- Email: [email protected]
Technologies in Smart District Heating System
Available online at www.sciencedirect.com
ScienceDirect Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2017) 000–000
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www.elsevier.com/locate/procedia
Energy Procedia 142 Energy Procedia 00(2017) (2017)1829–1834 000–000 www.elsevier.com/locate/procedia
9th International Conference on Applied Energy, ICAE2017, 21-24 August 2017, Cardiff, UK
Technologies in Smart District Heating System a
The 15th International Symposium on District Heating and Cooling
Lin Gao , Xuyang Cuia, Jiaxin Nia, Wanning Leib, Tao Huangb, Chao Baib, Junhong a, Assessing the feasibility of using Yang * the heat demand-outdoor
Key Laboratory of Efficient Utilization offor Low and Grade Energy,MOE ，School heat of Mechanical Engineering， Tianjin temperature function a Medium long-term district demand forecast aa
a,b,c
I. Andrić
University,Tianjin 300350,China； Energy Group Xian Raising a a co.LTD,Xian 710100,Shanxi,China b
b bXi
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrièrec, O. Le Correc
a
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal Abstract b c
Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
The smart district heating (DH) system has greater advantages than the traditional heating system in many aspects (e.g., energy saving, regulation and control, troubleshooting), and it shows great development potential and broad market prospect in the future. This paper elaborates the concept of the smart DH system, as well as the corresponding function of each component. Technologies Abstract that may be well integrated into this intelligent system are introduced and evaluated. The future development of the smart system is briefly analyzed. ©District 2017 The Authors. Published by Elsevier Ltd. heating networks are commonly in the literature as one of the most effective solutions for decreasing the © 2017 The Authors. Published by Elsevier addressed Ltd. Peer-review under responsibility scientific committee of the 9th International Conference on Applied Energy. greenhouse under gas emissions from of thethe building sector. These of systems high investments are returned Peer-review responsibility of the scientific committee the 9threquire International Conferencewhich on Applied Energy.through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, Keywords: District heating; Smart thermal grids; Intelligent control prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 1.buildings Introduction that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were During with the urbanization increases, but the corresponding building energy consumption stays compared results from aprocess, dynamic the heatcity-size demand model, previously developed and validated by the authors. results showed whenenergy only weather change is considered, the margin couldenergy be acceptable for somehowever, applications atThe a high level. The that heating consumed is the most important part of of error building consumption, it annual demand thanenergy 20% forsaving all weather scenarios considered). However, after introducing renovation is(the alsoerror the in most wasted and was has lower the most potential. Studies concluded that district heating (DH) plays therole errorinvalue increased up to 59.5% (depending on the weather and renovation anscenarios, important the implementation of future sustainable energy systems [1,2]. scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the With the increase of the heat user’s number and heating network scale, the traditional heating system could not decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and satisfy the current urban heating demand, and the shortcomings become prominent. The increasing degree of renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the deregulation of the energy market and the increasing focus on energy efficient buildings place a demand on DH coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and systems more of efficient than estimations. ever. The development of the technology provides opportunity and change for improve to thebe accuracy heat demand © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Cooling. E-mail address: [email protected]
Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy.
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 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy. 10.1016/j.egypro.2017.12.571
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Lin Gao et al. / Energy Procedia 142 (2017) 1829–1834 Author name / Energy Procedia 00 (2017) 000–000
district heating, many new concepts are presented and applied to realize the “city smart network”, aiming at energy saving and improvement of the comfort degree. A concept of 4th Generation District Heating (4GDH) including the relations to District Cooling and the concepts of smart energy and smart thermal grids was defined in [3]. The future DH system should fulfill the goals like supplying low-temperature district heating for space heating and domestic hot water, integrating renewable heat sources, and operation planning. Besides, the smart DH system may work as an integrated part of smart energy systems, i.e., integrated smart electricity, gas, fluid and thermal grids. This work aims to describe the concept and framework of the smart district heating system. Some related technologies that may be well integrated with the smart system are also introduced. 2. Concept and structure of smart DH system The concept of smart thermal grids can be regarded as being parallel to smart electricity grids, and it focuses on the integration and efficient use of potential future renewable energy resources as well as the operation of a grid structure allowing for distributed generation which may involve interaction with consumers[3]. Through the information network, different parts (thermal source, network of pipes, substation, heat user) are connected together and integrated into a long-distance management controlled and intelligent system, i.e., the smart district heating system. A series of measures are adopted to improve the reliability, safety and efficiency of the heating system, and the dynamic optimizations of heating system operation, maintenance and planning could be realized. The system obtains the real time data of different parts and builds a running database, in order to realize the storage and analysis of all the information on a uniform management platform. Based on the smart district heating system, the smart forecasting of load, the smart regulation of heat, the smart optimization of scheduling and the smart diagnosis of fault could be realized. Fig. 1 illustrates the schematic diagram of the smart district heating system. The smart system consists of control center, communication network, geographic information system (GIS), supervisory control and data acquisition system (SCADA). The control center which is the core of the smart thermal grids system takes charge of the monitoring, control and management of the whole system running. SCADA system acquires and supervises the realtime condition of the heat users. The communication network connects each instrument together to the control center, in order to ensure the real-time transmission of data and instructions. GIS system provides the position data of the heating network. Besides, a powerful software system named intelligent control system is established to supervise and control the hardware equipment.
Fig. 1. Schematic diagram of the smart DH system
Through the cooperative work of each section, the smart district heating system has characteristics of high efficiency, energy-saving, real-time, reliability and forward looking. In view of the real-time supervision, the data of control center is synchronized with the operational data, which could detect the fault in the first time and avoid the leak and steal of the steam. Via the simulating operation and analysis of heating network, the optimized running condition could be confirmed, which avoids the thermodynamic imbalance and hydraulic imbalance of the heating
Lin Gao et al. / Energy Procedia 142 (2017) 1829–1834 Author name / Energy Procedia 00 (2017) 000–000
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network. The smart system could automatically adjust the temperature of water supply according to the outside temperature, which saves energy consumption and improve the heating quality. 3. Innovations and new techniques in smart DH system Due to the development of the computer technology, communication technology and automatic control technology, the district heating system is becoming smarter. Some new technologies as well as new control strategies are applied to the smart heating system. As energy management and control regulation become more precise, the operation of multi-source network and distributed variable frequency pump becomes more possible. With the popularization of intelligent control in the end users, the heat load regulation strategy could turn from unified control of whole system into joint regulation of heat source, substation and heat user, which makes it possible to be adjusted to the user’s own need. 3.1. Intelligent control system Compared with the traditional heating system, the smart district heating system has an intelligent control system which could constantly monitor, control and manage the whole heating system to ensure high effectiveness and reliability. The intelligent control system serves as the brain of the smart system, and it imitates the human brain in the unknown environment in order to achieve intelligent management and operation. The intelligent control system includes three parts: dispatching and management system, on-line energy consumption analysis system, accident alarm system. The control center remotely real-time monitors and controls each substation. Operation parameters are real-time collected by diverse sensors on site, and then sent back to the control center. The dispatching and management system make a trend prediction based on the data analysis, and a feedback is made to control the substation. The on-line energy consumption analysis system analyzes the collected data and compares them with the historical database. The operation condition of each substation is evaluated, and high energy consumption processes could be detected and regulated. Heat load could be pre adjusted based on the climate and characteristic of heating supply in different periods. For example, corresponding relationship between heating temperature and outside temperature is established. As a result, optimal heat load could be computed in different weather, which achieves load pre-regulation and improves user’s comfort. The accident alarm system could quickly detect accident. Combined with the GIS system, the accident point location could be achieved exactly, which ensure the heating system safety and avoid steam steal or leakage. As the network and the buildings have great thermal inertia, there is a large lag in control. It is difficult for a common control system to achieve exact thermal match between supply and requirement. Besides, the heating network is a multi-input multi-output coupled nonlinear system whose dynamic characteristics vary largely with operating conditions. Many segments have great inertia, pure time-delay, non-linear and model uncertainty. For purpose of heat supply-demand balance and hydraulic balance of heating system, the intelligent control system requires accuracy, robustness, real-time and fault tolerance. The control methods include fuzzy control [4], neural network control [5], expert control [6], human-simulated intelligent control [7] and hybrid control. For example, with respect to the uneven distribution and strong stochastic of DH system, expert control strategy could improve the heating efficiency. The fuzzy control method can be used to identify the time lag in the system. However, a single control method usually has the limitation of application. Several intelligent control methods are usually integrated together to form computational intelligence [8,9], which is close to the human brain. More and more attention has been paid to the integrated intelligent system based on computational intelligence. Hybrid intelligent control based on conventional control and intelligent control is a widely used method, for instance, fuzzy technology, neural network and genetic algorithm are integrated and form a new hybrid intelligent system. The integrated intelligent system has simple structure, good universality and expansibility. Besides, it can improve the reliability and real-time performance while realizing complex function. With respects to the large time lag, the predictive control algorithm is applied in DH system. It has good tracking performance, requires less model accuracy and is suitable for controlling large time-delay object. Thus, it is paid more attention on complex industrial process control.
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Lin Gao et al. / Energy Procedia 142 (2017) 1829–1834 Author name / Energy Procedia 00 (2017) 000–000
Since in DH network there are a large number of intelligent devices, managing a huge amount of real-time information sent and received from these devices is a challenging task, as shown in Fig. 2. Therefore, cloud computing is a suitable solution to meet these demands and to solve the issue of information storage. A framework based on Cloud technology for big data information management in DH network, was introduced in [4]. The framework could make a platform to store, access and share heterogeneous energy information.
Fig. 2. Information flow in DH network
3.2. DH system with distributed variable-frequency pump With the expansion of the city district heating scale, it usually takes hundreds minute for hot water to circle the whole network, therefore the measured temperature of return water lags behind the adjusted temperature by users. The control of circulating water pump based on return water temperature can not timely meet the user’s heat requirement. If circulating water pump makes adjustment, the control effect shows until the hot water circles and reaches the data collection point. The time lag can be eliminated by changing the heating mode. Besides, there is excessive available head pressure in traditional heating system. Regulating valve must be installed in household water supply line to eliminate the excess head pressure, which induces low efficiency of water pump. Without efficient regulation, the near-end users usually have surplus water flow, however the far-end users have insufficient water flow, which leads to uneven cold and hot in heating system. With the development of frequency conversion variable speed technology, the concept that using variable-frequency pump to adjust water flow instead of the valve is presented [11,12], as shown in Fig. 3. Heating system with distributed variable-frequency pump is flexible to adjust and suitable for metering heating. This new network and the method used to control the flow rate of the primary supply water and its temperature are changed according to the outdoor temperature. The correct temperature level of the supply temperature is fixed as functions of the outdoor temperature. It is showed that the new DH system could save 49.41% of the average electrical energy compared with heating system conventional central circulating pump [12].
Lin Gao et al. / Energy Procedia 142 (2017) 1829–1834 Author name / Energy Procedia 00 (2017) 000–000
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Fig. 3. Schematic diagram of DH system with distributed variable-frequency pump
The end-user needs to add a booster pump in case the main circulating pump is laid out to the second most adverse loop pipe network. Compared with heating system with conventional circulating pump, the district heating system with distributed variable-frequency pumps is provided with an appropriate node in the pipe network as the pressure difference control point; therefore, the main circulating pump head only overcomes the resistance in the main pipe, while the respective pumps take charge of the pressure head from respective users. 3.3. Multi-heat source joint heating system The traditional DH system usually has one heat source which supplies heat to one district. This system has many stand-by equipment, and the boiler can not run at full capacity, resulting in low thermal efficiency. The district heating system with multi-heat source could efficiently solve the aforementioned problem. With the help of the intelligent control center, the coordination and matching between multiple heat sources will be better controlled by the intelligent control center. The multi-heat source heating allows for the wide use of heat from waste-to-energy and various industrial surplus heat sources as well as the inclusion of geothermal and solar energy. Several heat sources, which include a main heat source with maximum heat capacity and some auxiliary heat sources, simultaneously supply heat to the thermal grids in a multi-heat source joint heating system. During whole heating period, the main heat source keeps full load operation, while auxiliary heat sources are regulated by turns to meet different thermal loading requirement of users, in this way, the average efficiency of heat sources is greatly improved [13]. The heating radius of each heat source is not very large, therefore the joint heating system keeps a relative lower power consumption. The traditional heating system usually has to be shut down when fault occurs. By contrast, the joint heating system has higher reliability. When fault occurs in some heat source or pipe, only the fault point need to be shut down and repaired, while other parts are in normal operation. In addition, the multi-heat source system is much more convenient to be made capacity expansion renovation. In the smart district heating system, the users have permission to adjust heat load to their own needs. However, the adjustment by users is of great randomness, resulting in frequent fluctuation in the whole system heat load. Multi-heat source system is more conducive to the overall or local regulation of heat load. The heat load of each heat source is optimized by the intelligent control system in order to adapt to the load variation of the whole network, and realize the good energy saving. 3.4. Household intelligent control system There are few measure and control equipment unit installed in end users. When the room temperature does not meet the requirements, the user can not adjust temperature independently. When no man stays in the room for a long time, room heating can not be shut down or reduced, resulting in energy waste. With the intelligent measure and control unit in the household, it creates conditions for the household heat-metering and charging which promotes active energy saving for users. Fig. 4 illustrates the schematic diagram of household intelligent control system. Based on this, energy saving regulation integrated by intelligent center system control and user active control is established and users could take responsibility on heating adjustments and optimization with the help of apps that run on smartphones, tablets and PCs. For instance, people could shut down heating when he leaves home or turn on heating before he returns home by the smartphone. There are individual differences in the comfort of the environment, person can adjust the heat supply according to his own need. The intelligent control system compares real-time room temperature with the user preset value, then automatically adjust the water flow. Heating time, heat load and room temperature preference are collected and upload to database, the control center in turn could predict and pre adjust the heat load according to user’s heating characteristic.
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Fig. 4. Household intelligent control system
4. Summary The smart district heating system is just in its start-up step in China, only local automatic control and unattended operation are realized in some heating enterprise. The smart DH system is based on integration of the state-of-art technology and theoretical research. With the development of technology, more novel elements would be added into the concept of smart DH system. Related research and application of new technologies could be independently performed, and gradually integrated together. The smart DH system will bring about change in district heating, as well as in production mode and management mode of heating enterprise. In addition, the users may change their old energy usage habit and save energy actively. The automatic and precise regulation by the smart DH system would saves manpower resource, material resource and financial resource for heating enterprise, which is important to increase enterprise efficiency, and enhance the competitiveness of enterprise. References [1]. Brand M, Svendsen S. Renewable-based low-temperature district heating for existing buildings in various stages of refurbishment. Energy 2013; 62:311-319. [2]. Rezaie B, Rosen M A. District heating and cooling: Review of technology and potential enhancements. Applied Energy 2012; 93:2-10. [3]. Lund H, Werner S, Wiltshire R, et al. 4th Generation District Heating (4GDH). Energy 2014; 68:1-11. [4] Grosswindhager S, Voigt A, Kozek M. Predictive control of district heating network using fuzzy DMC. Proceedings of International Conference on Modelling, Identification & Control. IEEE 2012:241-246. [5] Chmielnicki W J. Application of Neural Networks for Control of District Heating \ Wykorzystanie Sieci Neuronowych Do Regulacji W Ciepłownictwie. Archives of Civil Engineering 2010; 56:219-238. [6] Liu W , Yang J, Lin Y. An intelligent control method on district heating network. Journal of Dalian University of Technology, 2004. [7] Li Z, Wang G. Schema theory and human simulated intelligent control. IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. IEEE, 2003:524-530 vol.1. [8] Koščák J, Jakša R, Sepeši R, et al. Daily Temperature Profile Prediction for the District Heating Application. Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems. Springer International Publishing, 2013:363-371. [9] Dostál I P, Chramcov I B, Ing P, et al. Soft Computing and Control of District Heating System. 2003:480-484. [10]. Dalipi F, Yayilgan S Y, Gebremedhin A. A Cloud Computing Framework for Smarter District Heating Systems. Ufirst 2015: the, IEEE International Symposium on Ubicom Frontiers - Innovative Research, Systems and Technologies. IEEE, 2015. [11] Chen T, Zhang J, Zhao T, et al. Development Status of Dynamic Distributed Transmission and Distribution System in Chinese. Procedia Engineering, 2015, 121:2083-2090. [12] Sheng X, Lin D. Electricity consumption and economic analyses of district heating system with distributed variable speed pumps. Energy & Buildings, 2016, 118:291-300. [13] Li A, He S, Zhang X. Dispatching management of interconnected multi-heat-source heating systems: a regulation analysis for central heating system in Baoji. Heating Ventilating & Air Conditioning, 2008.
ScienceDirect Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2017) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia
Energy Procedia 142 Energy Procedia 00(2017) (2017)1829–1834 000–000 www.elsevier.com/locate/procedia
9th International Conference on Applied Energy, ICAE2017, 21-24 August 2017, Cardiff, UK
Technologies in Smart District Heating System a
The 15th International Symposium on District Heating and Cooling
Lin Gao , Xuyang Cuia, Jiaxin Nia, Wanning Leib, Tao Huangb, Chao Baib, Junhong a, Assessing the feasibility of using Yang * the heat demand-outdoor
Key Laboratory of Efficient Utilization offor Low and Grade Energy,MOE ，School heat of Mechanical Engineering， Tianjin temperature function a Medium long-term district demand forecast aa
a,b,c
I. Andrić
University,Tianjin 300350,China； Energy Group Xian Raising a a co.LTD,Xian 710100,Shanxi,China b
b bXi
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrièrec, O. Le Correc
a
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal Abstract b c
Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
The smart district heating (DH) system has greater advantages than the traditional heating system in many aspects (e.g., energy saving, regulation and control, troubleshooting), and it shows great development potential and broad market prospect in the future. This paper elaborates the concept of the smart DH system, as well as the corresponding function of each component. Technologies Abstract that may be well integrated into this intelligent system are introduced and evaluated. The future development of the smart system is briefly analyzed. ©District 2017 The Authors. Published by Elsevier Ltd. heating networks are commonly in the literature as one of the most effective solutions for decreasing the © 2017 The Authors. Published by Elsevier addressed Ltd. Peer-review under responsibility scientific committee of the 9th International Conference on Applied Energy. greenhouse under gas emissions from of thethe building sector. These of systems high investments are returned Peer-review responsibility of the scientific committee the 9threquire International Conferencewhich on Applied Energy.through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, Keywords: District heating; Smart thermal grids; Intelligent control prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 1.buildings Introduction that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were During with the urbanization increases, but the corresponding building energy consumption stays compared results from aprocess, dynamic the heatcity-size demand model, previously developed and validated by the authors. results showed whenenergy only weather change is considered, the margin couldenergy be acceptable for somehowever, applications atThe a high level. The that heating consumed is the most important part of of error building consumption, it annual demand thanenergy 20% forsaving all weather scenarios considered). However, after introducing renovation is(the alsoerror the in most wasted and was has lower the most potential. Studies concluded that district heating (DH) plays therole errorinvalue increased up to 59.5% (depending on the weather and renovation anscenarios, important the implementation of future sustainable energy systems [1,2]. scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the With the increase of the heat user’s number and heating network scale, the traditional heating system could not decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and satisfy the current urban heating demand, and the shortcomings become prominent. The increasing degree of renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the deregulation of the energy market and the increasing focus on energy efficient buildings place a demand on DH coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and systems more of efficient than estimations. ever. The development of the technology provides opportunity and change for improve to thebe accuracy heat demand © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Cooling. E-mail address: [email protected]
Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy.
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 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy. 10.1016/j.egypro.2017.12.571
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Lin Gao et al. / Energy Procedia 142 (2017) 1829–1834 Author name / Energy Procedia 00 (2017) 000–000
district heating, many new concepts are presented and applied to realize the “city smart network”, aiming at energy saving and improvement of the comfort degree. A concept of 4th Generation District Heating (4GDH) including the relations to District Cooling and the concepts of smart energy and smart thermal grids was defined in [3]. The future DH system should fulfill the goals like supplying low-temperature district heating for space heating and domestic hot water, integrating renewable heat sources, and operation planning. Besides, the smart DH system may work as an integrated part of smart energy systems, i.e., integrated smart electricity, gas, fluid and thermal grids. This work aims to describe the concept and framework of the smart district heating system. Some related technologies that may be well integrated with the smart system are also introduced. 2. Concept and structure of smart DH system The concept of smart thermal grids can be regarded as being parallel to smart electricity grids, and it focuses on the integration and efficient use of potential future renewable energy resources as well as the operation of a grid structure allowing for distributed generation which may involve interaction with consumers[3]. Through the information network, different parts (thermal source, network of pipes, substation, heat user) are connected together and integrated into a long-distance management controlled and intelligent system, i.e., the smart district heating system. A series of measures are adopted to improve the reliability, safety and efficiency of the heating system, and the dynamic optimizations of heating system operation, maintenance and planning could be realized. The system obtains the real time data of different parts and builds a running database, in order to realize the storage and analysis of all the information on a uniform management platform. Based on the smart district heating system, the smart forecasting of load, the smart regulation of heat, the smart optimization of scheduling and the smart diagnosis of fault could be realized. Fig. 1 illustrates the schematic diagram of the smart district heating system. The smart system consists of control center, communication network, geographic information system (GIS), supervisory control and data acquisition system (SCADA). The control center which is the core of the smart thermal grids system takes charge of the monitoring, control and management of the whole system running. SCADA system acquires and supervises the realtime condition of the heat users. The communication network connects each instrument together to the control center, in order to ensure the real-time transmission of data and instructions. GIS system provides the position data of the heating network. Besides, a powerful software system named intelligent control system is established to supervise and control the hardware equipment.
Fig. 1. Schematic diagram of the smart DH system
Through the cooperative work of each section, the smart district heating system has characteristics of high efficiency, energy-saving, real-time, reliability and forward looking. In view of the real-time supervision, the data of control center is synchronized with the operational data, which could detect the fault in the first time and avoid the leak and steal of the steam. Via the simulating operation and analysis of heating network, the optimized running condition could be confirmed, which avoids the thermodynamic imbalance and hydraulic imbalance of the heating
Lin Gao et al. / Energy Procedia 142 (2017) 1829–1834 Author name / Energy Procedia 00 (2017) 000–000
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network. The smart system could automatically adjust the temperature of water supply according to the outside temperature, which saves energy consumption and improve the heating quality. 3. Innovations and new techniques in smart DH system Due to the development of the computer technology, communication technology and automatic control technology, the district heating system is becoming smarter. Some new technologies as well as new control strategies are applied to the smart heating system. As energy management and control regulation become more precise, the operation of multi-source network and distributed variable frequency pump becomes more possible. With the popularization of intelligent control in the end users, the heat load regulation strategy could turn from unified control of whole system into joint regulation of heat source, substation and heat user, which makes it possible to be adjusted to the user’s own need. 3.1. Intelligent control system Compared with the traditional heating system, the smart district heating system has an intelligent control system which could constantly monitor, control and manage the whole heating system to ensure high effectiveness and reliability. The intelligent control system serves as the brain of the smart system, and it imitates the human brain in the unknown environment in order to achieve intelligent management and operation. The intelligent control system includes three parts: dispatching and management system, on-line energy consumption analysis system, accident alarm system. The control center remotely real-time monitors and controls each substation. Operation parameters are real-time collected by diverse sensors on site, and then sent back to the control center. The dispatching and management system make a trend prediction based on the data analysis, and a feedback is made to control the substation. The on-line energy consumption analysis system analyzes the collected data and compares them with the historical database. The operation condition of each substation is evaluated, and high energy consumption processes could be detected and regulated. Heat load could be pre adjusted based on the climate and characteristic of heating supply in different periods. For example, corresponding relationship between heating temperature and outside temperature is established. As a result, optimal heat load could be computed in different weather, which achieves load pre-regulation and improves user’s comfort. The accident alarm system could quickly detect accident. Combined with the GIS system, the accident point location could be achieved exactly, which ensure the heating system safety and avoid steam steal or leakage. As the network and the buildings have great thermal inertia, there is a large lag in control. It is difficult for a common control system to achieve exact thermal match between supply and requirement. Besides, the heating network is a multi-input multi-output coupled nonlinear system whose dynamic characteristics vary largely with operating conditions. Many segments have great inertia, pure time-delay, non-linear and model uncertainty. For purpose of heat supply-demand balance and hydraulic balance of heating system, the intelligent control system requires accuracy, robustness, real-time and fault tolerance. The control methods include fuzzy control [4], neural network control [5], expert control [6], human-simulated intelligent control [7] and hybrid control. For example, with respect to the uneven distribution and strong stochastic of DH system, expert control strategy could improve the heating efficiency. The fuzzy control method can be used to identify the time lag in the system. However, a single control method usually has the limitation of application. Several intelligent control methods are usually integrated together to form computational intelligence [8,9], which is close to the human brain. More and more attention has been paid to the integrated intelligent system based on computational intelligence. Hybrid intelligent control based on conventional control and intelligent control is a widely used method, for instance, fuzzy technology, neural network and genetic algorithm are integrated and form a new hybrid intelligent system. The integrated intelligent system has simple structure, good universality and expansibility. Besides, it can improve the reliability and real-time performance while realizing complex function. With respects to the large time lag, the predictive control algorithm is applied in DH system. It has good tracking performance, requires less model accuracy and is suitable for controlling large time-delay object. Thus, it is paid more attention on complex industrial process control.
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Lin Gao et al. / Energy Procedia 142 (2017) 1829–1834 Author name / Energy Procedia 00 (2017) 000–000
Since in DH network there are a large number of intelligent devices, managing a huge amount of real-time information sent and received from these devices is a challenging task, as shown in Fig. 2. Therefore, cloud computing is a suitable solution to meet these demands and to solve the issue of information storage. A framework based on Cloud technology for big data information management in DH network, was introduced in [4]. The framework could make a platform to store, access and share heterogeneous energy information.
Fig. 2. Information flow in DH network
3.2. DH system with distributed variable-frequency pump With the expansion of the city district heating scale, it usually takes hundreds minute for hot water to circle the whole network, therefore the measured temperature of return water lags behind the adjusted temperature by users. The control of circulating water pump based on return water temperature can not timely meet the user’s heat requirement. If circulating water pump makes adjustment, the control effect shows until the hot water circles and reaches the data collection point. The time lag can be eliminated by changing the heating mode. Besides, there is excessive available head pressure in traditional heating system. Regulating valve must be installed in household water supply line to eliminate the excess head pressure, which induces low efficiency of water pump. Without efficient regulation, the near-end users usually have surplus water flow, however the far-end users have insufficient water flow, which leads to uneven cold and hot in heating system. With the development of frequency conversion variable speed technology, the concept that using variable-frequency pump to adjust water flow instead of the valve is presented [11,12], as shown in Fig. 3. Heating system with distributed variable-frequency pump is flexible to adjust and suitable for metering heating. This new network and the method used to control the flow rate of the primary supply water and its temperature are changed according to the outdoor temperature. The correct temperature level of the supply temperature is fixed as functions of the outdoor temperature. It is showed that the new DH system could save 49.41% of the average electrical energy compared with heating system conventional central circulating pump [12].
Lin Gao et al. / Energy Procedia 142 (2017) 1829–1834 Author name / Energy Procedia 00 (2017) 000–000
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Fig. 3. Schematic diagram of DH system with distributed variable-frequency pump
The end-user needs to add a booster pump in case the main circulating pump is laid out to the second most adverse loop pipe network. Compared with heating system with conventional circulating pump, the district heating system with distributed variable-frequency pumps is provided with an appropriate node in the pipe network as the pressure difference control point; therefore, the main circulating pump head only overcomes the resistance in the main pipe, while the respective pumps take charge of the pressure head from respective users. 3.3. Multi-heat source joint heating system The traditional DH system usually has one heat source which supplies heat to one district. This system has many stand-by equipment, and the boiler can not run at full capacity, resulting in low thermal efficiency. The district heating system with multi-heat source could efficiently solve the aforementioned problem. With the help of the intelligent control center, the coordination and matching between multiple heat sources will be better controlled by the intelligent control center. The multi-heat source heating allows for the wide use of heat from waste-to-energy and various industrial surplus heat sources as well as the inclusion of geothermal and solar energy. Several heat sources, which include a main heat source with maximum heat capacity and some auxiliary heat sources, simultaneously supply heat to the thermal grids in a multi-heat source joint heating system. During whole heating period, the main heat source keeps full load operation, while auxiliary heat sources are regulated by turns to meet different thermal loading requirement of users, in this way, the average efficiency of heat sources is greatly improved [13]. The heating radius of each heat source is not very large, therefore the joint heating system keeps a relative lower power consumption. The traditional heating system usually has to be shut down when fault occurs. By contrast, the joint heating system has higher reliability. When fault occurs in some heat source or pipe, only the fault point need to be shut down and repaired, while other parts are in normal operation. In addition, the multi-heat source system is much more convenient to be made capacity expansion renovation. In the smart district heating system, the users have permission to adjust heat load to their own needs. However, the adjustment by users is of great randomness, resulting in frequent fluctuation in the whole system heat load. Multi-heat source system is more conducive to the overall or local regulation of heat load. The heat load of each heat source is optimized by the intelligent control system in order to adapt to the load variation of the whole network, and realize the good energy saving. 3.4. Household intelligent control system There are few measure and control equipment unit installed in end users. When the room temperature does not meet the requirements, the user can not adjust temperature independently. When no man stays in the room for a long time, room heating can not be shut down or reduced, resulting in energy waste. With the intelligent measure and control unit in the household, it creates conditions for the household heat-metering and charging which promotes active energy saving for users. Fig. 4 illustrates the schematic diagram of household intelligent control system. Based on this, energy saving regulation integrated by intelligent center system control and user active control is established and users could take responsibility on heating adjustments and optimization with the help of apps that run on smartphones, tablets and PCs. For instance, people could shut down heating when he leaves home or turn on heating before he returns home by the smartphone. There are individual differences in the comfort of the environment, person can adjust the heat supply according to his own need. The intelligent control system compares real-time room temperature with the user preset value, then automatically adjust the water flow. Heating time, heat load and room temperature preference are collected and upload to database, the control center in turn could predict and pre adjust the heat load according to user’s heating characteristic.
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Author Energy Procedia 00 (2017) Linname Gao / et al. / Energy Procedia 142 000–000 (2017) 1829–1834
Fig. 4. Household intelligent control system
4. Summary The smart district heating system is just in its start-up step in China, only local automatic control and unattended operation are realized in some heating enterprise. The smart DH system is based on integration of the state-of-art technology and theoretical research. With the development of technology, more novel elements would be added into the concept of smart DH system. Related research and application of new technologies could be independently performed, and gradually integrated together. The smart DH system will bring about change in district heating, as well as in production mode and management mode of heating enterprise. In addition, the users may change their old energy usage habit and save energy actively. The automatic and precise regulation by the smart DH system would saves manpower resource, material resource and financial resource for heating enterprise, which is important to increase enterprise efficiency, and enhance the competitiveness of enterprise. References [1]. Brand M, Svendsen S. Renewable-based low-temperature district heating for existing buildings in various stages of refurbishment. Energy 2013; 62:311-319. [2]. Rezaie B, Rosen M A. District heating and cooling: Review of technology and potential enhancements. Applied Energy 2012; 93:2-10. [3]. Lund H, Werner S, Wiltshire R, et al. 4th Generation District Heating (4GDH). Energy 2014; 68:1-11. [4] Grosswindhager S, Voigt A, Kozek M. Predictive control of district heating network using fuzzy DMC. Proceedings of International Conference on Modelling, Identification & Control. IEEE 2012:241-246. [5] Chmielnicki W J. Application of Neural Networks for Control of District Heating \ Wykorzystanie Sieci Neuronowych Do Regulacji W Ciepłownictwie. Archives of Civil Engineering 2010; 56:219-238. [6] Liu W , Yang J, Lin Y. An intelligent control method on district heating network. Journal of Dalian University of Technology, 2004. [7] Li Z, Wang G. Schema theory and human simulated intelligent control. IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. IEEE, 2003:524-530 vol.1. [8] Koščák J, Jakša R, Sepeši R, et al. Daily Temperature Profile Prediction for the District Heating Application. Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems. Springer International Publishing, 2013:363-371. [9] Dostál I P, Chramcov I B, Ing P, et al. Soft Computing and Control of District Heating System. 2003:480-484. [10]. Dalipi F, Yayilgan S Y, Gebremedhin A. A Cloud Computing Framework for Smarter District Heating Systems. Ufirst 2015: the, IEEE International Symposium on Ubicom Frontiers - Innovative Research, Systems and Technologies. IEEE, 2015. [11] Chen T, Zhang J, Zhao T, et al. Development Status of Dynamic Distributed Transmission and Distribution System in Chinese. Procedia Engineering, 2015, 121:2083-2090. [12] Sheng X, Lin D. Electricity consumption and economic analyses of district heating system with distributed variable speed pumps. Energy & Buildings, 2016, 118:291-300. [13] Li A, He S, Zhang X. Dispatching management of interconnected multi-heat-source heating systems: a regulation analysis for central heating system in Baoji. Heating Ventilating & Air Conditioning, 2008.