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This paper investigates heat pump systems in smart grids, focussing on fields of application and control approaches that have emerged in academic literature. Based on a review of published literature technical aspects of heat pump flexibility, fields of application and control approaches are structured and discussed. Three main categories of applications using heat pumps in a smart grid context have been identified: First stable and economic operation of power grids, second the integration of renewable energy sources and third operation under variable electricity prices. In all fields heat pumps - when controlled in an appropriate manner - can help easing the transition to a decentralized energy system accompanied by a higher share of prosumers and renewable energy sources. Predictive controls are successfully used in the majority of studies, often assuming idealized conditions. Topics for future research have been identified including: a transfer of control approaches from simulation to the field, a detailed techno-economic analysis of heat pump systems under smart grid operation, and the design of heat pump systems in order to increase flexibility are among the future research topics suggested.
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On heat pumps in smart grids: A review
David Fischera,b,
, Hatef Madanib
aFraunhofer Institute for Solar Energy Systems, Freiburg, Germany
bKTH Royal Institute of Technology, Stockholm, Sweden
Abstract
This paper investigates heat pump systems in smart grids, focussing on fields of application and control approaches that have
emerged in academic literature. Based on a review of published literature technical aspects of heat pump flexibility, fields of
application and control approaches are structured and discussed. Three main categories of applications using heat pumps in a smart
grid context have been identified: First stable and economic operation of power grids, second the integration of renewable energy
sources and third operation under variable electricity prices. In all fields heat pumps - when controlled in an appropriate manner -
can help easing the transition to a decentralized energy system accompanied by a higher share of prosumers and renewable energy
sources. Predictive controls are successfully used in the majority of studies, often assuming idealized conditions. Topics for future
research have been identified including: a transfer of control approaches from simulation to the field, a detailed techno-economic
analysis of heat pump systems under smart grid operation, and the design of heat pump systems in order to increase flexibility are
among the future research topics suggested.
Keywords: Heat Pump, Smart Grid, Demand Side Management, Energy Management, Controls, Thermal Storage, PV
self-consumption
Contents
1 Introduction 1
1.1 Changes in the energy system and the role of
heat pumps ................... 1
1.2 Aim of study .................. 2
2 Heat pumps in a smart grid context 3
2.1 The idea of a smart grid ............. 3
2.1.1 Time scales and the need for flexibility
in the power system .......... 3
2.2 Heat pump systems ............... 4
2.2.1 Heat pumps in brief ........... 4
2.2.2 Building level integration ........ 4
2.2.3 Consideration on flexibility of heat
pump systems .............. 6
3 Applications of heat pumps in a smart grid 6
3.1 Grid focussed .................. 7
3.2 Renewable energy focussed ........... 8
3.3 Price focussed .................. 9
4 Controls 11
4.1 Tasks and targets ................ 11
4.2 Hierarchy and levels of integration ....... 11
4.3 Classification of approaches .......... 12
4.3.1 Non-predictive Methods ........ 12
4.3.2 Predictive Methods ........... 13
Corresponding author. Tel.: +49 761 4588 5429. E-mail-address:
David.Fischer@ise.fraunhofer.de
5 Conclusion and recommended future research 15
5.1 Conclusion ................... 15
5.2 Recommendation for future research ...... 16
1. Introduction
Heat pumps (HP) are a wellknown technology for heating
and cooling of residential buildings. From 2010 to 2015 ap-
proximately 800.000 electrically driven heat pump units have
been sold in the the European Union (EU21) per year [1] adding
up to more than 7.5 million units. Thus, HPs play an increas-
ing role in the heating sector. In electrically driven heat pumps,
electricity is used to lift low exergetic heat to a higher tempera-
ture and consequently higher exergy level by running a vapour
compression cycle. The heat is taken from sources like ambi-
ent air, water or ground. Heat pumps have been known as a
low CO2emission technology for heat generation in the res-
idential sector. Heat pump coefficient of performance (COP)
and the CO2emissions of electricity generation determine the
emissions during the operation phase of the heat pump.
1.1. Changes in the energy system and the role of heat pumps
Over the recent years three main developments that affect the
role heat pumps in the energy system are observed.
First, due to progress in heat pump development [2], COPs
of heat pumps are increasing. An evaluation of over 800 heat
pumps at nominal conditions listed in reference [3] shows that
COP values for market available heat pump units lie in the range
of 3.2 to 4.5 for air source heat pumps (ASHP) and between
Preprint submitted to Elsevier October 4, 2017
4.2 and 5.2 for ground source heat pumps (GSHP), for testing
conditions according to EN 145111.
A second trend besides growing COP values is the growth
of renewable electricity (RE) generation from wind and photo-
voltaic (PV) plants. On the level of individual households this
results in the emergence of prosumers, consuming and produc-
ing electricity at different points in time[4;5]. In 2015 more
than 32% of the annual electricity demand in Germany is met
by renewable sources [6]. On the long run, the commitment
to reduce the use of coal-fired power plants as decided at the
Paris Climate Change Conference 2015 (COP212), will hope-
fully lead to lower CO2emissions for electricity generation and
thus most likely for heat pump operation. In a simulation and
optimization study, which investigates different pathways to a
renewable German energy system, [79], it was found that heat
pumps can play a major role for de-carbonization of the heating
sector. For the Danish case it is shown in reference [10] that
district heating schemes with combined heat and power plant
(CHP) and individual residential heat pumps offer the best solu-
tion for transforming the residential heat sector towards reduced
CO2emissions. Reference [11] highlights the CO2emissions
reduction potential of air source heat pumps in an exploratory
simulation for the U.K. in a 2050 scenario.
With increasing generation of electricity from wind and PV
new challenges arise in the power sector. Traditionally elec-
tricity generation would follow the demand. This is changing
towards a system where increasing shares of the demand will
be constantly adjusted to follow a fluctuating electricity gen-
eration. This leads to an increased need for flexibility on the
demand side [12;13] and the need for storage capacity [14] to
guarantee the balance of electricity demand and generation.
A third major development over the recent decade is the in-
creased availability of small and relatively performant com-
puting capacities, progress in algorithm design and the fur-
ther spread of wireless communication networks with sufficient
bandwidth to exchange measured data or control signals. The
vision of the internet of things [15], where devices of all kinds
are connected and help solving problems or increasing comfort
of humans has emerged and partly became reality. In the field
of energy, particularly in the power system, the concept of a
smart grid has emerged.
But what is a smart grid exactly and what roles will heat
pumps play in it? There is no clear answer to these questions,
yet. The interpretations of a smart grid, the definition of its
system boundaries and possible applications of heat pumps are
diverse. This diversity in research interests and interpretations
is illustrated by Fig.1showing the 50 most frequent words ap-
pearing in the title of scientific articles on a query of Reuters
Web of Science using “heat pump” and “smart grid”.
The topic of heat pumps in smart grids is of high relevance
and considerable knowledge has been built over the recent
years. From 2007 to 2015 the topic of heat pumps in smart
grid has come into the focus of research. For this period a to-
1TAir=2C, TWater=35C;TBrine =0C, TWater=35C
2(FCCC/CP/2015/L.9/Rev.1)
Figure 1: Wordcloud of the 50 most frequent words in paper titles when query-
ing for "heat pump", "smart grid" at Reuters Web of Science.
tal of 121 publications were listed on Reuters Web of Science3
shown in Figure 2.
Figure 2: Number of publications appearing on Reuters Web of Science query-
ing for "smart grid" refined by "heat pump"(16/1/2016).
1.2. Aim of study
The aim of this study is to provide a structured overview of
the current discussion on heat pumps in a smart grid context
with focus on residential applications. The study aims at pro-
viding new researchers with a quick start on the topic and expe-
rienced researchers with a summary of findings, structure and
guidance for future research. Two main subcategories are anal-
ysed in further detail:
1. Applications of heat pumps in a smart grid context.
2. Control schemes used for these applications.
Heat pump technology in a smart grid context deserves a de-
tailed and separate analysis on its own, since it is a key tech-
nology linking the electric and the thermal energy sector. Fur-
thermore, heat pumps show technology specific characteristics,
explained in Section 2.2, that need to be considered when dis-
cussing their use to provide flexibility to the power system.
3accessed 16/1/2016 querying for "smart grid" refined by "heat pump"
2
For this review over 240 studies presented at international
conferences, in peer reviewed journals or as academic theses
were analysed and the most interesting and relevant ones (in the
authors opinion) were selected. In Section 2concepts and sys-
tem definitions of heat pump systems in a smart grid as well as
technical aspects regarding heat pump flexibility are discussed.
The main applications of heat pumps in a smart grid context are
presented in Section 3. The concepts and ideas used for control
are presented in Section 4. The paper concludes in Section 5
with a recommendation for further research.
2. Heat pumps in a smart grid context
As a first step, important concepts and aspects concerning
smart grids, heat pump systems and technical aspects regarding
the flexibility of heat pumps shall be discussed.
2.1. The idea of a smart grid
The term "smart grids" found in academic literature is used
in many ways and is used differently by different authors de-
pending on the parts of the energy system that are considered.
As an example the German grid agency defines smart grid in a
way that only parts of the actual power grid are considered. In
this definition the target of a smart grid is to optimally use the
existing line capacity, manage congestions and improve safety.
The benefits of a smart grid following this definition are mainly
for the grid operator. Savings are achieved by decreasing the
need for additional line and transformer capacity. A clear dis-
tinction is made between capacity and energy. Devices used to
match generation and demand with the target of optimal power
plant use and dispatch are seen in the context of energy and are
not part of the core smart grid as such [16].
A similar but less strict line of argumentation is followed by
the US department of energy [17], which excludes devices such
as wind turbines, plug-in hybrid electric vehicles and solar ar-
rays from the smart grid. With this definition only control and
communication devices that provide the possibility to integrate
and intelligently control distributed generation and consump-
tion devices are seen as smart grid components.
A wider perspective is taken in reference [18] where the vi-
sion of a smart grid is defined as: "... an electric grid able to
deliver electricity in a controlled, smart way from points of gen-
eration to consumers that are considered as an integral part of
the SG since they can modify their purchasing patterns and be-
haviour according to the received information, incentives and
disincentives...". The focus on consumer flexibility is a cen-
tral point of demand response and demand side management
(DSM) [5;12;1924] as well as decentralized energy manage-
ment [25;26] approaches.
A wider, more holistic argumentation is stated in reference
[27], where it is suggested to extend the focus of a smart elec-
tric grid towards a whole energy system approach including not
only electric demand and generation but as well the heat and
transportation sector.
All of the recent literature has in common, that the chal-
lenges of integrating fluctuating renewable energy generation
are tackled with a set of distributed controllable devices. This
reaches from pure power line components as stated in refer-
ence [16] over heat generation units [27] all the way to demand
side measures where operation of individual household appli-
ances [28;29] or even persons’ electric consumption behaviour
is changed [3032].
The main motivations for a smart grid, as stated in academic
literature, are:
1. Minimum cost for installation and operation of the electric
grid.
2. Stable operation of the electric grid within the allowed
boundaries for frequency, voltage and transmission capac-
ity.
3. Optimal use of the generation resources mostly targeting
minimum CO2emissions or minimum cost.
In the context of a smart grid, heat pumps are seen as part
of the demand side that can be actively managed to support the
realization of a smart grid [12;3342]. Coupling heat pumps
to thermal storage or actively using buildings’ thermal inertia
offers the possibility to decouple electricity consumption from
heat demand, which brings flexibility in operation that can be
used in a smart grid.
2.1.1. Time scales and the need for flexibility in the power sys-
tem
The need for flexibility in the power system is frequently mo-
tivated by an increase in renewable energy [12;43] and the re-
sulting need for an ability to react or plan ahead [18] for safe
and efficient power system operation. A transition towards a re-
newable electricity sector means that all services that are nowa-
days provided by conventional power plants will have to be pro-
vided by other devices.
For an individual device the definition of flexibility provided
by Eurelectric [43] highlights the important properties as seen
from the electric point of view:
"On an individual level flexibility is the modification of gen-
eration injection and/or consumption patterns in reaction to an
external signal (price signal or activation) in order to provide a
service within the energy system. The parameters used to char-
acterise flexibility include the amount of power modulation, the
duration, the rate of change, the response time, the location
etc." Figure 3shows the time-wise characteristics of selected
Figure 3: Time scales of selected areas and applications in the power system.
3
fields and mechanisms in the power system, where flexibility of
the demand side might be used to create benefits. The mecha-
nisms used to enable flexibility change with the service in the
power system that is to be provided [12;18;44]. Depending on
the speed needed for reaction different ways are used to activate
flexibility. Direct activation signals are used to provide operat-
ing reserve. For primary reserve those are based on direct mea-
surements of the grid frequency on-site; real time signals are
sent to the devices for spinning and non-spinning reserve. An-
other way to activate flexibility is the use of prices. Those are
with decreasing dynamics: Real time pricing, day ahead pricing
and time of use tariffs [18].
The potential of heat pumps to provide flexibility to the
power system depends on the case of application and the char-
acteristics of the HP system.
2.2. Heat pump systems
A brief description of the most important features consider-
ing residential heat pump systems with respect to smart gird
applications is given in the following. For more detailed in-
formation on HP technology and different applications refer to
references [2;4549] to get started.
2.2.1. Heat pumps in brief
Heat pumps are used to provide heat using thermal energy
from a heat source and additional energy needed for compres-
sion. The energy needed for compression depends on the com-
pression principle. In this paper the focus is on electrically
driven, vapour compression heat pumps.
A basic vapour compression heat pump cycle comprises two
heat exchangers, one acting as an evaporator and another as a
condenser, a compressor, and an expansion device. These four
components, together with the working fluid enable the pump-
ing of heat from the low temperature renewable heat source
such as ambient air, ground, lake or sea water to higher tem-
perature useful for space heating and/or domestic hot water.
In such heat pumps a stream of liquid refrigerant is evap-
orated at low pressure using the heat source. The refrigerant
vapour is compressed, leading to a temperature increase. This
compressed refrigerant stream is condensed at high pressure
and thus high temperature. The resulting heat is transferred to
the heat sink. The now liquid refrigerant is expanded to the low
pressure level and the cycle goes on.
Depending on the source and sink temperatures only little
additional energy is needed for the compression process. For
compression in residential applications typically a compressor,
driven by an electric motor is used. The coefficient of perfor-
mance COP of a heat pump is defined as the ratio of usable heat
and the needed electricity for this.
COP =
˙
Quse
Pel
(1)
Depending on the system boundaries the usable heat ˙
Quse and
the electrical energy necessary Pel include different compo-
nents, such as compressor, fans and auxiliary systems [50]. The
characteristics of COP can be explained using a simple Carnot
model as found in thermodynamic textbooks like [51]:
COP η·Tc
TcTe
(2)
In this simplified model all additional losses and deviations
from the Carnot process are summarised in the efficiency η.
It becomes visible that the temperature lift from evaporator
to condenser Te,Tchas a major impact on efficiency. The con-
denser and evaporator temperature determine the pressure dif-
ference that needs to be overcome by the compressor. Increas-
ing the temperature lift leads to reduction of heat pump effi-
ciency. The sink temperature depends on the temperature level
in the heating distribution system which might be radiator or
floor heating. The performance of the heat exchangers and the
current thermal output influence the temperature of evaporation
and condensation.
2.2.2. Building level integration
An exemplary layout of a heat pump system is shown in Fig-
ure 4. Air is used as heat source and water is used for the sink
side. The system consists of the heat pump unit, a thermal stor-
age tank for domestic hot water and a storage tank to buffer
space heating demand. Floor heating is used as heating distri-
bution system. An electric heater is used as an auxiliary heater
to cover peak demand. Valves and pumps are needed for oper-
ation of the hydronic circuits.
Figure 4: Exemplary layout of a residential HP system [42].
Generally a residential heat pump system can be character-
ized by the type of heat source and sink, the technical features
of the subsystems such as compressor type, refrigerant cycle
properties and controls, and the heating system of the building.
Figure 5shows the main distinctive features of residential heat
pump systems.
The type of heat source and sink are important when charac-
terizing heat pumps. The source and sink temperatures directly
influence the unit efficiency as an increased temperature dif-
ference between source and sink leads to a lower heat pump
COP. Hence the COP changes during the course of the year. As
seasonal fluctuation in ambient temperature are usually higher
than those of the soil or ground water, air as heat source leads
to stronger seasonal changes in COP compared to ground as a
4
Figure 5: Main distinctive features of residential heat pump systems.
heat source. Furthermore using air as heat source, frosting of
the evaporator can occur and additional energy is needed for
defrosting.
In residential buildings, the type of heat sink is linked to the
type of heating distribution and storage system. Water is used
for radiator or floor heating systems and the preparation of do-
mestic hot water (DHW), whereas air is mostly used in ventila-
tion and heat recovery applications. The temperatures that need
to be provided by the heat pump differ depending on the re-
quirements of the heat sink. Depending on the building physics
commonly the highest temperatures are needed for the prepara-
tion of DHW (up to 65 C) followed by radiator heating (up to
55C), ventilation (up to 40C) and floor heating (up to 35C)
[52].
The type of storage used and the way it is integrated into the
building energy system plays an important role when consid-
ering heat pumps for DSM applications. Water, phase change
material or the building thermal inertia is frequently used as
thermal storage material.
When water is used for heat distribution this offers the possi-
bility to easily store heat in tanks or to thermally activate build-
ing parts, which is done in reference [34;5364]. When using
building as storage, the buildings’ wall or ground temperature
and consequently the indoor temperature change when heat is
stored. Hence, thermal comfort could change and is an impor-
tant point to be considered when actively using the building.
Reference [65] show that building physics play a major role in
possible load advance with heat pumps. They conclude that for
the U.K. building stock heat pump blocking for 1.5 hours is pos-
sible without violating indoor comfort, which can be increased
by adding buffer tanks and better insulation of the buildings.
Reference [66] highlights that the amount and composition of
the floor material impacts indoor comfort when load shifting is
done with heat pumps. They suggest the use of PCM in build-
ing material to improve comfort and flexibility. A comparison
of storage tank, considered as active storage, and the building
mass, considered as passive storage is done in reference [67].
Heat pump flexibility is used to integrate wind power genera-
tion. The authors conclude that using building thermal mass
as passive heat storage offers the most cost effective solution
compared to heat accumulation tanks for the investigated case.
Most of the heat pump systems in Europe are connected to a
hydronic system and a thermal storage tank, which is reflected
in references [35;37;40;68?89]. In Germany approx. 90%
of the existing and 80% of the new built buildings are equipped
with thermal storage tank when a heat pump is used [90]. As
newer buildings are often well insulated and equipped with
floor heating, with high thermal inertia sobuffer storage tanks
are not always necessary. Using tanks for load shifting min-
imises the risk of high fluctuation of indoor temperature and
thus comfort violations since the indoor set-points remain un-
changed. This requires thermostats in the rooms and mixing
valves in the supply and return pipes of the heat distribution
system to keep the room temperature at the wanted level whilst
increasing the temperature of the storage.
When ground source heat pumps are combined with renew-
able electricity such as solar PV and wind, there is a possibility
to convert the renewable electricity to heat and store the heat
seasonally in the borehole or a borehole field comprising sev-
eral arrays of boreholes as shown in reference [91;92].
The annual performance of the heat pump systems is strongly
influenced by how the heat pump capacity is regulated depend-
ing on the variable heat demand. Fixed speed heat pump units
are operated in an on-off manner, whereas variable speed heat
pumps allow a continuous regulation of the compressor speed
over large parts of the operation range. This allows a control of
the thermal output or the electric demand. For applications in
Swedish single family houses it is shown in reference [93] that
variable speed heat pumps can but do not necessarily improve
system efficiency. For smart grid application the possibility to
increase or decrease electric consumption offers higher opera-
tional flexibility which is for example used in order to improve
power quality [72;94] or to increase local PV self-consumption
[78;86]. Reference [95] demonstrated the concept of rapidly
adjusting compressor speed to provide ancillary services on a
lab scale.
The choice of refrigerant and the properties of the thermo-
dynamic cycle influence the allowed temperature range of op-
eration, part load characteristics and heat pump efficiency (for
further reading see reference [2;45]).
5
Depending on the type of heating system, additional heat
sources can be used together with the heat pump. If the entire
heat demand is supplied by the heat pump, the system is re-
ferred to as monovalent system. Furthermore an additional heat
source such as an electric heater (mono-energetic system) or a
fossil fired boiler (bi-valent system) can be used to cover de-
mand peaks. The type and use of the auxiliary heater influences
system seasonal performance factor (SPF) and CO2emissions
during operation.
2.2.3. Consideration on flexibility of heat pump systems
The potential flexibility that can be provided by heat pumps
for smart grid purposes is influenced by different factors. In
reference [65;76;96] a structured assessment of the load shift
problem with heat pumps is described and extended in the fol-
lowing.
First of all appropriate controls and communication inter-
faces between the heat pump unit, the building energy man-
agement system and the power system or an external body are
required. If these are given, the potential flexibility is mainly
determined by the thermal demand, the heat pump size, the
storage size, the dynamic system properties and the flexibility
requirements from the power system. Figure 6depicts the main
factors influencing heat pump flexibility.
The thermal demand and its profile over time determines the
maximum amount of energy that can be shifted within a given
time period. In residential applications thermal demand is the
sum of space heating load and the demand for domestic hot
water (DHW). Depending on the building type, the location and
the occupants behaviour, the energy demand varies during the
day and year.
The heat pump capacity limits the possibility to increase or
decrease HP’s electric consumption by switching on or off, or
by ramping the heat pump capacity up or down. The difference
between the actual heat demand and the heat production of the
heat pump unit determines the change in energy content of the
storage. If the heat pump capacity is equal or smaller than the
current heat demand the only flexibility option is reducing heat
production of the heat pump, given that the storage is not empty.
In case that thermal capacity of the heat pump exceeds thermal
demand, HP thermal output can be increased to charge the stor-
age. Since COP and maximum thermal capacity are dependent
on source and sink temperatures, the available flexibility is not
constant during the course of the year. Falling outdoor tempera-
tures usually requires higher temperatures in the heating circuit
to transfer the needed heat to the building, which leads to higher
sink temperatures and hence reduced COP and reduced maxi-
mum HP capacity. This leads to reduced flexibility in times of
high heat demand [76].
The type and capacity of the thermal storage determines how
much energy can be shifted over a certain period of time. For
water tanks, the maximum allowed temperature in the tank is
limited by the maximum possible temperature of the heat pump
and the safety issues. As a consequence, usable tank capacity
decreases with decreasing ambient temperatures as the mini-
mum required tank temperature increases. For building activa-
tion, indoor comfort is the most critical point. Building thermal
mass, solar and internal gains of the building and influence the
available storage capacity over the course need to be consid-
ered.
The amount of energy that can be shifted with a given storage
capacity depends on the charging and discharging frequencies.
The charging and discharging is determined by the flexibility
requirements from the power system, the thermal load profile
and the applied control strategy. This determines the number of
charge and discharge cycles per day. A high number of cycles
result in an intensive use of the storage and a higher amount of
shifted energy compared to slow infrequent charging and dis-
charging of the storage.
Finally the dynamic properties of the heat pump unit are im-
portant for flexibility. The speed of response is limited by the
maximum allowed change rate of compressor speed and thus
power consumption over a certain time. A minimum run and
pause-time requirement as implemented in most heat pumps
further decreases flexibility. Since frequent switching events
reduce the lifetime of the heat pump this should be avoided,
which further reduces heat pump flexibility.
Figure 6: Important points that influence flexible operation of heat pumps sys-
tems.
3. Applications of heat pumps in a smart grid
Integration into a smart grid will change the way heat pumps
are used. This leads to new requirements for HP control and
design. In fact, some applications in a smart grid might be more
suitable than others due to the technological characteristics of
heat pumps.
Over the recent years different smart grid applications and
control approaches for HPs have been covered by a number
of scientific research projects and publications. The fields of
application and conditions under which the HPs operate vary
significantly from study to study. Nonetheless, they can be cat-
egorized in three main domains (see Figure 7):
Provision of ancillary services for the power grid, some-
times referred as grid-friendly operation.
Facilitate the integration of renewable electricity on build-
ing, distribution grid and power system level.
6
Operation of heat pumps under variable electricity prices.
Clearly these applications overlap and are partly mutually de-
pendent. However, most studies focus primarily on one of these
aspects. An overview over the studies, their key findings and
references is given in Table 1.
Figure 7: Main fields of applications with heat pumps in a smart grid context,
regarding the use-cases presented in academic literature.
3.1. Grid focussed
In the category of grid focussed applications, heat pump op-
eration is aimed at providing ancillary services to the grid to
allow a stable and cost efficient operation of the electric grid.
The list of ancillary services according to the references [97
99] contains:
Voltage control
Congestion management
Provision of spinning and non-spinning reserve
In voltage control applications the heat pump is employed
in the electric distribution grid to guarantee that the voltage is
within the allowed limits [72;94;100]. Essentially, this means
reducing HP’s active power demand in times of local under-
voltage and increasing it in times of over-voltage. Too high
values for local voltage in distribution grids may occur when
PV installations are present and feeding in electricity [94;100].
Problems with too low voltage values are caused by high de-
mands and can be caused by heat pumps itself [94;101]. Re-
ducing and coordinating heat pumps’ active power demand in
such situations helps to stabilize local voltage. The References
[72;94;100] use a combination of simplified heat pump system
models to calculate the power demand at the buildings grid con-
nection point and a simulation of the electric network to study
the impact on local voltage. The results show that HPs can help
overcoming over-voltage problems caused by PV but the appli-
cability is limited by the seasonal mismatch of heat demand and
PV production [72;94;100;102]. Power consumption of a vari-
able speed heat pump can be adjusted in reference [94] accord-
ing to local voltage conditions. Voltage stabilisation is achieved
using a droop control, which adjusts compressor speed propor-
tionally to the level of voltage violation. A piecewise linear
control characteristic is implemented depending on heat pumps
actual working point.
The provision of reactive power with heat pumps for voltage
control is not discussed although theoretically possible using
the inverter.
An application closely related to local voltage control is con-
gestion management in the distribution grid to avoid limita-
tions in the transformer and line capacity. In this context, heat
pumps are operated to avoid transformer overloading [40;103]
thereby helping to reduce or postpone investments for grid re-
inforcement [41;101;103]. However it has been shown that
increasing the number of heat pumps in a distribution grid can
possibly lead to higher transformer loadings and lower voltage
levels [94;101]. To reduce transformer loading heat pumps
can be switched depending on the state of the transformer as
done in reference [40]. Operation can be planned to avoid si-
multaneity of load peaks on household and grid level, which is
discussed in reference [41] as one strategy. Furthermore volt-
age and transformer loading problems can be prevented by a
an optimised planning of HP operation. Nonetheless, in most
studies a real-time control strategy is used for the HP to react to
unforeseen events [40;72;73;103], sometimes combined with
a day-ahead planning to avoid operation during critical periods
[104;105]. The response to problems in transformer loading or
local voltage is done by switching on heat pumps when voltage
levels are below a certain threshold or vice versa [40;100]. For
this case minimum unit run times of the heat pumps should be
considered [100].
In the field of grid focused applications the reduction of
peaks in load and feed-in plays an important role. The general
set-up of these studies involves a) putting the system boundary
at the grid connection point of the individual household and try-
ing to reduce individual load peaks or b) using an aggregated
load profile (usually on a national level) as a signal for load
shifting and trying to reduce load peaks on this level. Target
of operation is a) to avoid peaks (positive or negative) in the
house’s load profile or b) to shift the load of the heat pumps
to hours of low aggregate loads [23;33;36;39;42;59;74
77;81;82;106;107;107?110]. Shifting is typically achieved
by planning operation on a day-ahead basis.
Potential benefits of peak reduction on an aggregate level in-
volve lower electricity generation costs (merit order effect), less
need for peak generation reserve power plants and less need for
transmission capacity. The generation of renewable electricity
can lead to negative load peaks (valley) on aggregate and indi-
vidual level. Reducing these peaks (valley filling) at individual
level could lead to decreased costs for grid connection depend-
ing on the applied electricity pricing scheme. When demand
side management is done to counteract feed-in peaks from re-
newables, this leads to an increased capacity of the electric grid
to integrate renewable electricity generation, especially on the
local distribution grid level (in the case of negative peaks caused
by a feed-in surplus from renewable energy sources).
To balance electricity generation and demand, and ensure
a stable frequency in the electric grid spinning and non-
7
spinning reserve capacities are available to the transmission
system operator (TSO). The reserve capacity are generation
units or electric consumers that can be regulated upwards and
downwards on demand. The time scales for regulation vary be-
tween a few seconds (primary reserve), to a few minutes (sec-
ondary reserve) up to more than ten minutes (tertiary reserve).
Although operation of small responsive loads [111;112] like
heat pumps on the reserve markets is heavily discussed in re-
view articles on demand side management, flexibility and smart
grid [12;18;24] the examples concerning heat pumps on that
field are limited [70;99;100;104;113120]. In most coun-
tries the organisation of reserve provision is done using market
mechanisms to decide which units will be used. To participate
in the reserve markets a minimum unit or pool size is needed
in most countries (5 MW for Germany, 10 MW for northern
European countries). Hence using heat pumps in the reserve
markets leads to the challenge of operating a large number of
small units. This includes:
Predicting the flexibility of a heat pump pool in a way that
it can be traded in the reserve markets.
Planning of bidding strategy and scheduling of the pool.
Control of a large pool after a reserve power call has been
received.
The studies on reserve power with heat pumps consequently
put the focus on three aspects: First, forecasting the flexibility
of a pool of heat pumps [70;121;122]. Second, the calculation
of a bidding strategy and evaluation of market attractiveness
[114;120;123;124]. Third, controls of a pool of heat pump for
participating in the reserve markets [113;115;117;119;123;
125].
In references [116;124] forecasting the heat demand and
electric demand of a heat pump pool is done using a lumped
model. To supply the electricity at lowest price, an optimized
operation schedule of the pool is calculated on a day-ahead
base. This schedule is adjusted intra-day if needed. If econom-
ically attractive, participation on the reserve markets is done.
A control approach focusing on trajectory tracking of a pool of
heat pumps, as needed in case of reserve power calls for the
pool, is performed in references [113;117;119;123;125]. A
heuristic strategy based on a sorting algorithm or priority stacks
of available devices is used for unit dispatching and is reported
to work for a heat pump portfolio of 10,000 units. In this ap-
proach the most suitable units are successively turned on or off
to reach the wanted power output. The approach is successfully
demonstrated in a field test with 54 heat pumps [115].
3.2. Renewable energy focussed
The main targets of heat pump operation in renewable energy
focussed papers are an increased utilization rate of renewable
electricity, a reduction of feed-in peaks and smoothing of the
residual load curve. Special attention is paid to the integra-
tion of wind and PV electricity as the two main fast growing
sources of fluctuating renewable electricity generation.
The references [83;126] show that the integration of wind
power on building level can be supported by heat pumps and
the required electricity from the grid can be reduced up to 95%.
Reference [83] highlights the benefits of variable speed heat
pumps for this case as they are able to constantly adjust electric-
ity consumption. However since wind power plants are mainly
installed in capacities above 1 MW, utilization of of the gen-
erated electricity has to be coordinated between several heat
pumps. For this purpose heat pump systems may receive an
external signal (like prices or current wind power production)
to adjust their operation. Electricity generation from wind,
the resulting negative peaks and fluctuations are highest dur-
ing winter season as references [76;127] show for Denmark
and Germany. This corresponds well with the heat demand and
the seasonal variation in load shifting capacity of heat pumps
[54;73;76;96]. Reference [128] concludes that even without
adjusted controls, the electricity demand of heat pumps matches
the availability of electricity generated by wind power in Den-
mark. Furthermore the references [128;129] and reference [36]
show that on an aggregate level heat pumps, operated in an op-
timal way, can be used to increase the absorption of wind power
in the power system while at the same time reducing the need
for peak capacity and thus costs.
The integration of PV is discussed mainly on two lev-
els: First, on the level of individual households, where self-
consumption is the focus of most studies, and second on the
level of the electric distribution grid, where a reduction of feed-
in peaks is the focus.
In many countries PV electricity generated locally has be-
come cheaper than the electricity buying price for households
and self-consumption has become an economically viable op-
tion. In this application which is presented in references
[26;37;71;75;77;78;83;84;8789;130133] heat pumps are
used to increase the self-consumption rate of locally generated
PV. The self-consumption rate is the share of on-site consumed
PV electricity with respect to the total PV generation over the
course of the year.
Increasing PV self-consumption is achieved by shifting heat
pump operation to hours where PV electricity generation ex-
ceeds household electricity consumption. If information about
PV generation and demands are known ahead of time HP oper-
ation can be planned ahead. If only real-time measurements are
available HP operation is triggered when PV feed-in exceeds
a threshold. In the references [86;134] compressor speed of
a variable speed heat pump is adjusted real time to minimise
interaction with the power grid.
The achievable self-consumption rate varies depending on
the size of the PV plant, the thermal and electric demand of the
household, the size of the thermal storage and the used controls
of the heat pumps. Self-consumption rates from 30% up to over
65% are reported [71;86;88;134]. A limiting factor for self-
consumption with residential heat pumps is the seasonal mis-
match between PV generation and space heating demand. Dur-
ing summer time PV electricity generation exceeds HP electric
demand and the opposite occurs during winter times given cen-
tral European climate and a reasonably sized PV installation.
In reference [86] for German climate and building condi-
8
tions self-consumption rates could be increased by up to 10%
when adding a heat pump to a single family house with PV.
In reference [78] it is shown that adjusting controls for vari-
able speed heat pumps can further increase self-consumption
by around 7%. In reference [86] the use of variable speed
heat pumps leads to up to 14% higher self-consumption rates
compared to on-off systems, depending on the building energy
standard (higher for older buildings). The advantages of vari-
able speed heat pumps decrease with increasing PV size. In
this case the benefits of modulation decrease as electricity sur-
plus is sufficient to power the on-off heat pump. An option to
further increase PV self-consumption is to allow higher stor-
age temperatures when the HP is operated with PV electric-
ity. Increased self-consumption might come at the cost of de-
creased system efficiency, though. Depending on the control
approach the temperatures of the storage tank might be kept
on an unnecessary high temperature level, already early in the
day or even for several days in a row if controls mainly fo-
cus on the maximisation of PV self-consumption. Predictive
controls can be used to only store the heat needed for the com-
ing period, thereby minimising losses whilst maximising self-
consumption. The benefits of increased thermal storage to in-
crease PV self-consumption seem limited [38;74;75;78;134]
as storage losses or investment costs quickly overcompensate
additional self-consumption gains. A decisive parameter for
the potential benefit of storage is the frequency of sunny and
cloudy days and the structure of thermal demand.
Solar fraction is another parameter that is frequently used
when PV heat-pump applications are discussed. The solar frac-
tion is the annual share of the heat demand that can be supplied
using heat generated with PV electricity. For the German case
values of about 25% up to 40% are reported in the references
[77;86].
On the level of the electric distribution grid the goal of opera-
tion is to reduce feed-in peaks caused by PV. As feed-in of mul-
tiple PV units occurs locally approximately at the same time,
reducing the feed-in peak is needed to allow stable operation of
the distribution grid and to increase the tolerance towards inte-
grating PV into the power system. The strategies for this are
discussed in Section 3.1. A reduction of peaks of up to 30% to
55% is reported in the references [74;75;88].
3.3. Price focussed
Operation under time variable electricity prices is the third
major category of studies. Clearly, prices are a vehicle to in-
centivise a certain electricity consumption behaviour. Prices
are used to convey information about critical events, capac-
ity limits, predictable load and generation situations and con-
gestions or simply to reflect the real-time or day-ahead events
like surplus renewable energy generation. Variable electricity
prices are closely linked to the grid and renewable energy fo-
cussed applications, and are considered to be a central compo-
nent of the smart grid. Different pricing schemes are applied
in studies concerning heat pumps, which makes it hard com-
paring numbers on cost savings. Besides the level and the ra-
tio of high to low prices the main difference between different
pricing schemes lies in their timely characteristics. Time of
use (TOU) prices such as classical high-low tariff schemes as
used in the references [33;69;85;135;136] might be static
over a long period (up to many years), whereas dynamic prices
as used in the references [23;3638;56;57;59;64;77;82;
103;114;115;118;119;123;134;137;138] might change
at daily (day-ahead pricing) or even shorter intervals (real-
time-pricing). A frequently used price signal are the day-
ahead electricity spot price or a price that is based on it. When
prices are known ahead of time, heat pump operation can be
planned using heuristics or optimal control methods, which are
discussed in Section 4.3.2 and used among others in the ref-
erences [37;57;69;77;136;139]. For more information on
market design and prices see reference [140].
The references [74;77] state that operating heat pumps with
day-ahead electricity spot prices of the European Power Ex-
change EPEX, leads to a shift of heat pump operation towards
night time when costs are low. In reference [77] it is shown
for an ASHP that operation along time variable prices might
lead to reduced HP efficiency due to higher storage and lower
ambient temperatures during operation and increased part load
ratios. The fact that lowering operational costs might not lead
to higher efficiency is also stated in reference [141] where 8%
lower electricity costs but 2% higher electricity demands are
reported. In reference [82] even up to 19% higher electricity
consumption is reported due to load shifting.
Furthermore, it is hard to quantify the benefits of changed op-
eration as the cost savings that can be achieved strongly depend
on the price assumptions of the individual study and the extend
of idealisation in the assumptions. Reported savings reaching
from 7% [57] up to around 35% [56]. Structure and volatility
of prices, the quality of forecasts and a information about the
system are decisive parameters influencing the results.
Changing operation in order to minimise costs might not only
lead to increased energy consumption but also to indoor com-
fort deterioration as highlighted in the references [57;139] by
showing the pareto curves for comfort violations vs. economic
savings.
9
Table 1: Summary of the main applications and the commonly selected approaches and reported results.
Applications Target Findings References
Grid Voltage Con-
trol
Keep voltage in
tolerance band
Reduce critical
hours
Reduce grid rein-
forcement
Significant reduction of voltage problems with appropriate con-
trols
If HP is not controlled properly, could lead to voltage problems
Limited beneficial effects of storage increase
[40;72;73;94;100;
101;104;105;142]
Congestion
Management
Keep transformer
load within tolerance
Reduce peaks
Critical situations in the distribution grid appear mainly in sum-
mer (PV) or cold winter (HP)
Controls can help reduce load peaks in most cases but not al-
ways
HP can help reduce grid problems induced by PV, but potential
is limited
Potential of HP for grid services shows strong seasonal depen-
dency
Grid reinforcement due to increased HP penetration can be
avoided or limited with appropriate controls
[33;3941;81;103;
106;107;142? ?
146]
Operating Re-
serve
Providing reserve
power
Pools of heat pumps are able to operate on the reserve market
Focus of studies on development and demonstration of control
strategies
A bidding and a reference tracking strategy is proposed as con-
trols
[70;100;104;113
120]
RE Wind Integra-
tion
Increase wind
consumption
Decrease fluctua-
tions and peaks
Reduce carbon
emissions
Mostly focus on aggregated load profile
Reduced need for peak power plants based on flexible HP oper-
ation
Reduced imbalances
Correlation of wind availability and space heating demand
(northern/central Europe)
[76;83;85;96;107;
126;128;147149]
PV Integra-
tion
Increase self-
consumption rate
Reduce load peaks
Reduce voltage
problems
Reduce carbon
emissions
Mostly focus on individual household
Increased self-consumption through heat pumps
Reduced PV-feedin peaks at noon
Seasonal discrepancy between PV production and heat demand
limits self-consumption
Variable speed HP increase self-consumption for small PV sizes
Limited impact of increased storage size on self-consumption
Efficiency losses reported
[26;37;38;58;71;
74;75;7779;8389;
110;130;131;145;
147;150;151]
Residual Load Smooth load curve
Reduce peaks
Significant reduction of peaks and valley filling achieved
Reduction of power gradients
Reduced cost of el. generation,
[34;76;82;89;149?
]
Price Variable Elec-
tricity Prices
Minimize annual
cost of energy
Most studies focus on decentralized control/scheduling algo-
rithms
Total electricity purchase cost reduced
Positive and negative impact on efficiency reported
Significant load shifting achieved through price signals
[23;3638;54;56;
57;62;64;66;6870;
77;7981;84;107;
109;119;122;123;
125;135;137;138;
141;148;152156]
10
4. Controls
Along with new applications, different approaches for the in-
tegration and control of heat pumps in a smart grid context have
emerged and are briefly introduced in the following. Many of
the control approaches have been enabled by the emergence
of small, affordable and sufficiently powerful computation and
communication technology, new communication protocols and
tailored algorithms over the recent years.
4.1. Tasks and targets
In the previous section it has been highlighted that the main
cases of applications considered for heat pumps in a smart grid
context are the provision of services to the electric grid, facil-
itating the integration of renewable electricity generation into
the power system and operation under time variable electricity
prices. The role heat pumps will play in the power system will
also influence the way heat pumps are operated and controlled.
The main control task of the heat pump, the supply of ther-
mal energy to meet the comfort requirements, will be extended
when integrating the heat pump into the power system. This
results in two tasks required from future heat pump controllers:
1. Planing and scheduling (mostly day-ahead) of the heat
pump operation ahead of time as a reaction to a forecast
or broadcasted signal (e.g. day-ahead prices)
2. Change of operation as a reaction to a real time signal
The control approach of the heat pump and storage is selected
depending on the application. For all applications controls
should avoid a violation of user comfort requirements, while
maximizing utility. The objectives are to achieve the thermal
comfort of the building occupants at:
a) Minimum cost of operation
b) Maximum efficiency of the system
c) Maximum self-consumption
d) Maximum benefits for the power system (as stated in the
previous section)
Often it is possible or required to target multiple objectives si-
multaneously. In the remaining Section the main concepts as
found in heat pump related work are briefly discussed. The fo-
cus is on providing an overview of the most important concepts
and findings with respect to the application of heat pumps in a
smart grid context. A comprehensive review on advanced con-
trol measures and techniques applied in buildings can be found
in reference [157], where the focus is on comfort criteria and
building supply with a high share of renewable energy.
4.2. Hierarchy and levels of integration
In reference [140] it is highlighted that energy markets’ reg-
ulatory requirements and the time scales as shown in Figure
6strongly influence the choice of controls and integration ap-
proach. For the control of heat pump systems in a smart grid
context three boundary levels and resulting control tasks exist:
1. Power system level: This includes integration and control
of individual buildings or entire heating networks, (renew-
able) electricity generation and consumption devices in the
context of electricity grids and markets.
2. Building level: This includes control of the heat pump,
thermal storages, heating distribution systems, indoor tem-
perature, on-site renewable energy sources as solar PV and
solar thermal
3. Heat pump unit level: This includes the control of the re-
frigerant cycle including fans, valves and compressor. A
good introduction to this topic is provided by the refer-
ences [45;158]
In a smart grid the different systems have to interact. This can
be implemented in an open loop way, where the high level con-
troller sends requests or set-points to the low level system, with-
out having state feedback. In a closed loop implementation
feedback is provided to from the lower to the higher control
level. The higher control level might receive information about
the outputs and states of the controlled system, which it uses
to adjust the control signals. Control hierarchy as assumed in
most heat pump related articles is mostly hierarchically orga-
nized (cascaded). An exception to this are agent or negotiation
based control approaches, where individual actions are coordi-
nated in a decentralized way using market places or other game
theoretic negotiation approaches (for further reading see refer-
ences [159162]).
Figure 8: Control hierarchy and levels of integration.
In a hierarchically organized control approach, as depicted
in Figure 8, a central instance sends signals like prices, grid
state or switching orders (direct load control) to the building
energy management system which coordinates the different de-
vices (or just the heat pump) in the building. A central question
is where operation decisions are made. In case of centrally con-
trolled virtual power plants, a high level control instance gen-
erates operation schedules for each device and submits those to
the field units in the form of switching orders, thus the degree
of freedom for the field units is limited. The challenge for the
high level is to determine control decisions that maximise over-
all utility, and are feasible and economically reasonable for the
lower systems. Contrarily, if prices are transmitted to the field
devices they have the freedom and challenge to autonomously
11
manage their operation. In this case the challenge for the high
level controller is to anticipate the reaction of the subsystems to
the price signal.
According to the autonomy level of the building energy man-
agement system, based on reference [159], three categories can
be defined with corresponding signals:
Passive systems (direct control): The main control deci-
sions are done at the higher control level. Direct set values
are transmitted from the power system (e.g. an aggregator)
to the field device, which tries to follow.
Passive intelligent systems (indirect control): A cost sig-
nal is transmitted from the higher control level and the
field device tries to optimise operation within the given
cost structure.
Active systems (agent based control): All entities are seen
as individual agents seeking to maximise an individual or
joint utility function. Control action of all entities is ne-
gotiated interactively, so that the common and individual
goals of operation are achieved[106;109;118;147;149]
In all cases the low level controls have a certain minimum au-
tonomy to guarantee that the heat pump unit is operated within
the allowed range and user thermal comfort is not sacrificed.
4.3. Classification of approaches
Control approaches used at the building level are closely
linked to the application and the top level control approach
taken. For passive intelligent systems the two major categories
(shown in Figure 9) are predictive control methods and non-
predictive methods . For non-predictive methods, the key dis-
tinction is how control action is derived from the current system
state. Predictive methods can be categorized by the predicted
values, the prediction methods and the way the scheduling task
is solved. Since no prediction is perfect, the treatment of uncer-
tainties can play an important role.
Figure 9: Applied control strategies for heat pumps in a smart grid context.
Only passive intelligent systems are considered.
4.3.1. Non-predictive Methods
Non-predictive methods are the way most heat pumps are
controlled today. Real-time or averaged sensor values of e.g.
temperature sensors, PV electricity generation, frequency or
voltage in the electric grid, and price information are used to
calculate a control decision for the heat pump at every time step.
Such methods are mainly used if predictions are not available
or do not offer any additional useful information. Sometimes
it is also possible that the costs of design and implementation
of predictive methods exceed the benefits of improved controls.
The calculation of the control signal is done using classical con-
trol theory, rule-based control (if-then) or predefined schedules
and programs.
One example of non-predictive rule-based controls is pro-
viding "fast" services to the power grid for stabilisation of volt-
age or frequency as shown in the references [40;72;73;94;
100]. In those studies the heat pump power is regulated up-
or downwards given a certain condition in the power grid. As
such critical conditions are not known beforehand and require
fast response, rule-based non-predictive methods are the appro-
priate solution.
A further example of rule-based controls is the case of PV
self-consumption [38;71;75;78;83;86;88;110;130;151].
Heat pump operation is started when PV production exceeds
a certain value or compressor speed is increased when elec-
tricity is fed into the grid. In reference [78] the limitations of
non predictive methods are visible. When PV self-consumption
maximising controls are applied the storage is charged to use
as much PV electricity as possible with the heat pump. The
future thermal demand or the availability of PV electricity on
the following day are not included in the calculation of heat
pump operation. This leads to high storage temperatures and
avoidable losses over a long period as more heat is stored than
actually needed for the next day. However as intended PV self-
consumption has been increased by 7% compared to a control
without appropriate expert rules.
A further example of non-predictive methods are time sched-
ules, as used frequently today when a static time of use tariff
structure is applied or grid congestions are to be avoided. In
such a case heat pump operation is blocked during high price or
high load hours.
Today non-predictive methods are applied in the majority of
building energy management systems. A strong point of non-
predictive rule-based controls is that the design of rules can be
relatively simple and the controls still show good performance
[37;75]. Further computation of the control actions does not
require a lot of resources and rules can be robust. Sometimes
rule-based controls are the only way to quickly response to crit-
ical conditions in the grid, such as voltage or frequency viola-
tions.
On the other hand, it is hardly possible with expert rules that
do not take prediction into account to achieve an optimal result.
Overheating of the storage, comfort violations and loss of effi-
ciency can be the result. Furthermore, when conditions such
as pricing structure, demand pattern or comfort requirement
change frequently, designing appropriate rules can be challeng-
12
ing. In order to improve the performance of controls, predic-
tions can be used.
4.3.2. Predictive Methods
As aforementioned, operating a heat pump in a smart grid
context implies some sort of external signal to be given. If this
information is provided ahead in time, heat pump operation can
be scheduled accordingly to serve the thermal demand. Given
the current state of the system, two main tasks arise:
1. Predicting the values of importance for operation (e.g.
prices, PV generation, thermal demand).
2. Finding an operation schedule that satisfies the demand at
minimum "cost" and respects the physical limitations of
the system and the comfort requirements of the occupants.
The task of prediction can be divided to predicting external
signals, that mostly influence cost of operation, and predicting
demand to which operation is subjected to. For external signals,
like prices, weather, PV and wind generation, forecasts can of-
ten be obtained at least a day-ahead from third party providers.
Day-ahead electricity prices may come directly from the spot
markets. Thus, the challenge for planning heat pump operation
is mostly in predicting the buildings energy demand for space
heating and domestic hot water.
Prediction methods differ depending on which information is
used in order to predict the demand. Internal methods use his-
toric measurements to learn or recognize patterns and predict
the future of a value from its past. Persistence methods (e.g.
yesterday-is-today), statistical methods such as auto regressive
and moving average methods, AR(I)MA, artificial neural net-
works and generalized mixture models or clustering methods
are examples for internal methods. The benefit of using internal
methods for prediction is that no external information, such as
weather forecasts, is needed for running a predictive controller,
which makes the system autonomous and saves costs.
External methods use additional data for prediction, such as
temperature or irradiation forecasts. Those can be used for
predicting thermal demand of the building or the prediction of
available PV electricity. For this purpose, regression methods,
AR(I)MA-X, neural networks or system identification based on
reduced physical models are frequently used. For more infor-
mation on forecasting, see references [163165] for statistical
methods, reference [166] for an overview and reference [167]
for artificial intelligence methods.
The type of decision algorithm is used to further categorize
the control methods. Given a forecast for demand and costs, an
operation schedule for the heat pump system has to be calcu-
lated and a control signal has to be applied to the system. This
is usually done in a receding horizon way. A control schedule
is planned for a given prediction time span (prediction horizon)
and only the first steps of the scheduled actions are applied to
the system (control horizon). After that, a new prediction is
made and a new schedule based on the current system state
and prediction is calculated. The task to be done is to find the
best feasible control trajectory over the whole prediction hori-
zon given the predictions and the current system state.
The methods used to solve this task are categorized by ref-
erence [79] into model based and model free methods. Model
based methods, referred to as model predictive controls (MPC),
use a representation of the physical system to be controlled, for
finding an optimal control trajectory. The type of model (black
box, grey box or white box i.e. physical) and the mathemati-
cal formulation determine the effort for modelling and the type
of the resulting optimization problem (optimal control prob-
lem). The type of the resulting optimization problem deter-
mines the class of solvers to be used. The parts of the building
energy system that are commonly included in the model are the
heat pump, storage (building thermal mass or water tanks) and
sometimes the heat distribution system. A heat pump system
shows non-linear, hybrid (i.e. a mix of contentious and discrete)
system characteristics, which are treated differently to be used
in optimisation. In classic model predictive controls, linear or
convex quadratic problems are favoured in terms of computa-
tional effort over non-linear non-convex formulations. Linear
MPC is used in the references [56;64], convexified approaches
are presented in the references [57;69;77;136]. In those
cases, computational performance is prioritised over model de-
tail. In non-linear approaches the non-linearities of the system
are represented more accurately as presented in the references
[155;168] where shooting methods and interior point optimi-
sation are used for the solution and in reference [37] where dy-
namic programming is used for the solution of the optimal con-
trol problem. In the references [26;37;56;57;62;64;68;69;
77;79;80;84;122;123;135;137;148;153;155;156] model
predictive control has been used successfully with heat pumps.
Especially cases with variable electricity prices show cost sav-
ings up to 35%. However, the assumptions often include perfect
predictions and no mismatch between the optimization model
and the controlled entity. As a benchmark, mostly simple rule-
based controllers are used.
A benefit of using MPC for building controls is the ability
of MPC to include forecasts (like price and weather) in the cal-
culation of the controls. The possibility to handle constraints
of the system (like maximum HP capacity), the ability to track
multiple objectives (such as comfort and cost) and the flexibil-
ity towards changing boundary conditions (such as prices) make
MPC an interesting control option [169]. Especially when sys-
tem inertia is high (as it is the case when controlling a thermal
storage or building’s indoor temperature), planning the opera-
tion ahead using MPC offers benefits over classical controls. It
is shown that MPC improves efficiency, comfort and especially
economic performance significantly compared to the used rule-
based controllers.
The downside of model predictive controls is clearly that
the problem formulation can be challenging and that identi-
fication procedures might be needed to adjust the model pa-
rameters [69]. Furthermore, the computational effort for solv-
ing the resulting optimization problem is higher than in rule
based approaches, leading to higher requirements for the con-
troller hardware. Moreover, the need of forecasts and optimiza-
tion tools on the controller increases the complexity of the task
and leads to increased technical requirements for field devices.
Building a model representation of the controlled system might
13
be non-trivial, time consuming, and costly.
In order to account for uncertainty due to imperfect predic-
tions stochastic control methods are used [139;152;156;170].
Those methods are based on the insight that forecasts will never
be perfect, thus uncertainty is already included in finding the
best control trajectory. This is done in the references [152;156]
by considering different scenarios, adding them to the opti-
mization problem and solving a larger optimization problem.
It is shown that the scenario based method mostly outperforms
classical MPC approaches, but at the cost of increased com-
plexity in modelling, scenario generation and computation. In
[139;170] a combination of stochastic programming and opti-
mal controls is used to account for uncertainty in weather pre-
dictions. A safety margin is added to the constraints of the op-
timal control problem so that comfort requirements are fulfilled
with a certain probability, given the uncertainty of the forecasts.
It is shown in the references [139;170] that stochastic MPC out-
perform classical MPC approaches and rule based controls for
the given cases.
Model-free predictive methods avoid the complexity of mod-
elling and solving an optimal control problem. This is done
using heuristics or rules to derive a control trajectory with re-
spect to forecasts of price and demand. In such predictive rule-
based decision strategies, decisions are based on prior engineer-
ing knowledge (expert systems). This has been successfully
applied in the references [37;41;60;103;125;138;141]. If
designed carefully such rules might be a good compromise be-
tween MPC and non-predictive methods, being computation-
ally inexpensive but still using the available information and
forecasts. However, the solution might not be optimal and rules
might not be flexible enough to cover all possible scenarios.
Further artificial intelligence techniques like reinforcement
learning where the control action is learned and improved from
previous tries are discussed in reference [63] for building tem-
perature control but have not been demonstrated with heat
pumps so far. Table 2lists and compares the different control
approaches used to control buildings and heat pump systems for
the case of indirect control signals (e.g. price).
14
Table 2: Overview of frequently used control approaches for heat pump systems.
Type Pros Cons References
Rule based Mostly simple to implement and design
Computationally cheap
No external information needed
Mostly inflexible rules adjusted to the
use-case
A-priori expert knowledge needed
[33;38;40;53;54;
7173;75;78;82;
82;83;86;88;89;
100;110;115;117;
130;132;133;151]
Model-free
predictive
control
Uses information from predictions
Compromise between complexity and perfor-
mance
Computationally cheap
Better performance compared to simple rule-
based controls
Mostly static adjusted to the use case
A-priori expert knowledge needed
[41;60;74;103;
105;113;116;125;
138;141]
Model based
predictive
control
Uses information from predictions
Optimal or close to optimal solutions possible
Flexible towards changes in boundary condi-
tions (like pricing structure)
Constraints handling possible
Mostly superior performance compared to rule-
based controls
Complex in design
Modelling effort
Computational requirements
Prediction errors
Modelling errors
Robustness
[23;26;34;
36;37;39;55
59;61;62;64;67
70;76;77;79
81;84;85;92;101;
104;105;107;114;
119;122;123;128;
135137;142
146;148;152;153;
155;156;171?
174]
Stochastic
Predictive
Controls
Treats errors in prediction
mostly better results than non-stochastic MPC
Robustness
Complex in design
Can be computationally expensive
[36;144;152;156;
173]
5. Conclusion and recommended future research
In this study the use of heat pumps in a smart grid context was
analysed and discussed. The term smart gird can have different
meanings depending on the authors’ perspectives and focus of
the study. The majority of applications with residential heat
pumps in a smart grid context are motivated by the provision
of services to the electric grid, a maximization of the use of re-
newable electricity or the operation under variable prices. It has
been highlighted that those fields of applications are inherently
linked together.
5.1. Conclusion
Heat pumps are considered to be a major technology to pro-
vide flexibility to the power system meanwhile providing ef-
ficient heating and cooling solutions to residential buildings.
The technology is supported by increasing efficiency, the de-
ployment of computing and communication technology and in-
creased renewable electricity generation. In order to success-
fully integrate heat pumps in a smart grid, it is critical to have
a holistic view on the energy systems affected. Analysing the
smart grid barely from the electric perspective will lead to miss-
ing how heat pump system efficiency and indoor comfort will
be affected by potential changes in heat pump control. Op-
positely, if the focus is only on heat pump efficiency without
considering the characteristics and expectations needed in the
future electric system, this will lead to considerable costs and
waste of resources in the power system. Hence, a holistic per-
spective is required to analyse, design and operate the future
energy system.
The investigated studies show that heat pumps can be used to
ease the transition towards a renewable interconnected energy
system. It is highlighted that altered heat pump operation might
come at the cost of efficiency. High storage temperatures, oper-
ation far from optimum compressor speed or frequent switching
of the heat pump units.
The potential flexibility of heat pump systems, which should
be considered already in the planning phase, is mainly depen-
dent on the building physics and the resulting thermal demand
profile, heat pump and storage type and size with respect to the
demand, and the control strategy applied.
Predictive and non-predictive control approaches have been
presented for heat pumps in a smart grid context. The choice
of controls is strongly connected to the application and inte-
gration approach. On the level of individual buildings, model
predictive control approaches have been found to outperform
non predictive approaches in terms of achieving control goals
15
such as maximising thermal comfort and minimising operation
costs. However, those come at the cost of additional complex-
ity, needing expertise in design and computational resources.
Predictive rule-based approaches can be a promising compro-
mise between complexity and effectiveness of controls.
5.2. Recommendation for future research
The research over the recent years has contributed with sim-
ulations, prototypes and field tests towards integrating heat
pumps in smart grids. The concept of a smart grid integration
of heat pumps can thus be considered as proven. However to
enable a large scale integration of heat pumps into the power
system, further research should focus on application topics in
three levels.
The first level is the integration and management of heat
pumps in the power system. Here the following points should
be addressed:
1. The use of a large number of heat pumps in a pool: Here
the development of scalable control concepts and knowl-
edge about the flexibility of a heat pump pool in contrast
to single entities should be in the focus.
2. Development of business cases to build the foundation for
integrating heat pumps in smart grids.
3. A techno-economic analysis of heat pumps when oper-
ating on different electricity markets such as day-ahead,
intra-day and the reserve markets.
The second level is the integration and management of heat
pumps in buildings energy systems. Here the following points
should be addressed:
1. The design of optimal flexible systems for a given ap-
plication, should be addressed in a more clear and struc-
tured way. This includes recommendations for sizing heat
pumps, storage, the layout of building energy systems and
the choice of control approach.
2. The impact of different control approaches and smart grid
applications on system cost and efficiency needs to be in-
vestigated further. A focus should be on the practical rele-
vance and feasibility of many suggested solutions.
3. Model predictive control is used in many studies for oper-
ating heat pumps in a smart grid context. Although bene-
fits are well known implementation rate in the field is low.
A comprehensive study about strength, weakness and op-
portunities especially when compared to expert systems
should be conducted to give advice to system engineers
and researchers to improve MPC for practical use.
The third level is the heat pump unit itself. Here the focus
should be on the impact of smart grid use on the heat pump
units and address the following points:
1. The use of variable speed compressors enables a continu-
ous regulation of power consumption. This option should
be further investigated with respect to possible benefits for
smart grid applications.
2. Many studies consider a heat pump as a black box which
can be easily used for smart grid purposes. However, this
can strongly influence the performance of heat pump cycle
and system. Therefore the design of the whole heat pump
system should be investigated with respect to be optimally
adapted to the requirements from the electric system.
3. Minimum run and pause times and ramping rate con-
straints are examples for limitations to consider when inte-
grating heat pumps to a smart grid. Finding and improving
such smart grid bottlenecks in heat pump component and
circuit design can improve flexibility characteristics and
lifetime of a heat pump unit.
We conclude that heat pumps have the potential to be a cen-
tral part of an efficient, renewable and interconnected energy
system, but there is still some work to be done.
Acknowledgements
he research leading to these results was conducted in the
green heat pump project [175] and received funding from the
European Union Seventh Framework Programme (FP7/2007-
2013) under grant agreement N308816. We also thank the
Swedish research program "Effsys Expand" for partly financ-
ing the study within the project No. 40933-1.
We thank Friederike Rautenberg for her excellent work help-
ing to analyse the literature and Marc-André Triebel for editing.
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... However, to benefit from the potentials reported, flexibility estimation has to be provided at low technological and economic costs [7]. Finding an optimal integration solution with the lowest cost, potential flexibility should already be considered in planning phase of the HP system; for scalable solutions and broader knowledge about flexibility use, HP pools should be considered [7,8]. Different flexibility estimation and assessment methods are discussed in literature [4,8,9,10,11,12], but rarely applied to settings of real-world implementation, struggling with reduced sensor information [6]. ...
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... They are also investing in expensive smart metering infrastructures, which generates data streams on the electricity consumption of households, including the consumption of heat pump installations (EC, 2014). Smart metering can enable heat pumps to increase grid stability and facilitate the integration of renewable energy sources (Fischer and Madani, 2017;Hillberg et al., 2019), for example, using dynamic electricity tariffs (Gottwalt et al., 2011). However, utilities are still looking for new use cases for leveraging their smart meter infrastructure to get a return on their significant investments. ...
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... A number of studies [7][8][9] proposed that large-scale electric heat pumps can provide an integration between the heating and power sectors, improving their operational flexibility and increasing the use of renewable energy sources. In this context, it is expected that large-scale heat pumps will have a significant role in future smart energy systems [10,11]. ...
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... Heat pumps are also compatible with smart electricity grids [62]. This has convinced policy makers to consider a particular role for heat pumps in roadmaps to zero emission heating and cooling systems [11]. ...
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find the free access text in the link below: http://hdl.handle.net/1854/LU-8762630
... Replacing gas boilers with heat pumps can also increase renewable utilization, particularly in a cold climate where heat is the largest energy demand in buildings [105]. Electrifying all buildings will cause a considerable increase in electricity demand and can potentially lead to higher peak loads [106]; however, the flexibility offered by hot water tanks and building mass, combined with smart controls, make load shifting and peak reductions increasingly possible [107][108][109][110]. ...
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Thesis
Energy transition, i.e.the passage from a fossil fuel-based energetic mix to a renewable-basedenergetic mix, is commonly accepted as a necessity imposed by the fight against climate change. Unfor-tunately, this transition is not smooth. Regarding only technical issues, energy sources identified as thebasis of the futur energetic mix, sun and wind, are intermittent and non-controllable. Moreover, the mostproductive areas are not necessarily those where consumption occurs. The main solutions considered toresolve this spatial and temporal gap are respectively electric grid renovation and the diffusion of storage.These solutions are expensive.The work realized in this thesis explores on another approach, complementary, Demand-Side Management(DSM). This one is a paradigm in which the grid manager can adapt partially the consumption to pro-duction constraints. This principle is not new, as time of use tariffs are fully part of DSM. More precisely,our research started with the development of a software designed to manage in real-time multi-energygrids and relying on Direct-Load-Control (DLC), i.e.by monitoring directly some domestic appliances.The main objective is to use the flexibility offered by DLC to improve the use of local renewable energyand thus to reduce the need of storage or energy exchanges with the grid. This method raises numerousquestions outside of the physical field. Here, we integrate economical aspects of the problem, not onlyvia a classical techno-economical approach, but also via a collaboration with the economy laboratory ofUniversité de Pau et des Pays de l’Adour. Through our interactions, we based our work on a contract-based approach, where different contracts are proposed to consumers (with or without DLC) and wedesigned a set of adapted rules. We are especially concerned with the following questions : Which returnfor consumers ? Which economical model for the grid manager ? Our approach is based on three mainobjects : devices, contracts and strategies. A software has been developed with this approach and addi-tion of new elements for each object (add a new production technology, for example). Then, we realizedsimulations exploring the relations between DLC popularity, energy demand (which quantity ? consumedwhen ?), kind of energy networks (electricity, heat, gas ?), available technologies for production, conver-sion or storage (which ones ? With which capacity ?) and applied strategy for grid management (Whichobjective ?). If results obtained thanks to these simulations do not allow to draw definitive conclusions,several observations can be made. First, using DLC improves renewable energy usage and reduce theneed to call the grid, either for buying or selling energy. Second, it seems that thresholds effects exist.Last, if DSM reduces effectively consumers’ bills, curtailment rates observed with our current strategiesand contracts are very (too much ?) high.
Article
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Heat pump (HP) units coupled to thermal storage offer flexibility in operation and hence the possibility to shift electric load. This can be used to increase PV self-consumption or optimise operation under variable electricity prices. A key question is if new sizing procedures for heat pumps, electric boilers and thermal storages are needed when heat pumps operate in a more dynamic environment, or if sizing is still determined by the thermal demand and thus sizing procedures are already well known. This is answered using structural optimisation based on mixed integer linear programming. The optimal system size of a HP, an electric back-up heater and thermal storage are calculated for 37 scenarios to investigate the impact of on-site PV, variable electricity price, space heat demand and domestic hot water demand. The results are compared to today's established sizing procedures for Germany. Results show that the thermal load profile has the strongest influence on system sizing. In most of the scenarios investigated, the established sizing procedures are sufficient. Only large PV sizes, or highly fluctuating electricity prices, create a need for lager storage. However, allowing the storage to be overheated by 10 K, the need for a larger storage only occurs in the extreme scenarios.
Conference Paper
In this paper, we address the challenge of adaptively controlling a home heating system in order to minimise cost and carbon emissions within a smart grid. Our home energy management agent learns the thermal properties of the home, and uses Gaussian processes to predict the environmental parameters over the next 24 hours, allowing it to provide real time feedback to householders concerning the cost and carbon emissions of their heating preferences. Furthermore, we show how it can then use a mixed-integer quadratic program, or a computationally efficient greedy heuristic, to adapt to real-time cost and carbon intensity signals, adjusting the timing of heater use in order to satisfy preferences for comfort whilst minimising cost and carbon emissions. We evaluate our approach using weather and electricity grid data from January 2010 for the UK, and show our approach can predict the total cost and carbon emissions over a day to within 9%, and show that over the month it reduces cost and carbon emissions by 15%, and 9%, respectively, compared to using a conventional thermostat.
Conference Paper
Energy storages connected to the power grid will be of great importance in the near future. A pilot project has investigated more than 100 single family houses with heat pumps all connected to the internet. The houses have large heat capacities and it is possible to move energy consumption to suitable time slots and this makes it possible to avoid some grid storage capacity. The energy is bought based on prediction of energy prices, weather forecast and an aggregated house model on the NORD POOL day-ahead market. The bought energy is then distributed to the houses using a model free sorting (scheduling) algorithm. The properties of this scheduling are investigated in the paper especially the flexibility and ability to trade on the intra-day regulating market is in focus.
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Self-consumption of locally generated electricity from domestic PV Systems is limited to 25-35% in Central Europe due to lack of synchronization between generation and load [1]. Adding heat-pumps and thermal storage provide efficient means to shift loads in time and thereby increase levels of self-consumption and local autonomy. The paper analyses these effects for different building standards. The listed self-consumption and autonomy levels are a result of simulations that involve analysis of the time-series of solar radiation, outside temperature, hot water demand and electricity demand for a whole year. Battery storage is added to complete the picture. Finally storage-control algorithms are proposed and simulated, which allow reduction of the peak injection into the grid to as low as 70% of the nominal peak-output power of the installed domestic PV system.
Conference Paper
Photovoltaic (PV) system operators in Germany with an installed PV peak power of less than 30 kWp are obliged to either let their PV infeed being reduced externally or to generally limit their maximum PV infeed to a threshold of 70% of the installed PV peak power. This curtailment reduces the PV-induced grid load significantly but also leads to a notable loss of renewable energy. This paper presents the contribution of heat pumps to reduce the energy loss by increasing the self-consumption of smart homes. This is achieved by applying an appropriate control of the heat pumps. Additionally, the corresponding impact on a low-voltage distribution grid is investigated. Simulation results reveal that it is possible to notably reduce the loss of energy even throughout the summer season. Furthermore, load flow calculations show that it is also possible to stabilize the low-voltage distribution grid at the same time.
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Buildings are dynamical systems with several control challenges: large storage capacities, switching aggregates, technical and thermal constraints, and internal and external disturbances (occupancy, ambient temperature, solar radiation). Conflicting optimization goals naturally arise in buildings, where the maximization of user comfort versus the minimization of energy consumption poses the main trade-off to be balanced. Model predictive control (MPC) is the ideal control strategy to deal with such problems. Especially the knowledge and use of future disturbances in the optimization makes MPC such a powerful and valuable control tool in the area of building automation. MPC compromises a class of control algorithms that utilizes an online process model to optimize the future response of a plant. The main benefits of MPC are the explicit consideration of building dynamics, available predictions of future disturbances, constraints, and conflicting optimization goals to provide the optimal control input. MPC technology has been applied to process control for several decades and it is an upcoming field in building automation. This is a consequence of the large potential for saving energy in buildings and also allows to maximize the use of renewable energy sources. Furthermore, the added flexibility enables to integrate such buildings in future smart grids. In this work ten questions concerning model predictive control for energy efficient buildings are posed and answered in detail.