure sensor on the air-conditioning return pipe to reduce the hydraulic imbalance in the secondary pump loop caused by local flow changes to improve the hydraulic stability. 6. Optimization Results

x1

100

x2

28

x3

0

10

20

30

40

1.10

50

x1 Case x2

1.08

x3

1.06 1.04 1.02 1.00

26 24

60 22 20

10

20

30

40

50

Case

Fig. 4. Comparison of hydraulic imbalance before and after optimization

40

N after optimization N before optimization 20  after optimization  after optimization

18 16 14

0

80

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Pump efficiency (%)

1.14 1.12 1.10 1.08 1.06 1.04 1.02 1.00

pump energy consumption N(kW)

x after optimization

x before optimization

1) The hydraulic performance of the pipeline network after optimization can be seen in Fig.4. It is obvious that the hydraulic imbalance of the optimized pipeline network is reduced, especially in the transition from low load rate to high load rate, where the flow in the pipeline network suddenly increases and the coupling effect between the branches increases. Originally, the hydraulic disengagement of the branch 1 reached a peak value of 1.13, and the maximum value of the hydraulic out of schedule after optimization is 1.05, showing that the stability is greatly enhanced.

0 1.0

Load rate

Fig. 5. Comparison of pump energy consumption and pump efficiency before and after optimization of branch 3

2) The optimized pump energy consumption and pump efficiency，taking branch 3 as an example, are shown in the Fig.5. Under the condition of low load rate, compared with the DCPS before optimization, the pump energy consumption is reduced by 15.6%, and the pump efficiency remains above 68%. 7. Conclusions 1) The traditional CSPS is prone to inconsistent misalignment under variable load conditions, and the undercurrent of the remote branch is severe especially under low load rate. The DSPS can reduce the coupling effect of each branch and effectively improve the hydraulic stability of the pipeline network. Besides, the DSPS flow distribution can always meet user requirements when the user load changes. 2) DSPS shows more significant energy-saving effect than CSPS. The secondary pump for chilled water is dispersedly installed in each user's building, reducing the model of the pump and minimizing the energy consumption of the chilled water distribution system. Calculating the energy consumption of the pump at different load rates indicates that the energy saving rate of the DSPS can reach more than 48.6%. 3) In order to further improve the hydraulic stability and pump operation efficiency of DSPS at low load rates, corresponding improvement measures have been put forward on the connection form between the pump and the user and the operating range of the variable frequency pump, which greatly reduced the peak value of the hydraulic imbalance degree and increased system security. In addition, a reasonable operating range ensures that the pump can always operate efficiently and the pump energy consumption is further reduced by approximately 15.6%. 4) Based on the graph theory and Kirchhoff's laws, a set of hydraulic calculation model for DSPS transmission and distribution network is established, which can be used for the selection calculation and system optimization of DC transmission and distribution network. The correctness of this model has been verified.

6412 8

Yijia Huo et al. / Energy Procedia 158 (2019) 6405–6412 Author name / Energy Procedia 00 (2018) 000–000

Acknowledgements Thanks to the careful guidance of Prof. Cai and Prof. Zhang, as well as the support of Nanjing Architectural Design & Research Institute Co., Ltd, China. References [1] Yin Ping. Research of combined cooling heating and power systems (4): District cooling and heating [J]. Heating Ventilating & Air Conditioning,2013,(7):10-17. [2] Tan Futai. Energy Saving Optimization of District Cooling System in Guangzhou University City [J]. Energy Conservation Technology,2009,(4):371-374. [3] Deng Bo, Long Weiding, FENG Xiaoping. Application of Large scale Surface Water Heat Pump System in Shanghai EXPRO [J]. Building Energy & Environment,2007,(6):95-97. [4] Apple L.S. Chan, Vic I. Hanby, T.T. Chow. Optimization of distribution piping network in district cooling system using genetic algorithm with local search [J]. Energy Conversion and Management,2007,48(10): 2622-2629. [5] Li Xiangli, Lin Duanmu, Shu Haiwen. Optimal design of district heating and cooling pipe network of seawater-source heat pump [J]. Energy and Buildings,2010,42(1): 100-104. [6] Kang Yingzi, Hua Ben. Optimal design of secondary piping network in district cooling systems[J]. Heating Ventilating & Air Conditioning,2010,(1):33-38. [7] Nurdan Yildirim, Macit Toksoy, Gulden Gokcen. Piping network design of geothermal district heating systems: Case study for a university campus[J]. Energy,2010, (35): 3256-3262. [8] H.I. Tol, S. Svendsen, An Exploration on Teaching Reformation of Engineering Thermodynamics and Heat Transfer for Marinc Engineering[J]. Energy,2012, 38(1): 276-290. [9] Yi Jianguang. Heat transfer analysis of buried pipes in district heating and cooling system [J]. Heating Ventilating & Air Conditioning,2014,(10):33-37,56. [10] Zhang Guoqiang，Xu Yuzhen， Han Jie，etc. Research on Optimal Economic Temperature Difference between the Chilled Water Supply and Return Model of a District-Cooling System [J]. Journal of Hunan University (Natural Sciences),2015,(9):128-133. [11] Kang Yingzi, Zuo Zheng. Cooling loss of the secondary piping network in district cooling system [J]. Heating Ventilating & Air Conditioning,2009,(11):31-36. [12]Lo, A. C. W., Jones, P., & Yik, F. W. H. Effects of pumping station configuration on the energy performance of district cooling systems[J]. Building Services Engineering Research & Technology, 2017,38(3), 287-308. [13]Sheng, X. J., & Lin, D. M. Energy saving factors affecting analysis on district heating system with distributed variable frequency speed pumps[J]. Applied Thermal Engineering,2017,(121) :779-790. [14]Zhang, Y. C., Chen, C. X., & Xia, J. J. Energy Performance Analysis of an Integrated Distributed Variable-Frequency Pump and Water Storage System for District Cooling Systems[J]. Applied Sciences-Basel,2017,(7):11-15. [15] David J. Sanderson, David C.P. Peacock, Casey W. Nixon, Atle Rotevatn. Graph theory and the analysis of fracture networks[J].Journal of Structural Geology, Available online 14 April 2018, ISSN 0191-8141. [16] Dejan Brkić, An improvement of Hardy Cross method applied on looped spatial natural gas distribution networks[J].Applied Energy.2008,(86): 1290-1300. [17]Bian Juan. Study on Hydraulic Stability of Chilled Water System Based on Bypass Differential Pressure Control [J]. China Petroleum and Chemical Standard and Quality,2016,(10):58-61. [18] Yan Da，Xia Jianjun，H Liu Ye，etc. Building environment design simulation software DeST(9) :simulation and analysis of cooling/heating plants(part 2) [J]. Heating Ventilating & Air Conditioning,2005,(4):42-53. [19]Michel A, Bernier B. Pumping energy and variable frequency drivers [J]. ASHRAE Journal,1999,(12):37-40. [20] Huang Siyu, Zhan Lijun, Liu Gang,etc. Strategy Analysis on Variable Frequency Pumps in Parallel Optimization Operation [J]. Shanghai Energy Conservation,2016,(5):271-276.