1. General information of typical district heating systems

#### 1.1. Introduction of DHSs

Indoor environment of living and work in cold areas in the world must satisfy certain conditions in cold period. For instance, in some Nordic countries, if people feel cold with in zone air temperature, they could run the heating system to maintain the air temperature warmer due to the DHS operated yearly. Another example is that, in China, following the heating guide, if average outside air temperature lowers than 5C within 3 days continuously, heating systems

© The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

should be run from that time (adjusted a few days actually). The space heating systems have been developed from decentralized heating to district heating, which are sometime called as central heating systems, usually with huge served heating floor area.

(2) Quick dynamic responses and characteristics could be gathered in the simulation with

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(4) The boundary conditions could be changed very efficiently by inputting different param-

(5) Both greater efficiency and time-saving of R&D could be achieved by programming with

(6) Almost all consideration and simulation could be fulfilled by using dynamic modeling to

(7) The system responses such as the performance of system, energy efficiency and zone air

(8) By using simulation method, the optimal parameters of system operation could be found and applied for real system to improve energy efficiency of buildings as well as overall

(1) It is relatively difficult to develop an overall dynamic model of a DHS with correction.

(2) Programming based on mathematics, control theory, optimization method and computer

(3) Simulations for fast dynamic system, water mass flow rate and pressure, for instance,

(4) Very powerful computers could be required for more accurate simulation with bigger and

Any closed system must obey certain laws including energy, momentum and mass conservation [3–5]. According to the properties of DHSs, the dynamic responses mainly depend on slow response system (temperature dynamics) rather than fast response system (mass and pressure dynamics). To this end, the dynamics of fast response systems could be replaced by using steady-state method without affecting the major dynamic properties of DHSs. Energy and mass conversion are applied for the development of an overall DHS that is addressed in

Recently, simulation methods of DHSs have played more important rule than ever followed by the progress of science and technology. The computer, algorithm, programming and software

(3) The results could be obtained in simulation environment without any risks.

acceptable accuracy by simulating the actual model.

eters and could be utilized while they are modified.

temperature could be observed and analyzed.

skills is required for various dynamic simulations.

could consume more time to get results.

1.3. Methods of dynamic modeling and simulation

friendly user interface.

attain optimal results.

1.2.2. The disadvantages of modeling

more complex DHS system.

1.3.1. Dynamic modeling method

the following section below.

1.3.2. Dynamic simulation method

DHSs.

When the DHSs are operated from the beginning to the end, the systems have been expectedly running in an optimal way to improve their performance, increase their energy efficiency, reduce the pollutant emissions and maintain accepted indoor air temperature as well. Consequently, energy efficiency of buildings has been approached. DHS performance is related to operational data collection, status estimation, alarm, data analysis, control settings and control strategies. Energy efficiency should consider the heat transfer from the heat source to the end users, which could be separated by efficiency of heat source, pipe network and substation, respectively. Although zone air temperature is normally kept in constant, but the set points could be reasonably changed such as to increase the human body adaptability with a little bit zone air temperature fluctuation.

From energy-saving point of view, how to reduce energy consumption including fuel, electricity and water is always a hot topic, a question and a great challenge. These reasons behind are that currently DHSs integrated not only have been become larger and larger but also require strong and various technologies to assist the activity. For example, Beijing Heating Group has the biggest DHSs in the world, around 2.55 Mm2 in 2016–2017 heating period with powerful SCADA system to support the operation.

One way to increase system energy and served building efficiency is to investigate the system properties using collected operational data and apply the results to the real system operation. However, this is time-consuming and cannot test all kinds of situations, which are expected because of potential risks. Therefore, what the efficient way could be utilized has been considered by the researchers and HVAC engineers in this field. Currently, this methodology is entitled as dynamic simulation based on mathematical modeling, which basically could be used to obtain system characteristics, study control strategies and predict energy consumption and dynamic responses of system operation.

#### 1.2. The advantages and disadvantages of dynamic modeling

System modeling has two types of methods: one is steady-state modeling, and the other is dynamic modeling [1, 2]. The thermal capacities of DHSs are not considered in steady-state modeling process, but those have been calculated in dynamic modeling. This is because thermal capacities have profound influence in actual system dynamic responses and process control. To investigate the system responses closely to the real world, the system modeling and simulations in this chapter always refer to the dynamics. The properties of modeling can be described as follows.

#### 1.2.1. The advantages of modeling

(1) An ideal model of an overall DHS can be developed by the first law of thermodynamics and corrected by operational data, which has been changed to the actual model.


#### 1.2.2. The disadvantages of modeling

should be run from that time (adjusted a few days actually). The space heating systems have been developed from decentralized heating to district heating, which are sometime called as

When the DHSs are operated from the beginning to the end, the systems have been expectedly running in an optimal way to improve their performance, increase their energy efficiency, reduce the pollutant emissions and maintain accepted indoor air temperature as well. Consequently, energy efficiency of buildings has been approached. DHS performance is related to operational data collection, status estimation, alarm, data analysis, control settings and control strategies. Energy efficiency should consider the heat transfer from the heat source to the end users, which could be separated by efficiency of heat source, pipe network and substation, respectively. Although zone air temperature is normally kept in constant, but the set points could be reasonably changed such as to increase the human body adaptability with a little bit

From energy-saving point of view, how to reduce energy consumption including fuel, electricity and water is always a hot topic, a question and a great challenge. These reasons behind are that currently DHSs integrated not only have been become larger and larger but also require strong and various technologies to assist the activity. For example, Beijing Heating Group has the biggest DHSs in the world, around 2.55 Mm2 in 2016–2017 heating period with powerful

One way to increase system energy and served building efficiency is to investigate the system properties using collected operational data and apply the results to the real system operation. However, this is time-consuming and cannot test all kinds of situations, which are expected because of potential risks. Therefore, what the efficient way could be utilized has been considered by the researchers and HVAC engineers in this field. Currently, this methodology is entitled as dynamic simulation based on mathematical modeling, which basically could be used to obtain system characteristics, study control strategies and predict energy consumption

System modeling has two types of methods: one is steady-state modeling, and the other is dynamic modeling [1, 2]. The thermal capacities of DHSs are not considered in steady-state modeling process, but those have been calculated in dynamic modeling. This is because thermal capacities have profound influence in actual system dynamic responses and process control. To investigate the system responses closely to the real world, the system modeling and simulations in this chapter always refer to the dynamics. The properties of modeling can be

(1) An ideal model of an overall DHS can be developed by the first law of thermodynamics and corrected by operational data, which has been changed to the actual model.

central heating systems, usually with huge served heating floor area.

92 Sustainable Buildings - Interaction Between a Holistic Conceptual Act and Materials Properties

zone air temperature fluctuation.

SCADA system to support the operation.

and dynamic responses of system operation.

described as follows.

1.2.1. The advantages of modeling

1.2. The advantages and disadvantages of dynamic modeling


#### 1.3. Methods of dynamic modeling and simulation

#### 1.3.1. Dynamic modeling method

Any closed system must obey certain laws including energy, momentum and mass conservation [3–5]. According to the properties of DHSs, the dynamic responses mainly depend on slow response system (temperature dynamics) rather than fast response system (mass and pressure dynamics). To this end, the dynamics of fast response systems could be replaced by using steady-state method without affecting the major dynamic properties of DHSs. Energy and mass conversion are applied for the development of an overall DHS that is addressed in the following section below.

#### 1.3.2. Dynamic simulation method

Recently, simulation methods of DHSs have played more important rule than ever followed by the progress of science and technology. The computer, algorithm, programming and software become very powerful as simulation tools. Many businesses or academic software such as BLAST, EnergyPlus, DeST, DOE-2, RNSYS, PKPM-CHEC, eQuest, VisualDOE, ESP-r, Ecotect, IES, etc. [6] can be easily obtained from various channels. However, if considering inside of the software, most of them were developed based on steady-state approach. In research area, dynamics is normally applied to equipment or partial system simulations. Few researchers are working in the dynamic simulation field for developing entire DHS modeling and try to utilize the models for system-level improvement [7–12].

The hot water with high-level temperature (usually less than 150C in design condition) is supplied from the boiler (sometimes from CHPs) in the heat source and transfers heat to the substations; then the heat is released to the secondary side in the substation, and the temperature of the return water in the primary side is decreased. The radiators at the end-users receive transferred heat from the substations and then are emitted to the indoor air for space heating. The supplied heat should be continuously gained to maintain suitable zone air temperature due to the heat balance between the indoor and the outdoor environments. The threeway control valves installed in the primary side of the substations are utilized to regulate the water mass flow into the heat exchangers and to balance the heat supplied to the secondary systems. Note that the makeup water systems in the secondary side are same as it is in the

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From Figure 1, it is realized that the structure of the indirect DHS includes the following subsystem such as heat source, pipe network in the primary side, substation, pipe network in the secondary side, heat emit system from terminal, indoor air and outside environment.

To obtain the mathematical model of the DHS, it is required to design the heating system illustrated in Figure 1. The design parameters are given in Table 1. The DHS is designed based on these parameters, which could be utilized to develop mathematical model and simulations.

The designed DHSs are a very complex system from mathematic modeling point of view because of the multiple connections among the subsystems. To simplify the dynamic model development process, several assumptions are listed below without affecting major properties

(1) Some parameters such as comprehensive heat transfer coefficient of buildings and heated

(2) The water leakage from the pipe network is assumed taking place in the primary pipe network of the substation and in the end-user of the secondary side, and it is divided into

(3) Transportation delay of pipe network is not considered in the system dynamics.

(5) The solar radiation is considered from south side windows in the outside wall only.

(6) The water mass flow rate remains constant in the secondary system in each substation.

primary side (drawing ignored).

3. Mathematical model development

3.2. Assumption of model development

floor area of buildings are integrated.

half in supply and half in return pipes, respectively.

(4) Fast response system is expressed as steady-state condition.

2.2. Subsystem of the DHS

3.1. Physical model

of the DHSs [13]:

#### 1.4. Major focus on this chapter

In this chapter, a typical hot water DHS is considered and designed. Then, a dynamic mathematical model is developed based on the physical model and thermal dynamic principles. An actual model is developed by correcting an ideal model of the DHS. Following that, the characteristics of the DHS could be collected by using open-loop test (OLT) method. Finally, five types of control strategies are simulated and compared with the analysis of dynamic response, energy consumption and zone air temperature responses.
