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Energy Procedia 00 (2017) 000–000
www.elsevier.com/locate/procedia
Energy
(2017) 000–000
631–636
EnergyProcedia
Procedia122
00 (2017)
www.elsevier.com/locate/procedia
CISBAT 2017 International Conference – Future Buildings & Districts – Energy Efficiency from
Nano to Urban Scale, CISBAT 2017 6-8 September 2017, Lausanne, Switzerland
CISBAT 2017 International Conference – Future Buildings & Districts – Energy Efficiency from
Integration
Renewable
Energy
in 6-8
theSeptember
Built Environment
(Electricity,
Nano toofUrban
Scale,
CISBAT
2017
2017,
Lausanne,
Switzerland
15th
International
Symposium
on District
Heating
andfor
Cooling
Using
air The
source
heat
pump
air heater(ASHP-AH)
rural space
Heating and Cooling)
heating
and power
peakthe
load
shifting
Using
air source
pump
air
for rural space
Assessing
theheat
feasibility
of heater(ASHP-AH)
using
heat
demand-outdoor
heating Hui
andLe,
load
temperature function
for
apower
long-term
heat demand forecast
Haoyuepeak
Li,Yidistrict
Jiang*shifting
Building Energy Research
Center,Beijing,10084,China
*
a
b
Le, Haoyue
Li,Yi Jiang
I. Andrića,b,c*, A. Pinaa,Hui
P. Ferrão
, J. Fournier
., B. Lacarrièrec, O. Le Correc
Energy
Research
Center,Beijing,10084,China
IN+ Center for Innovation, TechnologyBuilding
and Policy
Research
- Instituto
Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
b
Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France
c
Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract
a
In
order to mitigate the severe atmospheric pollution caused by the gas emission of scattered coal consumption during winter in
Abstract
the Beijing-Tianjin-Tangshan area, a winter space heating scheme based on the use of an air source heat pump is proposed. This
Abstract
scheme
clean the
heating
rural areas by
enhancing
theby
efficiency
of electric
adjustment
through peak
shifting.
In order offers
to mitigate
severeinatmospheric
pollution
caused
the gas emission
of peak
scattered
coal consumption
during
winterThe
in
paper
firstly introduces how toarea,
use air
sourcespace
heat pumps
toscheme
replacebased
heating
stoves
forofrural
residential
heating,
then
explains This
how
the
Beijing-Tianjin-Tangshan
a
winter
heating
on
the
use
is
proposed.
an
air
source
heat
pump
District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the
to use this
heating
method
to in
adjust power
load. Finally,
computation
and
verification
was
carriedThe
out
scheme
offers
clean
heating
areaspeak
bysector.
enhancing
efficiency
ofanalysis
electric
peakexperimental
adjustment
through
peak
shifting.
greenhouse
gas
emissions
fromrural
the building
Thesethe
systems
require
high investments
which are
returned
through
the heat
to
show
the
effects
on
the
power
grid
in
the
Beijing-Tianjin-Hebei
area
and
on
farmer
households
if
all
the
6
million
farmer
paper
how toclimate
use air source
heat and
pumps
to replace
heating stoves
for heat
rural demand
residential
thencould
explains
how
sales.firstly
Due introduces
to the changed
conditions
building
renovation
policies,
in heating,
the future
decrease,
households
in the Beijing-Tianjin-Hebei
Region
were Finally,
to use this
heating method.
to
use this heating
method toreturn
adjustperiod.
power
peak load.
computation
analysis and experimental verification was carried out
prolonging
the investment
© 2017
The
Authors.
by Elsevier
Ltd.
to
show
thescope
effects
onPublished
the
power
in the
the
Beijing-Tianjin-Hebei
area demand
and on –farmer
households
if all
the 6 for
million
The
main
of this
paper
is togrid
assess
feasibility
of using the heat
outdoor
temperature
function
heat farmer
demand
Peer-reviewinunder
responsibility of the scientific
committee
of
the
scientific
committee of the CISBAT 2017 International
households
the
Beijing-Tianjin-Hebei
Region
were
to
use
this
heating
method.
forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665
Conference
–Authors.
Future Buildings
&byDistricts
Energy Efficiency from Nano to Urban Scale.
©buildings
2017 Thethat
Published
Elsevier–period
Ltd.
vary in
both construction
and typology. Three weather scenarios (low, medium, high) and three district
© 2017 The Authors.
Published by
Elsevier
Ltd. committee of the scientific committee of the CISBAT 2017 International
Peer-review
under
responsibility
of
the
scientific
renovation
scenarios
were
developed
(shallow,
intermediate,
deep).
To estimate
the error, Conference
obtained heat
demand
values were
Peer-review
under
responsibility
of the scientific committee
of the
CISBAT
2017 International
– Future
Buildings
Keywords:
Power
Grid
in
the Beijing-Tianjin-Tangshan
area,Smog
Air
Pollution,Air
Source
Heat
Pump,Rural Space Heating,Power
peak load&
Conference
– Future
Buildings
& Districts
– Energy
Efficiency
from Nano
to
Urban
Scale.
compared
with
results
from afrom
dynamic
heat
demand
model, previously
developed
and
validated by the authors.
Districts
–
Energy
Efficiency
Nano
to
Urban
Scale
shifting
The results showed that when only weather change is considered, the margin of error could be acceptable for some applications
Keywords:
Grid indemand
the Beijing-Tianjin-Tangshan
area,Smog
Air Pollution,Air
Source
Heat Pump,Rural
Space
Heating,Power
peak
load
(the errorPower
in annual
was lower than 20%
for all weather
scenarios
considered).
However,
after
introducing
renovation
shifting
scenarios, the error value increased up to 59.5% (depending on the weather and renovation 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
decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and
renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the
coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and
improve the accuracy of heat demand estimations.
author. Tel.: +8618800100881; .
©Corresponding
2017 The Authors.
Published by Elsevier Ltd.
E-mail address: leh13@mails.tsinghua.edu.cn
Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and
*
Corresponding author. Tel.: +8618800100881; .
Cooling.
*
1876-6102
© 2017 The
Authors. Published by Elsevier Ltd.
E-mail address:
leh13@mails.tsinghua.edu.cn
Peer-review under responsibility of the scientific committee of the CISBAT 2017 International Conference – Future Buildings & Districts –
Keywords: Heat demand; Forecast; Climate change
Energy Efficiency from Nano to Urban Scale.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the scientific committee of the CISBAT 2017 International Conference – Future Buildings & Districts –
Energy Efficiency from Nano to Urban Scale.
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 CISBAT 2017 International Conference – Future Buildings &
Districts – Energy Efficiency from Nano to Urban Scale
10.1016/j.egypro.2017.07.361
632
2
Hui Le et al. / Energy Procedia 122 (2017) 631–636
Hui Le et al. / Energy Procedia 00 (2017) 000–000
1. Introduction
The frequency of severe haze is much higher in winter than in the other three seasons in north China.[1] In addition
to climate factors, one of the main reasons for this pollution lies in a large number of air pollutant emissions caused
by coal-fueled heating in rural areas. [2] Therefore one of the key points to mitigate the haze is to replace the
scattered coal used for rural space heating.
Moreover, shifting power peak load restricts the development of renewable energy. Because of the lack of flexible
and effective means for shifting power peak load, wind power loss in China can be between 20% and 30% at
present. In addition, large-scale promotion of photoelectricity is difficult due to the restricted grid access.
2. Methods
Based on preliminary studies [3][4][5], we put forward the use of ASHP-AH (Air Source Heat Pump Air Heaters)
for heating, which can realize clean heating in rural areas and enhance the efficiency with regard to shifting power
peak load of the power grid. Driven by electricity, the ASHP-AH not only causes no emission of pollutants, but it
can also utilize wind and light energy to improve new energy efficiency. The two-stage compression system
increases heating capability and reliability in ambient conditions even down to -30 °C. Its COP can be up to 2.0 at
ambient temperature T=-20°C , and 3.6 at ambient temperature T=7℃. Meanwhile, the ASHP-AH eliminates the
sensation of draft and reduces the longitudinal temperature gradient by downside air supply.
When power load is lowest, as original power demand at 4 am in Fig4, a part of ASHP-AH becomes operational for
heating and the consumption of power grid electricity increases. When the power load reaches the peak, as original
power demand at 9 am in Fig.4, a part of ASHP-AH stops, thus decreasing the consumption of power grid
electricity, and in the meantime the indoor thermal environment is maintained by using the thermal inertia of the
building itself. So we can regard the huge thermal inertia of the building as a massive storage body, providing a
flexible and effective means for power peak load shifting. More specifically, a controller that can accept commands
from a Power Grid Dispatching Center is installed in each ASHP-AH. The controller can be operated in one of the
following three states: forced on, forced off, and controllable by users, which can be issued to a certain amount of
ASHP-AH according to the peak load variation of the grid load, the wind power and light power conditions. When
the ASHP-AH is forced to run or to stop, the power grid company will give economic compensation to users based
on their contribution to power peak load shifting.
In Beijing suburbs, each household needs 3 units at an installation cost of 4000 yuan / unit. The indoor temperature
can vary from18°C to 28°C , and not lower than 15°C during the coldest period. The HSPF (Heating Seasonal
Performance Factor) is between 3.2 and 3.5; and the power consumption during the heating season per unit floor
area is 16.9-33.7 kWh/m2. By now, this system has been installed in more than 200 rural households.
3. Simulation
3.1. Model &ASHP-AH operating conditions
Take an ASHP-AH used for space heating and power peak load shifting for example, and suppose that there are 6
million farmer households in the Beijing-Tianjin-Hebei Region, with a house area of 60m2 per household and a
heating period from 15th November to 15th March. The power capacity of ASHP-AH in each household is 2.7kW,
therefore the total installed capacity is 16.2million kW. DeST (a building energy simulation software [6]) was used
to predict building load.
There are 188 parallel power plants whose capacity is 300MW in Beijing-Tianjin-Tangshan area. ASHP-AH was
adopted as a tool for power peak load shifting of virtual power plants. And we propose to show you the simulative
ASHP-AH operating conditions on 3 typical days.
Hui Le et al. / Energy Procedia 122 (2017) 631–636
Hui Le et al. / Energy Procedia 00 (2017) 000–000
Fig. 1 ASHP-AH operating conditions on Nov. 15th
633
3
Fig.2 ASHP-AH operating conditions on Dec. 25th
Fig. 1 shows the typical operating conditions of ASHP-AH on 15th November at the beginning of winter. The Xaxis means 24 hours, the Y-axis means the 6 million farmer households in the Beijing-Tianjin-Hebei Region, a blue
column represents the number of forced off users, an orange column represents the number of forced on users, a
gray column represents the number of free users. Taking the operating condition at 1 am as an example, due to low
electricity consumption at night, a part of the air source heat pump needs to be opened to fill the valley; therefore
during this time the number of forced open users is more than 200 million, while at 9am more than 400 million users
are forced off due to high electricity consumption. At the beginning of winter, the average forced open time is 5.4h
and averaged forced close time is 1.8h per day per household.
Fig. 2 shows the typical operating conditions of ASHP-AH in the middle of winter. As the outdoor temperature is
relatively low, the heat inertia of the build is used to maintain the indoor temperature. It can be seen from simulation
results that each household only needs less than 3h forced off time to maintain the indoor temperature within a
reasonable range, which ASHP-AH can be controlled by the control center. The average forced on period is 6.2h,
and the average forced off time is 7.1h per household per day.
Fig3. ASHP-AH operating condition on Jan. 24th
Fig.4 Generation curve of the heat pumps
Fig.3 shows the typical operating conditions of ASHP-AH at the end of winter. During the day a certain number
of air source heat pumps are shut down to mitigate the peak load. During this time, the control center will arrange
forced off zones to ensure that not all pumps in every household are forced off longer than 3 hours, to ensure that the
indoor temperature of a farmer’s house is within the comfort range. The average forced open time is 4h and the
average forced off time is 2.9h per household per day.
Fig.4 shows the generation curve after peak load shifting of the heat pumps, the blue curve is the original
generation curve in the Beijing-Tianjin-Tangshan area, and the red curve is the ideal generation curve after using
ASHP-AH for heating of rural houses. In Fig.5, the blue curve indicates the variation of room temperature, whose
figures correspond to the vertical coordinates on the left. The red curve corresponds to users ‘forced on’ and ‘forced
off’. 1 represents ‘forced on’ and 0 represents ‘forced off’. The green curve corresponds to users who freely control,
the corresponding heat-supply corresponds to the heating load demand ratio on the right, ranging from 0 to 1.
Hui Le et al. / Energy Procedia 122 (2017) 631–636
Hui Le et al. / Energy Procedia 00 (2017) 000–000
634
4
Fig. 5 Beginning of winter, from November.20th to 22th
Fig.6 Middle of winter, from January.4th to 5th
Fig.5 depicts the typical indoor air temperature variation from 20th November to 22th November for 3
consecutive days in the early cold period. It can be seen that the indoor air temperature is relatively high in early
winter, within 18 ~ 22°C , while the load of ASHP-AH is relatively low.
Fig. 6 depicts the typical indoor air temperature variation from 4th to 5th in January for 3 consecutive days in the
middle of winter. The lowest indoor air temperature is not lower than 15°C (the designed indoor temperature of
rural residential in freezing areas and cold areas), and the load of ASHP-AH in the middle of winter is relatively
high. The “forced on” time increases due to the relatively low outdoor air temperature.
Table 1. Statistics of forced on/off hours in heating season
Forced on hours(h/day)
Forced off hours(h/day)
Beginning of winter
5.4
18
Middle of winter
6.2
7.1
End of winter
4
2.9
Note: average forced hours: on/off hour= 8.9h/day；on hours = 5.2h/day；off hours = 3.7h/day；Total electricity
consumptions= 3839kWh
Fig.7 Operation of the power plant in whole heating seasons
Fig.8 Operation of the power plant in transitional period in winter
The blue curve in Fig. 7 corresponds to the power supply curve before peak load adjustment of air source heat
pumps, showing a great fluctuation; and the orange curve to that after the adjustment. The power used during the
valley period is 75% of that in the peak period, with a variation of over 10 million kW. After taking advantage of an
air source heat pump, the power plant is capable of realizing stable operation in the whole day; and can satisfy
heating demand simply by turning on or turning off a certain number of machines. In this way, the power plant can
operate at full load. This method can not only decrease the coal consumption for power plant, but also improve the
efficiency of the power plant. Meanwhile, this method can decrease the frequency of starting/shutting of the
machine and prolong the service life of the machine.
As shown in Fig.8, the heating demands can be satisfied by putting a number of units into operation in the transition
from the early cold period to severe winter, and shutting down some in the transition from severe cold period to the
end of the cold period.
Hui Le et al. / Energy Procedia 122 (2017) 631–636
Hui Le et al. / Energy Procedia 00 (2017) 000–000
635
5
3.2. Power consumption analysis
Table 2. Power consumption in the whole heating season:
Current
After using ASHP-AH
Electricity consumption except for heating pumps（100 million kWh）
1307
1307
Electricity
Heating pumps（100 million kWh）
0
231
In total（100 million kWh）
1307
1538
Coal
Power plant（10thousand tce）
4375
5011
consumption
Rural heating（10thousand tce）
1572
0
Rural heating（10thousand tce）
5947
5011
In total（10thousand tce）
335
326
Using ASHP-AH to adjust electric peaks and valleys, we can effectively adjust the peak-valley variation of the
power grid load, and put wind power into the power grid to partially replace coal-fueled power which helps to
decrease the average coal consumption for power generation from 335gce/kWh to 326gce/kWh and save 9.36
million tce coal. As a result, the emissions caused by coal consumption may be reduced by 45%.
3.3. Economic analysis
The cost for this new method is estimated as follows: each household needs 12K RMB to install the 3 units of
ASHP-AH, 1560 RMB for coal-fueled heating in a heating season, and 1919RMB for electricity (suppose the price
is 0.5RMB/ kWh) in the whole heating season.
Due to generation efficiency enhancement with this method, users can obtain a subsidy 720RMB (according to
simulation data) in a whole heating season, which means that each only has to pay 1217RMB for heating. Therefore,
compared with coal-fueled heating, this methods provides not only a better room environment but also cheaper
heating.
4. Experiment
To verify the feasibility and effectiveness of this method, a demonstration project was carried out in the Fangshan
District, Beijing, with 88 ASHP-AH installed for a number of households. Through the entire heating season in the
test, it can be confirmed that the use of ASHP heating can indeed meet the heating demands of farmers and serve as
a virtual peak shifting power plant.
Fig.9 Operating conditions on coldest days in heating season
Fig.10 The test of thermal inertia of building
Hui Le et al. / Energy Procedia 122 (2017) 631–636
Hui Le et al. / Energy Procedia 00 (2017) 000–000
636
6
4.1. Meet the heating needs
It can be found in Fig.9 that, from 20th January to 24th January, 2017, the outdoor temperature varied from -10.6°C
to 3.5°C with an average temperature of -3.9°C . For rooms without heating, the average indoor temperature is 10.2
℃, while for rooms with ASHP-AH, open for a certain time, the daytime indoor minimum temperature is not lower
than 14°C.
4.2. Use as virtual power peak load shifting plant
The test results on building thermal inertia is shown in Fig. 10. In the test, the room was heated to 25°C by the
ASHP-AH and then cooled down naturally. Due to the thermal inertia of building, when average outdoor
temperature was 2°C , the temperature indoors was higher than 15°C even if the pump was turned off for more than
10 hours, which demonstrates that it is possible to utilize the mass building thermal inertia to store energy.
4.3. Actual statistics of energy consumption of ASHP-AH
Heating
area/(m2)
Number of
main heating
room
Table 2. Power consumption in the whole heating season:
Electricity
Average
Electricity consumption of
consumption in
electricity
heating
heating
consumption
/(RMB)
season(kWh)
(kWh/m2)
Heating
electricity per
unit area（RMB/m2）
1#
62
4
1587.0
25.6
783.8
12.6
2#
43
3
1497.7
34.8
591.5
13.8
3#
79
4
11269.9
16.1
527.9
6.7
4#
124
3
3594.4
29.0
835.0
6.7
5#
225
1
757.1
30.3
280.6
11.2
6#
90
3
2534.2
28．2
1001.1
11.1
7#
160
7
7070.8
44.2
2705.4
16.9
5. Conclusions and Outlook
In conclusion, ASHP-AH is a very good choice to replace rural coal-fueled heating with electricity, for it can
eliminate serious pollution caused by scattered coal consumption for heating during winter, significantly alleviate
occurrence of haze, improve farmers’ indoor comfort in winter and lower their economic burden. In addition, it also
can be used as a "virtual peaking power plant" through active adjustment to achieve a wide range of power peak
load shifting, significantly improve the efficiency of coal-fueled power plants, improve the current inefficient
operation of power plants, eliminate bottlenecks in wind power and photovoltaic power network access, and
promote the development of renewable energy.
References
[1] X. Ma, Z. Liu, X. Zhao, L. Tian, and T. Wang, “The Spatial and Temporal Variation of Haze and Its Relativity in the Beijing-Tianjin-Hebei
Region.,” Reg. Res. Dev., vol. 2, pp. 134–138, 2016.
[2] G. Zhi, “Rural Household Coal Use Survey，Emission Estimation and Policy Implications,” Res. Environ. Sci., vol. 8, 2016.
[3] R. Ma, L. Zhang, Y. Fu, X. Yang, and Y. Jiang, “Hot air type low ambient temperature air source heat pump:A Clean and Efficient Heating
Technology Suitable for Beijing Rural House,” Construction Science and Technology, vol. 14, pp. 76–81, 2016.
[4] Tsinghua University, Research Report on Annual Development of China ’s Building Energy Efficiency. China Construction Industry Press,
2016.
[5] J. Yi, “Suggestions on Realizing Low Temperature Air Source Heat Pump Hot Air Heating Technology to Achieve No Coalification in
Rural Area of Beijing, Tianjin and Hebei,” National High-end think tank,2016.
[6] Yan, Da, et al. "DeST—An integrated building simulation toolkit Part I: Fundamentals." Building Simulation. Vol. 1. No. 2. Tsinghua
University Press, co-published with Springer-Verlag GmbH, 2008.
[7] H. Le, H. li, and Y. Jiang,“Using Air Source Heat Pump Air Heater(ASHP-AH) For Rural Space Heating and Power Peak Load Shifting."
Energy of China,2016.