- Email: [email protected]
Simulation of heating loads and heat pump loads of a typical suburban residential building of Beijing, China in wintertime
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
ScienceDirect ScienceDirect
Energyonline Procedia (2018) 000–000 Available onlineat00 atwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy (2018) 000–000 348–353 EnergyProcedia Procedia152 00 (2017) www.elsevier.com/locate/procedia
Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems, Applied Energy Symposium andSymposium Forum 2018: Low carbon cities and urbancities energy systems, CUE2018-Applied Energy and Forum 2018: Low carbon and CUE2018, 5–7 June 2018, Shanghai, China 5–7 June Shanghai, ChinaChina urban CUE2018, energy systems, 5–7 2018, June 2018, Shanghai,
Simulation of heating loads and heat pump loads of a typical Simulation of heating loads and heat pump loads a typical The 15th International Symposium on District Heating andof Cooling suburban residential building of Beijing, China in wintertime suburban residential building of Beijing, China in wintertime Assessing the feasibility of using the heat demand-outdoor Xiaoling Yuaa, Qian Lvaa, Yifeng Dingbb, Shuo Yangbb, Liming Jiangcc, Liwen Jinaa Xiaoling Yu ,function Qian Lv , Yifeng Ding , Shuo Yang , Liming , Liwenforecast Jin temperature for a long-term district heatJiang demand Xi’an Jiaotong University, Xi’an, 710049, China. a
a Xi’anElectric Jiaotong University, Xi’an, 710049, China. State Grid Beijing Power Research Institute, Beijing, 100075, China. a,b,c bState cGrid a Electric a Research bBeijing, c Institute, 100075, ChinaBeijing Electric PowerPower Research Institute, Beijing, 100192, China.China. c c China Electric Power Research Institute, Beijing, 100192, China. a 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 Abstract c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract
I. Andrić
b
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
In recent years, Northern China suffers severe winter haze, which is attributed to extensive residential coal combustion for warm In Northern China suffers severe winterof haze, is attributed to extensive residential coal to combustion warm in recent winter.years, Chinese government presented proposals the which electrical energy substitute of the fossil fuels solve the for problem. in winter. the Chinese of the electrical energy substitute of electrical the fossilgrids fuels on to the solve the problem. However, wide government applicationspresented of electricproposals heating utilities expose the reliability of the production and Abstracttheside However, wide applications of electric heating exposewethestudied reliability of the electrical gridsload on the production transmission particularly in extreme cold days. utilities In this paper, heating loads and electric behaviors of anand air transmission side particularly in extreme cold days. Inbuilding this paper, we studied heating loads andinelectric load behaviors of an air source heat pump of a case-study suburban residential of Beijing, China in wintertime 2016. Firstly, we choose three District heating areloads commonly addressed in the literature as one themedium most effective solutions for the source heat pump offora case-study suburban residential building of warmest, Beijing, China in wintertime in Secondly, 2016. Firstly, wedecreasing choose characteristic daysnetworks heating analysis, i.e. the coldest, the andofthe day. heating loads ofthree the greenhouse gas from theby building sector. These systems require high investments which are returned through the heat characteristic daysemissions for heating loads analysis, i.e. the coldest, the warmest, and the medium heating loadsand of the case-study building were calculated a building energy simulation program, which is basedday. on aSecondly, coupled weather data sales. Due to transfer. the were changed climate building renovation demand indays the were future could decrease, case-study building calculated byconditions a building energy simulation program, which ischaracteristic based on a coupled weather databased and the building energy Hourly heating loads of and the case-study building inpolicies, the threeheat studied on prolonging the investment return period. building energy transfer. Hourly the case-study building the pump three characteristic werewere studied based on the weather analyses. Thirdly, theheating electricloads load of behaviors of the air sourceinheat used for roomdays heating investigated. The mainofscope of this paper is assess the feasibility ofofusing the source heat demand – outdoor temperature function forinvestigated. heat demand the weather analyses. Thirdly, thetodifferent electric load behaviors thewas air heat The pump used for and room were The COP the heat pump under operating conditions calculated. peak loads theheating electric load fluctuations of Alvalade, located in This Lisbon (Portugal), used asThe a case study. Thethe district consisted of for 665 The COP ofThe the district heat underwere different operating conditions was was calculated. peak on loads and electric load fluctuations offorecast. the heat pump in pump wintertime analyzed. work provided a valuable database heating loads andis electric loads buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district of the inheat pump area in wintertime work a valuable warm suburban in Beijing,were so asanalyzed. to help toThis achieve theprovided electric grid safety. database on heating loads and electric loads for renovation scenarios were developed (shallow, intermediate, deep).grid Tosafety. estimate the error, obtained heat demand values were warm in suburban area in Beijing, so as to help to achieve the electric compared with results from a dynamic heat demand model, previously developed and validated by the authors. Copyright © 2018 Elsevier Ltd. All rights reserved. Copyright © 2018 2018 Elsevier Elsevier Ltd. All All rights rights reserved. The results showed that when only weather change is considered, the margin of error could Symposium be acceptable forForum some 2018: applications Copyright © Ltd. reserved. Selection and peer-review under responsibility of the scientific committee of Applied Energy and Low Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy Symposium and (the error inand annual demand was lowerCUE2018. than 20% all weather scenarios considered). However, after introducing renovation Selection and peer-review under responsibility of thefor scientific committee of Applied Energy Symposium and Forum 2018: Low carbon cities urban energy systems, Forum 2018:the Low carbon cities and urban energy systems. scenarios, error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). carbon cities and urban energy systems, CUE2018. The value of slope increased average Electric within loads; the range of 3.8% up to 8% per decade, that corresponds to the Keywords: Heating loadscoefficient of building;Heating loadon simulation; Heat pump decreaseHeating in the loads number of heating hours 22-139h Electric during loads; the heating season (depending on the combination of weather and Keywords: of building;Heating loadofsimulation; Heat pump renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and 1.coupled Introduction improve the accuracy of heat demand estimations. 1. Introduction © 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. Tel.: +86-29-82664844; fax: +86-29-82668724. Cooling. * Corresponding Tel.: +86-29-82664844; fax: +86-29-82668724. E-mail address:author. [email protected]
E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the Applied Energy Symposium and Forum 2018: Low carbon cities Selection and peer-review under responsibility the scientific Selection peer-review responsibility of the scientific committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urbanand energy systems, under CUE2018. and urban energy systems, CUE2018. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems. 10.1016/j.egypro.2018.09.154
2
Xiaoling Yu et al. / Energy Procedia 152 (2018) 348–353 Xiaoling Yu / Energy Procedia 00 (2018) 000–000
349
In recent years, most region of China, particularly Northern China suffers severe and persistent winter haze characterized thick fog and high PM2.5 mass concentration. Previous studies show that the severe winter haze of Northern China attributed to extensive residential coal combustion for warm in winter[1]. In 2013, Chinese State Grid presented proposals of the electrical energy substitute of the fossil fuels[2]. For the design of electrical heating for warm of a building, the first task is calculating the heating loads of the building, which are dominated by the outdoor climate conditions, external wall configuration, and insulation layer thickness. The heating loads of the building can be calculated by the dynamic thermal balance models, which were based on a coupled weather data and the building energy transfer[3]. The heating loads of a building can be accurate evaluated by several commercial software such as EnergyPlus[4] and eQUEST[5]. Crawley et al performed a survey of these software and their capabilities[6]. According to proposals of the electrical energy substitute of the fossil fuels, all heating loads of a building should be supplied by electric heating utilities, sometimes together with energy storage system[7, 8]. In various kinds of electric heating utilities, the heat pump is the most energy saving. The electric load of the heat pump is shifting with the outdoor climate to maintain 18-23 ℃ indoor air temperature. There have been many papers published, focusing on load shifting of heat pumps[9, 10]. In this article, firstly, the heating demands of a typical suburban residential building in Beijing, China were analyzed during the whole winter based on the weather database research. Secondly, the electric loads shifting of an air source heat pump was modeled according to the change of the heating demands. This work is aimed to characterize hour-to-day electric load feature of the building, and is helpful to forecast the electric load shifting, thus to improve the electric grids safety. 2. Heating loads of a typical suburban residential building in Beijing 2.1. The case-study building configuration As shown in Fig.1, our study is based on a typical suburban residential building in Beijing. The case-study building has single storey living areas above ground level and it is constructed with brick-concrete framework. It has total area of 495m2, and contains two courtyards, two living rooms, a kitchen, two bathrooms, and five bedrooms. Its key construction and thermal characteristics are presented in Table 1. Table 1 Thermal characteristics of the case-study building. Elements
Material
Heat transfer coefficients(W/m2K)
Windows
Clear glass windows
2.6
Doors
Clear glass windows in doors
4.5
Floors
Concrete+mineral wool
0.35
Roofs
Gypsum board
0.65
Façade walls
360 mm brick+ concrete+50mm gypsum board
0.903
Intermediate walls
240 mm brick+ concrete+50mm gypsum board
3.8
Through the use of a building energy simulation program, we studied the heating demands of the case-study building during the winter from 15th Nov. 2016 to 15th Mar. 2017. We presented the hourly heat loads of the casestudy building in three characteristic days.
3
Xiaoling Yu / Energy Procedia 00 (2018) 000–000 Xiaoling Yu et al. / Energy Procedia 152 (2018) 348–353
350
29.9m Bathroom1
Bedroom1 Bedroom2 Bedroom3
Bedroom4
Storeroom2
Bedroom5
Kitchen
Livingroom1
Storeroom1
North courtyard
South courtyard
16.5m
Livingroom2
Bathroom2
Fig.1 the case-study building.
2.2. Heating loads of the case-study building The three characteristic days are the coldest day with outdoor daily mean temperature of -7 ℃ (30th Jan. 2017), the warmest day with outdoor daily mean temperature of 12 ℃ (11th Mar. 2017), and the medium day with outdoor
0
2
4
6
8
10 12 14 16 18 20 22 Time
Fig. 2
Heating loads Outdoor temperature
2
4
6
8
10 12 14 16 18 20 22 Time
Fig. 3
22 20 18 16 14 12 10 8 6 4 2 0 -2 -4 -6 -8 -10 -12 -14
38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0
Heating loads Outdoor temperature
22 20 18 16 14 12 10 8 6 4 2 0 -2 -4 -6 -8 -10 -12 -14
Temperature /℃
38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0
Heating loads/kW
26 24 22 20 18 16 14 12 10 8 6 4 2 0 -2 -4 -6 -8 -10 -12 -14
Temperature /℃
Heating loads Outdoor temperature
Heating loads/kW
40 38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0
Temperature /℃
Heating loads/kW
daily mean temperature of -2 ℃ (neither cold nor warm, 11th Dec. 2016). The hourly heating loads of the case-study building and the outdoor hourly temperatures in the three characteristic days were shown in Fig.2~4. From these figures, we can see the heating demand fluctuations in one day, as well as during the whole winter. It was found that in the warmest day the heating loads was 0-16 kW, while in the coldest day the heating loads was 22-36 kW. In the medium day, which is the most frequent in the wintertime, the heating loads were 20-30 kW. The hourly heating loads changes according to outdoor hourly temperatures, and it stays on the peak during the night-time hours (approximately 2:00 to 7:00), and goes down to its lows during the daytime hours (approximately 13:00 to 15:00). The heating loads wave gently during the night-time, while have relative large fluctuation between the time period of 6:00 AM to 14:00 PM.
0
2
4
6
8
10 12 14 16 18 20 22 Time
Fig. 4
Fig. 2 Hourly heating loads and outdoor hourly temperatures in the warmest day (11th Mar. 2017) Fig. 3 Hourly heating loads and outdoor hourly temperatures in the coldest day (30th Jan. 2017)Fig. 4 Hourly heating loads and outdoor hourly temperatures in the medium day(11th Dec. 2016)
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Xiaoling Yu et al. / Energy Procedia 152 (2018) 348–353 Xiaoling Yu / Energy Procedia 00 (2018) 000–000
351
3. Electric loads shifting of an air source heat pump in winter 3.1. Heat pump system configuration The bedroom 1 of the case-study building is heated by an air source heat pump. The indoor temperature is set to be 20 ℃. The heat pump system is illustrated in Fig.5, and P-h diagram of refrigerant cycle is shown in Fig.6. Parameters of the heat pump were presented in Table 2.
A-the first stage compressor, B- the second stage compressor, C-condenser, G- throttle valve 1, F- economizer, H- throttle valve 2, E-evaporator Fig.5 Heat pump system
Fig.6 P-h diagram of refrigerant cycle Table 2 Parameters of the heat pump Parameters
Settings
Refrigerant
R410A
Maximum pressure in the condenser
4.3 MPa
Maximum pressure in the evaporator
2.5 MPa
Temperature difference between the cold fluid and the hot fluid in the exchanger
10 ℃
Degree of supercooling
5℃
Degree of superheating
5℃
Compressor efficiency
90%
Electric motor efficiency
90%
5
Xiaoling Yu / Energy Procedia 00 (2018) 000–000 Xiaoling Yu et al. / Energy Procedia 152 (2018) 348–353
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3.2. COP and electrical loads analysis of the heat pump The effect of varying environment temperature over COP of the heat pump is investigated. The evaporating temperature of the heat pump is related to the environment temperature. It was found that COP of the heat pump increases with the environment temperature in Fig.7. The water flowing through the condenser absorbs heat in the condenser and then warms the room. The temperature of the water at inlet of the condenser (Twater-in) and at outlet of the condenser (Twater-out) can be adjusted. It can be seen in Fig.7 that when temperature of the hot water in the condenser is increased, COP of the heat pump decreases.
Fig.7 Effect of varying environment temperature and hot water temperature on the COP
Fig.8 is the electric load shift of the heat pump in the three characteristic days when the indoor temperature of the bedroom 1 is maintained as 20 ℃. From Fig.8, we can see the electric load fluctuations in one day, as well as during the whole winter. It is clearly seen that the electric load of the heat pump increases with the decrease of the outdoor temperature. For instance, under operating conditions of Twater-in=50 ℃ and Twater-out=55 ℃, the hourly electric load fluctuates in 400 W~800 W in the coldest day, 300 W~600 W in the medium day, and 0W~250 W in the warmest day. At the same outdoor temperature, the electric load of the heat pump increases with temperature of the produced hot water. As the hourly heating loads do, the hourly electric load changes according to outdoor hourly temperatures, and it stays on the peak during the night-time hours (approximately 2:00 to 7:00), and goes down to its lows during the daytime hours (approximately 13:00 to 15:00). The electric load shifting data provide a valuable information for forecasting electric loads for warm of the building and even the city, so as to help to achieve the electric grid safety.
(a)
(b)
(c)
Fig.8 The electric load shift of the heat pump in the three characteristic days. (a) the coldest day with outdoor daily mean temperature of -7 ℃(30th Jan. 2017), (b) the warmest day with outdoor daily mean temperature of 12 ℃(11th Mar. 2017), and (c) the medium day with
6
Xiaoling Yu / Energy Procedia 00 (2018) 000–000 Xiaoling Yu et al. / Energy Procedia 152 (2018) 348–353 outdoor daily mean temperature of -2 ℃(neither cold nor warm, 11th Dec. 2016).
353
4. Conclusions In this study, we have endeavoured to identify heating load and electric load shifting of a case-study suburban residential building of Beijing, China during the wintertime in 2016. Heating loads of the case-study building were calculated by a building energy simulation program. Hourly heating loads of the building in the three characteristic days were studied, and the peak/lows of the heating loads and their time period were determined. The electric load fluctuations of an air source heat pump used for heating a bedroom of the building were investigated. The energy analyses of the heat pump cycle were presented. The COP of the heat pump under conditions of varying outdoor temperatures and varying temperatures of the produced hot water was calculated. The hourly electric loads of the heat pump in the three characteristic days were presented. This study provided a valuable database on heating loads and electric loads for warm in suburban area in Beijing, so as to help to achieve the electric grid safety. Acknowledgements This work is supported by the National Science Foundation of China [No.51676150]. The authors are grateful for the support by the project named of " Heating load analyses of typical suburban residential buildings of Chinese different regions (5202011600U4)" in China State Grid Corp. References [1] P. Liu, C. Zhang, C. Xue, Y. Mu, J. Liu, Y. Zhang, D. Tian, C. Ye, H. Zhang, J. Guan, The contribution of residential coal combustion to atmospheric PM2. 5 in northern China during winter, Atmospheric Chemistry & Physics, 17 (2017) 1-37. [2] Chinese Environmental Protection online (Accessed at http://www.hbzhan.com/news/detail/83229.html). [3] L.E. Ortiz, J.E. Gonzalez, E. Gutierrez, M. Arend, Forecasting Building Energy Demands With a Coupled Weather-Building Energy Model in a Dense Urban Environment, Journal of Solar Energy Engineering, 139 (2016) 011002-1-011002-8. [4] D.B. Crawley, L.K. Lawrie, F.C. Winkelmann, W.F. Buhl, Y.J. Huang, C.O. Pedersen, R.K. Strand, R.J. Liesen, D.E. Fisher, M.J. Witte, EnergyPlus: creating a new-generation building energy simulation program, Energy & Buildings, 33 (2001) 319-331. [5] J. J. Hirsch, eQuest, The QUick Energy Simulation Tool, 2006, DOE2 Com, epub, http://www.doe2.com/equest/. [6] D.B. Crawley, J.W. Hand, M. Kummert, B.T. Griffith, Contrasting the capabilities of building energy performance simulation programs, Building & Environment, 43 (2008) 661-673. [7] X. Yang, S. Feng, Q. Zhang, Y. Chai, L. Jin, T.J. Lu, The role of porous metal foam on the unidirectional solidification of saturating fluid for cold storage, Applied Energy, 194 (2017) 508-521. [8] X. Yang, L. Zhao, Q. Bai, Q. Zhang, L. Jin, J. Yan, Thermal performance of a shell-and-tube latent heat thermal energy storage unit: Role of annular fins, Applied Energy, 202 (2017) 558-570. [9] J. Allison, A. Cowie, S. Galloway, J. Hand, N.J. Kelly, B. Stephen, Simulation, implementation and monitoring of heat pump load shifting using a predictive controller, Energy Conversion & Management, 150 (2017) 890-903. [10] C. Han, K.M. Ellett, S. Naylor, X. Yu, Influence of Local Geological Data on the Performance of Horizontal Ground-coupled Heat Pump System Integrated with Building Thermal Loads, Renewable Energy, 113 (2017) 10461055.
ScienceDirect ScienceDirect
Energyonline Procedia (2018) 000–000 Available onlineat00 atwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy (2018) 000–000 348–353 EnergyProcedia Procedia152 00 (2017) www.elsevier.com/locate/procedia
Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems, Applied Energy Symposium andSymposium Forum 2018: Low carbon cities and urbancities energy systems, CUE2018-Applied Energy and Forum 2018: Low carbon and CUE2018, 5–7 June 2018, Shanghai, China 5–7 June Shanghai, ChinaChina urban CUE2018, energy systems, 5–7 2018, June 2018, Shanghai,
Simulation of heating loads and heat pump loads of a typical Simulation of heating loads and heat pump loads a typical The 15th International Symposium on District Heating andof Cooling suburban residential building of Beijing, China in wintertime suburban residential building of Beijing, China in wintertime Assessing the feasibility of using the heat demand-outdoor Xiaoling Yuaa, Qian Lvaa, Yifeng Dingbb, Shuo Yangbb, Liming Jiangcc, Liwen Jinaa Xiaoling Yu ,function Qian Lv , Yifeng Ding , Shuo Yang , Liming , Liwenforecast Jin temperature for a long-term district heatJiang demand Xi’an Jiaotong University, Xi’an, 710049, China. a
a Xi’anElectric Jiaotong University, Xi’an, 710049, China. State Grid Beijing Power Research Institute, Beijing, 100075, China. a,b,c bState cGrid a Electric a Research bBeijing, c Institute, 100075, ChinaBeijing Electric PowerPower Research Institute, Beijing, 100192, China.China. c c China Electric Power Research Institute, Beijing, 100192, China. a 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 Abstract c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract
I. Andrić
b
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
In recent years, Northern China suffers severe winter haze, which is attributed to extensive residential coal combustion for warm In Northern China suffers severe winterof haze, is attributed to extensive residential coal to combustion warm in recent winter.years, Chinese government presented proposals the which electrical energy substitute of the fossil fuels solve the for problem. in winter. the Chinese of the electrical energy substitute of electrical the fossilgrids fuels on to the solve the problem. However, wide government applicationspresented of electricproposals heating utilities expose the reliability of the production and Abstracttheside However, wide applications of electric heating exposewethestudied reliability of the electrical gridsload on the production transmission particularly in extreme cold days. utilities In this paper, heating loads and electric behaviors of anand air transmission side particularly in extreme cold days. Inbuilding this paper, we studied heating loads andinelectric load behaviors of an air source heat pump of a case-study suburban residential of Beijing, China in wintertime 2016. Firstly, we choose three District heating areloads commonly addressed in the literature as one themedium most effective solutions for the source heat pump offora case-study suburban residential building of warmest, Beijing, China in wintertime in Secondly, 2016. Firstly, wedecreasing choose characteristic daysnetworks heating analysis, i.e. the coldest, the andofthe day. heating loads ofthree the greenhouse gas from theby building sector. These systems require high investments which are returned through the heat characteristic daysemissions for heating loads analysis, i.e. the coldest, the warmest, and the medium heating loadsand of the case-study building were calculated a building energy simulation program, which is basedday. on aSecondly, coupled weather data sales. Due to transfer. the were changed climate building renovation demand indays the were future could decrease, case-study building calculated byconditions a building energy simulation program, which ischaracteristic based on a coupled weather databased and the building energy Hourly heating loads of and the case-study building inpolicies, the threeheat studied on prolonging the investment return period. building energy transfer. Hourly the case-study building the pump three characteristic werewere studied based on the weather analyses. Thirdly, theheating electricloads load of behaviors of the air sourceinheat used for roomdays heating investigated. The mainofscope of this paper is assess the feasibility ofofusing the source heat demand – outdoor temperature function forinvestigated. heat demand the weather analyses. Thirdly, thetodifferent electric load behaviors thewas air heat The pump used for and room were The COP the heat pump under operating conditions calculated. peak loads theheating electric load fluctuations of Alvalade, located in This Lisbon (Portugal), used asThe a case study. Thethe district consisted of for 665 The COP ofThe the district heat underwere different operating conditions was was calculated. peak on loads and electric load fluctuations offorecast. the heat pump in pump wintertime analyzed. work provided a valuable database heating loads andis electric loads buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district of the inheat pump area in wintertime work a valuable warm suburban in Beijing,were so asanalyzed. to help toThis achieve theprovided electric grid safety. database on heating loads and electric loads for renovation scenarios were developed (shallow, intermediate, deep).grid Tosafety. estimate the error, obtained heat demand values were warm in suburban area in Beijing, so as to help to achieve the electric compared with results from a dynamic heat demand model, previously developed and validated by the authors. Copyright © 2018 Elsevier Ltd. All rights reserved. Copyright © 2018 2018 Elsevier Elsevier Ltd. All All rights rights reserved. The results showed that when only weather change is considered, the margin of error could Symposium be acceptable forForum some 2018: applications Copyright © Ltd. reserved. Selection and peer-review under responsibility of the scientific committee of Applied Energy and Low Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy Symposium and (the error inand annual demand was lowerCUE2018. than 20% all weather scenarios considered). However, after introducing renovation Selection and peer-review under responsibility of thefor scientific committee of Applied Energy Symposium and Forum 2018: Low carbon cities urban energy systems, Forum 2018:the Low carbon cities and urban energy systems. scenarios, error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). carbon cities and urban energy systems, CUE2018. The value of slope increased average Electric within loads; the range of 3.8% up to 8% per decade, that corresponds to the Keywords: Heating loadscoefficient of building;Heating loadon simulation; Heat pump decreaseHeating in the loads number of heating hours 22-139h Electric during loads; the heating season (depending on the combination of weather and Keywords: of building;Heating loadofsimulation; Heat pump renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and 1.coupled Introduction improve the accuracy of heat demand estimations. 1. Introduction © 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. Tel.: +86-29-82664844; fax: +86-29-82668724. Cooling. * Corresponding Tel.: +86-29-82664844; fax: +86-29-82668724. E-mail address:author. [email protected]
E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the Applied Energy Symposium and Forum 2018: Low carbon cities Selection and peer-review under responsibility the scientific Selection peer-review responsibility of the scientific committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urbanand energy systems, under CUE2018. and urban energy systems, CUE2018. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems. 10.1016/j.egypro.2018.09.154
2
Xiaoling Yu et al. / Energy Procedia 152 (2018) 348–353 Xiaoling Yu / Energy Procedia 00 (2018) 000–000
349
In recent years, most region of China, particularly Northern China suffers severe and persistent winter haze characterized thick fog and high PM2.5 mass concentration. Previous studies show that the severe winter haze of Northern China attributed to extensive residential coal combustion for warm in winter[1]. In 2013, Chinese State Grid presented proposals of the electrical energy substitute of the fossil fuels[2]. For the design of electrical heating for warm of a building, the first task is calculating the heating loads of the building, which are dominated by the outdoor climate conditions, external wall configuration, and insulation layer thickness. The heating loads of the building can be calculated by the dynamic thermal balance models, which were based on a coupled weather data and the building energy transfer[3]. The heating loads of a building can be accurate evaluated by several commercial software such as EnergyPlus[4] and eQUEST[5]. Crawley et al performed a survey of these software and their capabilities[6]. According to proposals of the electrical energy substitute of the fossil fuels, all heating loads of a building should be supplied by electric heating utilities, sometimes together with energy storage system[7, 8]. In various kinds of electric heating utilities, the heat pump is the most energy saving. The electric load of the heat pump is shifting with the outdoor climate to maintain 18-23 ℃ indoor air temperature. There have been many papers published, focusing on load shifting of heat pumps[9, 10]. In this article, firstly, the heating demands of a typical suburban residential building in Beijing, China were analyzed during the whole winter based on the weather database research. Secondly, the electric loads shifting of an air source heat pump was modeled according to the change of the heating demands. This work is aimed to characterize hour-to-day electric load feature of the building, and is helpful to forecast the electric load shifting, thus to improve the electric grids safety. 2. Heating loads of a typical suburban residential building in Beijing 2.1. The case-study building configuration As shown in Fig.1, our study is based on a typical suburban residential building in Beijing. The case-study building has single storey living areas above ground level and it is constructed with brick-concrete framework. It has total area of 495m2, and contains two courtyards, two living rooms, a kitchen, two bathrooms, and five bedrooms. Its key construction and thermal characteristics are presented in Table 1. Table 1 Thermal characteristics of the case-study building. Elements
Material
Heat transfer coefficients(W/m2K)
Windows
Clear glass windows
2.6
Doors
Clear glass windows in doors
4.5
Floors
Concrete+mineral wool
0.35
Roofs
Gypsum board
0.65
Façade walls
360 mm brick+ concrete+50mm gypsum board
0.903
Intermediate walls
240 mm brick+ concrete+50mm gypsum board
3.8
Through the use of a building energy simulation program, we studied the heating demands of the case-study building during the winter from 15th Nov. 2016 to 15th Mar. 2017. We presented the hourly heat loads of the casestudy building in three characteristic days.
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29.9m Bathroom1
Bedroom1 Bedroom2 Bedroom3
Bedroom4
Storeroom2
Bedroom5
Kitchen
Livingroom1
Storeroom1
North courtyard
South courtyard
16.5m
Livingroom2
Bathroom2
Fig.1 the case-study building.
2.2. Heating loads of the case-study building The three characteristic days are the coldest day with outdoor daily mean temperature of -7 ℃ (30th Jan. 2017), the warmest day with outdoor daily mean temperature of 12 ℃ (11th Mar. 2017), and the medium day with outdoor
0
2
4
6
8
10 12 14 16 18 20 22 Time
Fig. 2
Heating loads Outdoor temperature
2
4
6
8
10 12 14 16 18 20 22 Time
Fig. 3
22 20 18 16 14 12 10 8 6 4 2 0 -2 -4 -6 -8 -10 -12 -14
38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0
Heating loads Outdoor temperature
22 20 18 16 14 12 10 8 6 4 2 0 -2 -4 -6 -8 -10 -12 -14
Temperature /℃
38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0
Heating loads/kW
26 24 22 20 18 16 14 12 10 8 6 4 2 0 -2 -4 -6 -8 -10 -12 -14
Temperature /℃
Heating loads Outdoor temperature
Heating loads/kW
40 38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0
Temperature /℃
Heating loads/kW
daily mean temperature of -2 ℃ (neither cold nor warm, 11th Dec. 2016). The hourly heating loads of the case-study building and the outdoor hourly temperatures in the three characteristic days were shown in Fig.2~4. From these figures, we can see the heating demand fluctuations in one day, as well as during the whole winter. It was found that in the warmest day the heating loads was 0-16 kW, while in the coldest day the heating loads was 22-36 kW. In the medium day, which is the most frequent in the wintertime, the heating loads were 20-30 kW. The hourly heating loads changes according to outdoor hourly temperatures, and it stays on the peak during the night-time hours (approximately 2:00 to 7:00), and goes down to its lows during the daytime hours (approximately 13:00 to 15:00). The heating loads wave gently during the night-time, while have relative large fluctuation between the time period of 6:00 AM to 14:00 PM.
0
2
4
6
8
10 12 14 16 18 20 22 Time
Fig. 4
Fig. 2 Hourly heating loads and outdoor hourly temperatures in the warmest day (11th Mar. 2017) Fig. 3 Hourly heating loads and outdoor hourly temperatures in the coldest day (30th Jan. 2017)Fig. 4 Hourly heating loads and outdoor hourly temperatures in the medium day(11th Dec. 2016)
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3. Electric loads shifting of an air source heat pump in winter 3.1. Heat pump system configuration The bedroom 1 of the case-study building is heated by an air source heat pump. The indoor temperature is set to be 20 ℃. The heat pump system is illustrated in Fig.5, and P-h diagram of refrigerant cycle is shown in Fig.6. Parameters of the heat pump were presented in Table 2.
A-the first stage compressor, B- the second stage compressor, C-condenser, G- throttle valve 1, F- economizer, H- throttle valve 2, E-evaporator Fig.5 Heat pump system
Fig.6 P-h diagram of refrigerant cycle Table 2 Parameters of the heat pump Parameters
Settings
Refrigerant
R410A
Maximum pressure in the condenser
4.3 MPa
Maximum pressure in the evaporator
2.5 MPa
Temperature difference between the cold fluid and the hot fluid in the exchanger
10 ℃
Degree of supercooling
5℃
Degree of superheating
5℃
Compressor efficiency
90%
Electric motor efficiency
90%
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3.2. COP and electrical loads analysis of the heat pump The effect of varying environment temperature over COP of the heat pump is investigated. The evaporating temperature of the heat pump is related to the environment temperature. It was found that COP of the heat pump increases with the environment temperature in Fig.7. The water flowing through the condenser absorbs heat in the condenser and then warms the room. The temperature of the water at inlet of the condenser (Twater-in) and at outlet of the condenser (Twater-out) can be adjusted. It can be seen in Fig.7 that when temperature of the hot water in the condenser is increased, COP of the heat pump decreases.
Fig.7 Effect of varying environment temperature and hot water temperature on the COP
Fig.8 is the electric load shift of the heat pump in the three characteristic days when the indoor temperature of the bedroom 1 is maintained as 20 ℃. From Fig.8, we can see the electric load fluctuations in one day, as well as during the whole winter. It is clearly seen that the electric load of the heat pump increases with the decrease of the outdoor temperature. For instance, under operating conditions of Twater-in=50 ℃ and Twater-out=55 ℃, the hourly electric load fluctuates in 400 W~800 W in the coldest day, 300 W~600 W in the medium day, and 0W~250 W in the warmest day. At the same outdoor temperature, the electric load of the heat pump increases with temperature of the produced hot water. As the hourly heating loads do, the hourly electric load changes according to outdoor hourly temperatures, and it stays on the peak during the night-time hours (approximately 2:00 to 7:00), and goes down to its lows during the daytime hours (approximately 13:00 to 15:00). The electric load shifting data provide a valuable information for forecasting electric loads for warm of the building and even the city, so as to help to achieve the electric grid safety.
(a)
(b)
(c)
Fig.8 The electric load shift of the heat pump in the three characteristic days. (a) the coldest day with outdoor daily mean temperature of -7 ℃(30th Jan. 2017), (b) the warmest day with outdoor daily mean temperature of 12 ℃(11th Mar. 2017), and (c) the medium day with
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4. Conclusions In this study, we have endeavoured to identify heating load and electric load shifting of a case-study suburban residential building of Beijing, China during the wintertime in 2016. Heating loads of the case-study building were calculated by a building energy simulation program. Hourly heating loads of the building in the three characteristic days were studied, and the peak/lows of the heating loads and their time period were determined. The electric load fluctuations of an air source heat pump used for heating a bedroom of the building were investigated. The energy analyses of the heat pump cycle were presented. The COP of the heat pump under conditions of varying outdoor temperatures and varying temperatures of the produced hot water was calculated. The hourly electric loads of the heat pump in the three characteristic days were presented. This study provided a valuable database on heating loads and electric loads for warm in suburban area in Beijing, so as to help to achieve the electric grid safety. Acknowledgements This work is supported by the National Science Foundation of China [No.51676150]. The authors are grateful for the support by the project named of " Heating load analyses of typical suburban residential buildings of Chinese different regions (5202011600U4)" in China State Grid Corp. References [1] P. Liu, C. Zhang, C. Xue, Y. Mu, J. Liu, Y. Zhang, D. Tian, C. Ye, H. Zhang, J. Guan, The contribution of residential coal combustion to atmospheric PM2. 5 in northern China during winter, Atmospheric Chemistry & Physics, 17 (2017) 1-37. [2] Chinese Environmental Protection online (Accessed at http://www.hbzhan.com/news/detail/83229.html). [3] L.E. Ortiz, J.E. Gonzalez, E. Gutierrez, M. Arend, Forecasting Building Energy Demands With a Coupled Weather-Building Energy Model in a Dense Urban Environment, Journal of Solar Energy Engineering, 139 (2016) 011002-1-011002-8. [4] D.B. Crawley, L.K. Lawrie, F.C. Winkelmann, W.F. Buhl, Y.J. Huang, C.O. Pedersen, R.K. Strand, R.J. Liesen, D.E. Fisher, M.J. Witte, EnergyPlus: creating a new-generation building energy simulation program, Energy & Buildings, 33 (2001) 319-331. [5] J. J. Hirsch, eQuest, The QUick Energy Simulation Tool, 2006, DOE2 Com, epub, http://www.doe2.com/equest/. [6] D.B. Crawley, J.W. Hand, M. Kummert, B.T. Griffith, Contrasting the capabilities of building energy performance simulation programs, Building & Environment, 43 (2008) 661-673. [7] X. Yang, S. Feng, Q. Zhang, Y. Chai, L. Jin, T.J. Lu, The role of porous metal foam on the unidirectional solidification of saturating fluid for cold storage, Applied Energy, 194 (2017) 508-521. [8] X. Yang, L. Zhao, Q. Bai, Q. Zhang, L. Jin, J. Yan, Thermal performance of a shell-and-tube latent heat thermal energy storage unit: Role of annular fins, Applied Energy, 202 (2017) 558-570. [9] J. Allison, A. Cowie, S. Galloway, J. Hand, N.J. Kelly, B. Stephen, Simulation, implementation and monitoring of heat pump load shifting using a predictive controller, Energy Conversion & Management, 150 (2017) 890-903. [10] C. Han, K.M. Ellett, S. Naylor, X. Yu, Influence of Local Geological Data on the Performance of Horizontal Ground-coupled Heat Pump System Integrated with Building Thermal Loads, Renewable Energy, 113 (2017) 10461055.