Potential for Balancing Wind And Solar Power
Using Heat Pump Heating And Cooling Systems
David Fischer∗† , Karen B. Lindberg‡ , Stine Mueller∗ , Edo Wiemken∗ , Bernhard Wille-Haussmann∗
∗ Fraunhofer
Institute for Solar Energy Systems, Dept. of Smart Grids, Freiburg, Germany
† KTH Royal Institute of Technology, Stockholm, Sweden
‡ NTNU Norwegian University of Science and Technology, Trondheim, Norway
Abstract—An increased share of fluctuating renewable energy generation like wind and photovoltaic (PV) in the power
system has led to an increased need for capacity reserves. By
providing flexibility on the demand side, balancing of fluctuations is possible. In this paper, air source heat pumps (ASHP)
for heating and cooling in residential and office buildings are
investigated in order to increase the ability to integrate wind
and PV into the power system.
Building simulation is used to calculate the heating and
cooling load profiles as well as the demand for domestic hot
water (DHW). In order to evaluate the potential for flexibility
in Germany on a national scale, the load profiles are scaled up
so that the annual heat demand of the investigated buildings
equals 10% of the total heat demand of the residential and the
office sector respectively.
The load shifting is conducted by minimizing fluctuations of
either 1) wind generation, 2) PV generation or 3) residual load
on a 24 hours horizon for the whole year. The optimal operation
schedule is calculated by solving a quadratic optimization
problem.
It is shown, that heat pumps can help to better integrate
renewable energy sources into the power system. Different
energy demands (space heating, cooling, domestic hot water
(DHW)) in the building sector offer different integration possibilities. Cooling flexibility turns favourable for the integration
of PV in summer. DHW flexibility is available almost constantly
during the year and could be used to integrate higher shares of
wind electricity into the power system. Space heating provides
the largest flexibility potential, however this flexibility is only
available in winter.
As there are more residential buildings than offices, the
annual flexibility offered by residential buildings is about ten
times as high as the flexibility offered by offices, however
the offices’ flexibility is available more evenly throughout the
year as they have heating flexibility in winter and cooling
flexibility in summer. The residential buildings tend to offer
their flexibility mainly in winter, but the domestic hot water
(DHW) demand is available throughout the year, which offers
more flexibility than the offices in total.
When including a high amount of heat pumps into the power
system, new peaks and fluctuations in the power system can be
generated. This should be taken into account when designing
control strategies.
N OMENCLATURE
a0 , a1 ,a2
ASHP
C
Cap
CHP
COP
∆t
DHW
Eloadshif t
HP
Regression coefficients
Air Source Heat Pump
Storage energy capacity per Kelvin
Storage energy capacity
Combined Heat and Power
Coefficient of Performance
Calculation time step width
Domestic Hot Water
Energy shifted
Heat Pump
µP
P
Mean value of profile to be balanced
Profile to be balanced (PV, wind or residual
load)
Pel
Electric energy
PV
Photovoltaic
Q̇Demand Thermal energy demand from building or
DHW
Q̇max
Max. heat capacity of the HP
Q̇use
Used heat from HP
σloadshif t Intra-day standard deviation of profile to be
balanced
T
End time of calculation window
t
Point in time
Tamb
Ambient temperature
Tamb24
24 hrs. mean value of the ambient temperature
Tsupply,max Max. allowed water temperature out of HP
Tmin
Min required temperature in storage
Tsupply
Temperature out of heat pump
I. I NTRODUCTION
In the current work we investigate the potential of air
source heat pumps in residential homes and offices to
balance fluctuations in the power grid. Studying the seasonal
pattern enable us to investigate the limitations of the heat
pumps considering the availability of balancing capacity
during the year.
In the power system, traditionally, a change in electric
demand has to be compensated by a change in electric
generation. With the introduction of fluctuating renewable
energy sources like wind and solar, electricity generation is
also changing in time depending on the weather conditions.
This brings additional challenges to the existing power
system. Changing the electricity demand to compensate
for fluctuating renewable electricity generation can help to
reduce the negative impact of renewable energy sources to
the power system.
With the rise of cheap and secure communication and
actuation technology, demand side management even with
smaller units seems possible. In addition to electric storage
technologies like batteries, thermal-electric systems coupled
with thermal storage, such as combined heat and power
plants (CHP) [1], [2] and heat pumps (HP), offer possibilities
of flexible operation and consequently flexible electricity
demand [3]–[5].
In the German energy system HP installations are increasing [6]. Such systems are in many cases already equipped
with a thermal storage [7] to overcome times during the
day where HPs are not allowed to operate. In an urban
setting air-source-heat-pumps (ASHP) are expected to have
an increasing impact [8] due to the easy access to air as heat
source.
In the past and current discussion on load shifting with
heat pumps, the focus has been on heating demand in domestic buildings [9]–[11]. In this work, the focus is widened
to including flexibility in office buildings, and applications
of cooling technologies.
In [12], [13] a comprehensive study of the potential
contribution to load management of heat pumps in a power
market scenario has been undertaken, showing the annual
potential on an aggregated level for Germany. However,
when investigating the balancing potential, consideration
of seasonal availability is important since the availability
of flexible demand should match the need for flexibility.
Depending on the character of the renewable energy generation, the need for flexibility to balance fluctuations can
vary significantly during the year.
This paper is organised as follows. The approach for
evaluating the load shifting potential is elaborated in section
II-B. The way of calculating load shifting is explained in
II-A. Section III-A describes the energy system model of
the building, and Section III-B the optimisation problem that
gives the optimal dispatch of the heat pumps. To investigate
when the need for flexibility occurs, an analysis of the intra
day fluctuations of the PV generation, wind generation and
residual load is done in Section IV-A. Followingly, the match
between the need for flexibility and the provided balancing
potential of ASHPs is investigated in section IV-C.
II. M ETHOD
In the following, the method applied to evaluate the
potential of an air source heat pumps in houses and offices
to balance fluctuations in the grid is explained.
A. Load shifting
Shifting load means in our case shifting electric demand
from one point in time to another. Most heat pump systems
in Germany today are equipped with a storage which can
handle a 2 hr. blocking of the grid operator up to three
times on a daily basis. This implies that the already existing
heat pump systems in Germany today actually entails a
substantial amount of shifting potential. In our method we
assume that a reasonable timespan for load shifting seems
to be 24 hours, which also reflects the organization of the
day-ahead energy market in Germany. In other words, we
assume that the operation of the heat pump unit can be
shifted within 24 hrs. The aim of load shifting is to smooth
and flatten a given signal P (t). The signal could either be
wind or PV generation, or the residual load of the grid. The
best smoothed solution will then occur when the signal is
completely flattened out, i.e. equal to the mean value µP
of the initial signal P . One measure for quantifying the
fluctuations, is the amount of energy that needs to be shifted
within the 24 hr. time span to make the signal flat:
Eloadshif t =
T
1X 1
( |P (t) − µP |)
T t=1 2
(1)
However, when using the total shifted amount of energy
there is no information on the distribution of the energy
shifted around the mean value. By using the standard deviation, σloadshif t , we can overcome this lack of information,
as it quantifies the variability of the signal.
v
u
T
u1 X
σloadshif t = t
(P (t) − µP )2
(2)
T t=1
The frequency of the shifting should also be taken under
consideration, and will be investigated in further work.
By focusing on a 24 hour timespan, seasonal and weekly
components are not part of the shifting problem.
B. Approaching the load shift problem for heat pumps
The load shifting capacity and its limitation for heat
pumps are explained with the help of Figure 1.
1) Assuming full flexibility of the heat pump and storage
system, the amount of energy that can be shifted during
a day cannot exceed the demand during that day.
2) The heat pump capacity (PHP ) and the storage capacity (Cap) pose limitations to the flexibility in
operation.
3) It is assumed that the HP has the possibility to increase
or decrease the electric load by ramping up or down.
The discrepancy between the HP’s heat production and
the building’s thermal demand is supplied to or taken
from the storage. Depending on the heat pump capacity
and the demand at a given point in time, two cases are
possible:
a) If the HP capacity is higher than the thermal
demand, it is possible both to ramp up (charging
the storage) and ramp down (discharging the
storage). At this point shifting possibilities are
limited by the space left in the storage and the
maximum capacity of the heat pump.
b) If the HP capacity is less than or equal to the
thermal demand, the only available flexibility
offered is to ramp down (discharging the storage),
if there is energy available in the storage.
4) Whether storage capacity is a limiting factor depends
on the amount of energy that needs to be shifted
within a certain time. Choosing the optimal storage
size depends strongly on the thermal demand profile
and the load profile that should be balanced by the
HP, and is thus an important task (however it is not
addressed in this work).
a) High frequency shifting: If the profile to be
smoothed changes with a high frequency, energy
can be shifted using a comparably small storage,
which is charged and discharged frequently, in
this case the charging and discharging power of
the HP energy system is the key factor.
b) Low frequency shifting: In the case of low charging and discharging frequencies, storage capacity
is more relevant as the amount of energy stored
for a longer time is higher than in the high
frequency case.
C. Procedure for investigating the load shifting potential
To estimate the potential of heat pumps for load shifting,
the heat pump is separated into three operation modes:
TABLE II
E NERGY D EMAND 2012 FOR RESIDENTIAL AND OFFICE BUILDINGS
[15], [16]
Residential [TWh]
Office [TWh]
Space Heating
462
44
Domestic Hot Water
102
5
Cooling
0
0.95 1 / 2.2 2
1 Scaled with the same share as space heating
2 Scaled with the help of the building model
aims to use the storage for minimizing electricity peaks, e.g.
the electricity consumption of the heat pump, which equals
the objective function in eq. 7 when dropping the first term
P (t), making the electricity consumption being penalized
quadratically.
Fig. 1.
A brief scheme of the load shifting problem
TABLE I
H EAT P UMP AND STORAGE SIZING . A LL POWERS ARE THERMAL FOR
THE COLDEST HOUR OF THE CONSIDERED YEAR (-16 ◦ C).
Space Heating
Domestic Hot Water
Cooling
Heat Pump
10 kW
2.7 kW
same unit as for heating
Storage
670 l
409 l
670 l
III. M ODEL
This section first describes the model of the thermal
energy system within the building including the heat pump
unit, the thermal storage, and the thermal load profile of
the building. Secondly the optimisation model is described,
which gives the optimal scheduling of the heat pump units
while minimising fluctuations of a given signal (residual
load, PV or wind generation).
A. Description of the heat pump system
1) Serve the space heating load
2) Serve the domestic hot water load
3) Serve the cooling load
For each mode, the energy and the availability is analysed
and treated independently. For this reason, domestic hot
water is assumed to be prepared with a separate heat pump
unit, although in reality the space heating and DHW often
is operated together.
1) Sizing and Scaling: In order to determine the national
shifting potential, the following three-step-scaling methodology has been developed.
1) An average heat load profile of a group of buildings
is calculated (see section III-A1) and scaled down to
a peak demand of 10 kW at the coldest day
2) The heat pump and storage are then sized to the
demand according to data from [14] (see Table I)
3) Subsequently, the heat pump, storage and heat demand
are up-scaled to meet 10 percent of the national
thermal energy demand for the respectively building
types shown in Table II.
With this procedure, a representative load profile as well as
a realistic system configuration are derived. The heat pump
is used for heating in winter and cooling in summer time.
Domestic hot water is prepared by a separate unit in order
to be able to investigate its shifting potential. This leads to
a slight underestimation of the available heat pump power
especially during the summer, since in a combined system
the HP power which is used for space heating in winter
also is available for the preparation of domestic hot water in
summer.
2) Calculation of shifted load: The shifted load is calculated using eq. 1, finding the difference between the
conventional operation (labelled ”before”) and a load shifting
oriented operation (labelled ”after”) of the heat pump units
(see Figure 6). In the conventional operation the controller
In this paper, we investigate the shifting potential in
residential and office buildings built after 1984. As these
building have a relatively high technical standard, the supply
temperature of the heating system can be sufficiently low to
allow the use of heat pumps for space heating. The heating
system under consideration is radiator heating connected to
a thermal storage for space heating. In summertime this
storage can be used to feed the cold water system of the
building used for cooling. In the residential application, the
heat pump unit is additionally used for the preparation of
domestic hot water.
Fig. 2.
Schetch of the heat pump energy system within the building.
1) Determining the thermal load: The domestic hot water
(DHW) consumption and the energy demand for space
heating and cooling is calculated using a stochastic simulation method introduced in [5], [17], which is based on
a simplified thermal building model from [18]. To get a
representative heat load profile of an area, 300 residential
homes and 100 office buildings are simulated and then averaged. The share of the building types as well as the building
physics are taken according to [19]. In each simulation run
the buildings differ in occupancy patterns, orientation and
temperature set-points within the building.
2) The heat pump model: The reversible air source heat
pump is the core component of the system under investigation. The heat pump has some important characteristics
that need to be considered when accessing its ability to shift
electrical demand using a thermal storage:
1) The efficiency of converting electrical energy to thermal energy (COP) is strongly depended on the air- and
supply temperatures of the system
2) The thermal/electrical capacity of the unit is also
depended on the temperature levels applied. Meaning
that the available thermal and electric capacity of an
installed heat pump varies during the year
In [20], [21] it is suggested to model the COP of a heat
pump based on the temperature spread between source
and sink temperature. This approach is followed and an
approximation of the COP for a reversible ASHP based on
manufacturers data [14] is used:
COP =
Q̇use
= a0 + a1 · (Tsupply − Tamb )
Pel
Cap = C · (THP,max − Tmin (t))
(6)
(3)
For heating and cooling mode the coefficients a0 ,a1 are
different for the heat pump used in this study ( [14] page.
151). Figure 3 shows the COP for heating and cooling during
the year considered.
The thermal capacity of a heat pump is not constant during
the year, it is limited by the allowed entrance and exit conditions at the compressor and the heat transfer efficiency of
the heat exchangers. For the unit under consideration a linear
relationship of thermal capacity and ambient temperature
was extracted from manufacturers data.
Q̇max = a0 + a1 · Tamb
account. With higher conversion efficiency, less electrical
energy can be shifted per unit of thermal storage. Figure
4 shows the available electrical storage capacity during the
year. In February outdoor temperatures are low, which leads
to a low COP and hence higher electricity demand for heat
generation. This is why in the given Figure the electric
storage capacity for DHW is increasing. At the same time
low temperatures lead to an increase in supply temperature
for space heating, which increases the minimum allowed
temperature and reduces thereby the the possibility in storing
heat in the storage.
Fig. 3.
COP for the heat pump units in 2012.
(4)
The maximum water temperature at the heat pump outlet is
a further constraint that has to be respected. Since the heat
pump is used for the preparation of domestic hot water the
exit temperature limit is set 60◦ C and no backup heating is
be used.
3) Hydronic system and thermal storage: The hydronic
system of the building is considered by the means of the
needed minimum supply temperature to transfer the needed
heat. This temperature is based on a heating curve using the
mean ambient temperature of the last 24 hours Tamb24 as
base for calculation.
2
Tsupply = a0 + a1 · Tamb24 + a2 · Tamb24
(5)
The domestic hot water is targeted to be min 45◦ C in the
storage and the cold water storage is set to be at max at
17◦ C and min. 7◦ C.
The thermal storage itself is modelled as a perfectly mixed
tank with no losses. Thus the change in storage energy
content is the sum of inflowing and out flowing energy. The
available storage capacity Cap at each point in time t is
determined by its thermal mass C, the minimum temperature
Tmin (t) and the maximum allowed temperature of the heat
pump THP,max . When considering the equivalent electric
capacity of the thermal storage the COP has to be taken into
Fig. 4. Equivalent electric storage capacity of the thermal storage, when
heat pumps are assumed to cover 10% of the national residential heat load.
B. Formulation of the optimization problem
The optimal operation of a storage is strongly dependent
on the system characteristics, the heat pump and storage
capacity as well as the demand and supply profile. To access
the full potential of the heat pump storage system, the optimal operation is calculated using optimization. The objective
is to minimize the fluctuations of a given signal, which is
in our case the residual load, PV or wind power generation.
Standard deviation is used as measure for fluctuation thus
defining the need for load shift (see Section II-A). The
resulting equivalent objective function is:
min
Q̇HP
T
X
(P (t) + PHP (t))2
(7)
t=1
where P (t) is the target profile and PHP (t) is the heat pump
electric load defined as:
PHP (t) =
Q̇HP (t)
COP (t)
(8)
The optimization problem is bound to the maximum thermal
capacity Q̇max (t) of the heat pump unit and the maximum
and minimum storage capacity Capmin , Capmax . This leads
to the following boundary conditions:
demand are rising. It can also be seen that the fluctuations
in residual load are not simply the sum of the fluctuation
of wind and PV. This can be explained by a smoothing
effect due to overlaying PV and wind electricity generation
as well as the load. From Figure 5 the following conclusions
concerning the integration of Wind and PV into the power
system can be drawn:
1) If a high amount of PV should be integrated, balancing
capacity in summer is needed
2) If a high amount of wind, with the same generation
characteristics as in 2012, should be integrated, the
balancing capacity is needed almost constantly during
the year
3) Observing the residual load it is visible for the given
example, that the need for balancing capacity slightly
decreases during summer time.
s.T.
0 ≤ Q̇HP (t) ≤ Q̇HP,max (t)
Capmin (t) ≤ C(t) ≤ Capmax
(9)
(10)
∀t ∈ T
The storage content C(t) at each time is calculated as the
sum of the initial storage content C(t0 ) and the cumulated
inflows from the heat pump Q̇HP (t) and thermal demand
Q̇Demand (t):
C(t) = C(t0 ) +
T
X
((Q̇HP (t) − Q̇Demand (t))∆t
(11)
t=1
The optimization problem that is solved for each time
interval T is convex quadratic and solved with cvxopt [22].
IV. R ESULTS
In the following the results of a one year simulation
for the residential and office buildings are presented. The
calculations are done on a 15 minutes time step. The
German reference location Wuerzburg is used for weather
data obtained from [23]. Data for the load in the national
transmission grid, PV and wind generation data are obtained
from [24]. 2012 is chosen as year for investigation. The heat
load profile is scaled to cover 10% of the yearly energy
demand for space heating given in Table II. Domestic hot
water demand and cooling demand are put into relation
according to the results from the building model (see Section
III-A1).
A. Analysis of intra-day fluctuations
First, intra-day fluctuations and thus the need for load
shifting based on the standard deviations are analysed (see
eq. 2). Figure 5 shows the daily mean values of the intraday fluctuations in the German power system for each month
of the year. It can be seen that the fluctuations introduced
into the power system by PV are increasing in summer
time, due to an increase in PV electricity generation. Wind
power generation seems to be more stable, showing a slight
increase in fluctuation in winter time. Inspecting the residual
load, which is the demand in the grid minus PV and
wind generation, one can see, that fluctuations decrease in
summer, although PV production as well as the electricity
Fig. 5. Mean daily standard deviation of PV and wind generation and the
residual load in the German transmission grid for each month in 2012.
B. Changed heat pump operation
Figure 6 shows three operation modes of the heat pump
for an average winter day. When the heat pump is operated
without the use of storage (indicated as ”NoStorage”), the
heat pump follows the thermal demand of the building. As
a result the heat pump itself adds additional fluctuation to
the grid, mainly caused by the morning peak after a nightly
energy saving operation of the building’s hydronic heating
system. When the heat pump operates aiming to minimize
the squared electricity consumption (labelled ”before”), the
storage is being used for peak shaving of the electricity
consumption of the heat pump as well as shifting the demand
to times with favourable COP (i.e. warm afternoons). The
third case (labelled ”after”) shows the operation after load
shifting has taken place. In this mode the aim is to smooth
a signal (here: the residual load). It can be seen that the
heat pump is operated more actively during the 24 hours as
operation is increased compared to ”before” during the late
night hours and during midday. A decrease compared to to
the two other operation strategies can be seen in the morning
hours and the evening hours, where residual load is typically
high.
Fig. 6. Example of conventional and optimized heat pump operation when
smoothing the residual load.
heating and cooling demand is more equal in offices than
in residential homes. Thus when using heating and cooling
technologies for load shifting a more steady availability of
storage can be provided in offices. The shifted load, which
is shown in Figure 9 correlates with the energy demand up
to a certain extend. When comparing the heat demand for
January and February with the amount of energy shifted
it can be seen that although having a higher demand the
amount of shifted energy remains the same. It can also be
seen that the amount of shifted energy every day remains
almost constant during the heating season, indicating that
storage size rather than unit size or thermal demand is a
limiting factor for the shifting capability for the chosen
configuration. When inspecting load shifting for cooling a
strong correlation between demand and shifted energy can
be observed, indicating that limitations are in this case only
given due to a lack of demand. This is no surprise since the
system is dimensioned to serve the heating load which is in
the selected climate far greater then the cooling load.
Fig. 7. Mean residual load in the power system during winter, before and
after the load shifting operation of the heat pumps.
Fig. 8.
units.
Daily mean values of electric energy demand for the heat pump
C. Evaluation of the load shifting performance
The ability of ASHPs to balance the fluctuations shown
in Figure 5 is evaluated using two indicators. The first
is the total amount of energy that can be shifted and
thus be used for balancing. This is strongly dependent
on the storage size and the heat demand as explained in
Section II-B. In Table III the yearly energy demand and
the shifted energy for each scenario are listed. Figure 8
shows the mean daily energy demand for each technology.
Clearly the operation of the heating systems takes place
in winter, whereas cooling devices are operated during the
summer. The increase for cooling and heating demand goes
along with a decrease in COP for both applications due to
less favourable outdoor temperatures, and for space heating
higher supply temperatures to the storage. Thermal energy
demand for domestic hot water is almost constant during
the year. The resulting electricity demand, though is less in
summer due to increased COP of the heat pump system. It
can be seen that when serving 10% of the national heat load
by sector, the amount of energy used in residential homes
is considerably higher than for offices. The ratio between
When comparing the shifted energy in Figure 9 with
the intra-day fluctuations given in Figure 5 three main
observations are made:
1) The shifting potential of space heating systems in
offices and residential buildings shows a similar characteristic (high in winter) as the fluctuations in residual
load
2) Cooling demand and shifting potential are increasing
during the summer months. This correlates in parts
with the need for load shifting when integrating PV
into the system.
3) Domestic hot water load shifting capacity is almost
constantly available during the year.
In Figure 10 the ratio between the daily shifted electric
energy and the electricity demand for each technology are
shown. It can be seen that the share of demand that is
shifted is increasing with decreasing demand. This could
indicate increased flexibility of operation, when the thermal
demand is low compared to the capacity for heat storage and
generation.
V. C ONCLUSION
Fig. 9. Daily mean values of shifted energy for each month when balancing
the residual load.
Fig. 10. Daily mean values of the share of the shifted load compared to
the energy demand.
TABLE III
C OMPARISON OF THE YEARLY ELECTRICITY DEMAND AND THE
SHIFTED LOAD . S IMULATION RESULTS FOR 2012, WHEN COVERING
10% OF THE NATIONAL HEAT LOAD IN RESIDENTIAL AND OFFICE
BUILDINGS WITH ASHP.
Electricity
Demand
Smoothed
Signal
Residential
Heating
DHW
Cooling
Offices
Heating
Cooling
In this paper the potential of ASHPs for balancing fluctuations in the electric system has been analysed. The analysis
has been conducted for Germany for the year 2012, with
Wuerzburg as the reference climatic location. Offices and
residential buildings equipped with a heat pump sysem
including a thermal storage which supplies both heating,
cooling and domestic hot water has been investigated. As
shown in Section IV-A the need for balancing capacity
depends on whether it is PV, wind or the residual load that
is to be smoothed.
If the focus is on the integration of PV in the power
system, balancing capacity should be provided especially
during summer. For wind integration, a steady need for
compensating fluctuations during the year could be observed
in 2012. For smoothing the residual load more balancing
capacity should be present during winter times.
It has been shown that heat pumps can provide part of the
balancing power with a system sizing and configuration that
is common today. Depending on what kind of renewable
energy should be integrated, different heat pump applications such as space heating, cooling and the preparation of
domestic hot water can be used.
As there are more residential buildings than offices, the annual flexibility offered by residential buildings (5,4 TWh/a)
is about ten times as high as the flexibility offered by offices
(0,5 TWh/a), however the offices flexibility is available more
evenly throughout the year as they have heating flexibility
in winter and cooling flexibility in summer. The residential
buildings tend to offer their flexibility mainly in winter,
but the domestic hot water (DHW) demand (1,7 TWh/a) is
available throughout the year, which offers more flexibility
than the offices in total.
When integrating large quantities of heat pumps into the
power system without dedicated controls, negative effects
could occur. As shown in Figure 7 an operation without the
use of storage can lead to a morning peak and evening peak
when the heat pump is used for space heating or direct DHW
preparation.
Figure 10 shows that between 20%-30% of the electricity
demand for space heating can be shifted in wintertime. For
domestic hot water this number is about 65% throughout
the year. In the configuration used, between 40% to 90%
of the daily electricity demand for cooling could be shifted
depending on the time of year.
VI. O UTLOOK
Shifted Load
Wind
PV
[TWh/a]
ResidualLoad
[TWh/a]
[TWh/a]
[TWh/a]
14.6
2.4
0.8
3.4
1.7
0.3
1.6
1.2
0.2
1.9
1.6
0.4
1.4
0.3
0.41
0.1
0.3
0.07
0.3
0.07
As discussed in Section II-B, the influence of the shifting
frequency, the storage size, the ratio between storage size
and heat pump capacity as well as thermal demand needs
to be analysed in more detail, and will thus be the main
focus of further work. Costs and benefits of a changed heat
pump operation is also an aspect for further investigation.
As stated in Section V, control strategies that are capable
of serving the thermal load in an energy efficient manner
without adding problems to the power system, are needed
and will be further developed.
ACKNOWLEDGMENT
The work presented was developed within the Green
Heat Pump Project. This project has received funding from
the European Unions Seventh Programme for research,
technological development and demonstration under grant
agreement No 308816.
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