Renewable and Sustainable Energy Reviews 189 (2024) 114017
Contents lists available at ScienceDirect
Renewable and Sustainable Energy Reviews
journal homepage: www.elsevier.com/locate/rser
Key district heating technologies for building energy flexibility: A review
Yurun Guo a, Shugang Wang a, Jihong Wang a, *, Tengfei Zhang a, Zhenjun Ma b, Shuang Jiang c, **
a
Faculty of Infrastructure Engineering, Dalian University of Technology, 2 Linggong Road, Dalian, Liaoning, 116024, China
Sustainable Buildings Research Centre, University of Wollongong, 2522, Australia
c
College of Civil Engineering, Dalian Minzu University, Dalian, Liaoning, 116600, China
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
District heating systems
Energy flexibility
Prosumers
Low-temperature district heating networks
Thermal energy storage
Optimal control
In the background of the continued integration of renewable energy sources (RES) and the increasing flexibility
on the demand side, the diversity and complexity of new technologies for heating present increased challenges
for design and operation of district heating systems (DHS). This work first reviews the progress of the new
generation of DHS, followed by providing an overview of investigations on building energy flexibility in the field
of heating, with a focus on the characterization and quantification of energy flexibility, the realization of thermal
flexibility, and the use of building thermal mass in demand side management (DSM). Different technologies were
categorized and summarized according to the composition of the new generation of DHS. Control strategies such
as model predictive control were also examined. In particular, the concept of building thermal battery is used to
analyze buildings or prosumers thermal energy flexibility. Finally, new elements of DHS development and po­
tential challenges were discussed.
1. Introduction
In light of increased penetration of renewable energy sources (RES)
and the supply and demand management of the grids, the energy sys­
tems offer more flexible, efficient, and economical energy solutions by
transforming a single energy sector into coherent, coordinated crosssector systems [1]. Building energy systems, as a source of energy
flexibility [2], has been was found to have undergone two changes: the
trend of decentralization of energy systems and demand side manage­
ment (DSM). For the former, decentralized prosumers can not only
produce, consume, share, and import local low-carbon energy in a
certain area but also reduce energy costs. For the latter one, prosumers
change how and when they use energy through DSM [3]. In building
energy systems, district heating systems (DHS) contribute to the inte­
gration of intermittent and variable RES. From 2015 to 2020, the share
of RES in the global DHS sector increased by 1 %, and it is expected to
increase to 14 % by 2025 [4]. However, conventional DHS still requires
changes in terms of matching supply and demand and incorporating a
wider range of heat sources to accommodate building energy de­
velopments. Coupled heat and power technologies offer a variety of
ways to increase energy flexibility and enable DHS to operate in concert
with electrical systems with different spatial and temporal
characteristics. Grid managers are able to regulate various building
loads, such as lighting, heating, ventilation, and air conditioning
(HVAC) loads, using demand response (DR) [5].
The fourth generation district heating (4GDH) system [6] and fifth
generation district heating and cooling (5GDHC) system [7] represent
the latest developments in DHS, both of which focused on the
demand-side energy use potential and provided the necessary flexibility
to the entire energy system. 4GDH uses centralized heat supply from the
central heat source plant to the end heat stations or consumers. How­
ever, the temperature of 4GDH is reduced to 30 ◦ C–60 ◦ C, which not only
reduces the heat loss from the pipe network but also makes it easier to
integrate low-temperature heat sources. These low-temperature heat
sources include industrial waste heat [8], waste heat from sewage [9],
data centers and supermarkets [10], biomass sources [11], shallow
geothermal [12], solar [13], and other renewable heat sources [4]. The
integration of RES will allow the 4GDH system to extend its range of
services. 4GDH can be an important component of low-energy buildings
[14], such as energy-efficient buildings [15,16] and near-zero energy
buildings, and can meet different energy-efficient building renovation
schemes. 4GDH also helps achieve smart energy systems. Lund et al. [6]
showed that 4GDH can operate with higher efficiency and lower pro­
duction costs, and the cost of converting existing and new buildings to
4GDH is insignificant. However, there are inevitably some shortcomings
* Corresponding author.
** Corresponding author.
E-mail addresses: wangjihong@dlut.edu.cn (J. Wang), shjiang@dlnu.edu.cn (S. Jiang).
https://doi.org/10.1016/j.rser.2023.114017
Received 28 March 2023; Received in revised form 25 September 2023; Accepted 23 October 2023
Available online 11 November 2023
1364-0321/© 2023 Elsevier Ltd. All rights reserved.
Y. Guo et al.
Renewable and Sustainable Energy Reviews 189 (2024) 114017
Nomenclature
Symbols
F
P
t
γ
PCM
PV
RL
SH
TES
STES
ULTDH
Energy flexibility
amount of power input or output
Time
Heat to power ratio
Abbreviations
2DSM
Dual demand-side management
4GDH
Fourth generation district heating
5GDHC Fifth generation district heating and cooling
BTB
Building thermal battery
CHP
Combined heat and power
DHS
District heating system
DHW
Domestic hot water
DR
Demand response
DSM
Demand side management
DT
Digital twin
HP
Heat pump
HVAC
Heating, ventilation, and air conditioning
LTDH
Low-temperature district heating
MAS
Multi-agent systems
MPC
Model predictive control
Phase change material
Photovoltaic
Reinforcement learning
Space heating
Thermal energy storage
Seasonal thermal energy storage
Ultra low-temperature district heating
Superscripts
DHS
DHS
down
Downward
sys
System
up
Upward
Subscripts
actual
Actual
battery
Battery
desired
Desired
flexis
Flexibility
h2p
Heat to power
heating Heating
PV
Photovoltaic
total
Total
water
Water tank
in 4GDH systems, including: (1) users with different load distributions
and temperature requirements within the system may not be fully
accommodated [17]; (2) the network’s extension is limited by the con­
centration of energy output and the lack of reciprocal heat recovery
between buildings [18]; (3) the heating temperature of the 4GDH may
not meet the domestic hot water (DHW) needs of some customers [19].
5GDHC systems consist of bidirectional pipe networks, prosumers,
and thermal storage units [20]. 5GDHC technology is suitable for
buildings or building clusters with simultaneous cooling and heating
demands. The 5GDHC system offers both heating and cooling. Its cooling
function cannot be mentioned since the topic of this investigation is
heating. The 5GDHC network consists of separate single cold and warm
pipelines [21], and the temperature of the network is close to the ground
temperature and can float freely. In the definition by Buffa, the 5GDHC
network uses water or brine below 45 ◦ C as a carrier and is equipped
with a prosumer substation connected by bidirectional networks [20].
Furthermore, the plant can supply heating or cooling to the network in
order to balance prosumers’ net annual heat demand [22]. Energy hubs
can also balance the production, conversion, and storage between
different energy carriers [23] to meet the required demand at the output
side [24]. Features such as lower network temperatures and more flex­
ible topologies allow the 5GDHC to make more direct use of industrial or
urban waste heat, as well as RES. The type of integration of these energy
sources can be varied according to the heat load and heating and cooling
needs of prosumers. In the 5GDHC systems in operation in Europe, the
energy types include natural cooling and heat sources such as
low-temperature water sources (seawater, lake, river, and groundwater)
[20], shallow geothermal [17], urban waste heat sources including
prosumers (e.g., data centers [10], refrigeration systems in supermar­
kets [25,26], municipal sewage systems [27], and geological energy
structures [28]. The 5GDHC system is still in the development phase. A
number of research projects on 5GDHC have been carried out in Ger­
many, the Netherlands, Switzerland, Italy and the UK [20,29–31]. The
5G smart energy scheme in central London, UK, for example, aimed to
integrate a range of local renewable and secondary energy sources [32].
In addition, a number of 5GDHC simulation studies have contributed to
the spread of such heating networks. The integration of RES such as
geothermal energy and photovoltaics (PV) by the 5GDHC [33], the
electro-thermal coupling potential of 5GDHC networks [34], and the
topology of 5GDHC networks [22,35] are some of the studies covered in
this field.
The 5GDHC system needs to improve in the following areas: (1)
effective control of the decentralized heat pumps [36]. In this way, the
5GDHC will maximize the integration of low-grade thermal energy and
meet the cooling and heating needs of the different users in the system;
(2) thermal energy storage (TES) technology on different time scales
[37]. Managing the combinations of cooling and heating requirements
among different prosumers can reduce the capacity of TES. However,
there are technical difficulties in achieving this; (3) the threat of bacteria
such as Legionella [38]. Some solutions include using heat pumps (HPs)
to raise the temperature, eliminating water storage tanks in the system,
connecting the substation with small diameter pipes [39], adding cop­
per, silver ions, or chlorine dioxide to the water [40], etc; (4) local
geothermal energy resources are assessed. The thermal and economic
benefits of 5GDHC systems depend on the thermal properties of local
energy sources [20], and the utilization of low-temperature energy
sources often varies from region to region.
The contributions of this study are: (1) linking the development of
the new generation of DHS to energy flexibility in buildings. From there,
the importance of the demand side of the heating system was empha­
sized; (2) the potential for the new generation of DHS technologies was
examined from the perspective of energy flexibility; and (3) the trends
and challenges of DHS technologies were analyzed. Specifically, the
importance of building thermal batteries (BTBs) for modeling and
quantifying building flexibility was highlighted. Integrating DHS with
building energy flexibility will enable new changes in heating systems
and their ancillary technologies and businesses. This work will thus
inspire and guide research on DHS, the development of policies and
business models, and the governance of the environment and society.
The rest of the review is organized as follows. Section 2 presents
research on the energy flexibility of buildings. Section 3 summarizes and
classifies the key technologies that enable building flexibility in DHS. In
Section 4, the development trends and outlooks in existing DHS and
related technologies are outlined and discussed.
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Y. Guo et al.
Renewable and Sustainable Energy Reviews 189 (2024) 114017
2. Review methodology
represented the supply side (such as the power system) and the demand
side (such as residential, commercial, and industrial users) and coordi­
nated the sale, purchase, transmission, and other services of energy
between them. The demand flexibility of different thermal users can thus
be aggregated and integrated into the power system [49].
As the sources and properties of flexibility are varied, it is necessary
to quantify building energy flexibility so that the flexibility that could be
achieved under a given operating strategy can be measured. Finck et al.
[50] divided the indicators used to quantify flexibility into three cate­
gories: energy and electricity, energy efficiency, and energy cost. This
classification was based on: (1) obtaining detailed information on de­
mand flexibility related to the grid; (2) assessing the demand flexibility
of new energy system designs and renovations; and (3) obtaining the
optimal control of demand flexibility and measuring the effective utili­
zation of HP and TES. Li et al. [41] also reviewed the indicators,
methods, and applications of building energy flexibility. The six per­
formance criteria covered by these flexibility metrics are energy, power,
cost, time, emissions, and thermal comfort. The quantitative indicators
of thermal flexibility are related to the technical and related economic
indicators for achieving flexibility. For example, the former often uses
the energy transferred per unit time or the charging and discharging
time of energy storage technology, while the latter includes the cost
saved by shifting energy consumption to a period of low energy prices.
Ref. [51,52] investigated the flexibility provided to buildings by DHS.
Ref. [53–55] investigated and quantified the energy flexibility of
building clusters. These studies involved and quantified the impact of
HVAC equipment, heat storage methods, smart controls, and occupant
habits on the flexibility of building energy systems, taking into account
the coupling of thermal and power systems.
A number of specific flexibility indicators have been developed for
quantifying building flexibility [41,46]. For DHS flexibility, this can be
assessed in terms of heat and power coupling units by using Eq. (1) and
Eq. (2) [56]:
This study mainly used methods such as literature research and
literature analysis. The objective was to assess the status and trends of
district heating technologies for energy flexibility in buildings. Newer
sources were used in this study to improve the reliability of information
and data. Literature research for this review was obtained from Scien­
ceDirect (last accessed in January 2023). These keywords were used in
the literature search: district heating, heating networks, 4GDH, 5GDHC,
substations, TES, and advanced control. A total of 207 references were
cited in the study, including research articles (70.5 %), review articles
(17.9 %), conference papers (7.7 %), book chapters (2.4 %), and reports
and databases (1.5 %). They can be categorized according to when they
were published: 2018–2023 (54.1 %), 2013–2017 (37.2 %), and 2012
before (8.7 %). These key words in the literature were the design and
application of 4GDH and 5GDHC, the quantification of building energy
flexibility, the implementation of energy flexibility in DHS systems, and
the optimal control of DHS and HVAC systems. This work analyzed how
energy flexibility was achieved from the perspective of energy flexibility
by categorising each component or control method of DHS. As shown in
Fig. 2, these components include the prosumers, the heating network,
and the TES module. These components can be seen as sources of
building energy flexibility. In addition, optimal control, as an important
way to achieve the flexibility of building energy on the demand side, is
also a heating technology category.
3. Building energy flexibility in heating systems
3.1. Energy flexibility in buildings
Energy flexibility, in general, refers to a system’s capacity to modify
the production or consumption of distributed energy [41]. This ability
reflects both the capacity of the generator side and the demand side to
smooth the RES of load changes and fluctuations and the capacity of the
demand side to shift the energy consumption from the planned state to
the real state. Building energy flexibility, according to the definition in
the literature [41–43], is the ability of a building to support the opera­
tion of the entire building energy system by adjusting the relationship
between its energy demand and generation through specific operation
strategies in accordance with local environmental conditions and user
requirements.
In building energy systems, "power flexibility" refers to the ability of
the grid to respond cost-effectively to expected and unexpected fluctu­
ations in load or generation [44]. Corresponding to this, "thermal flex­
ibility" of DHS refers to the system’s ability to adapt to different heat
demands [45]. Thermal flexibility can arise either from the thermal
inertia of the heat (or cold) carriers, heat storage devices, or buildings
connected to the DHS or from the government’s or the operator’s cost
requirement or policy. Luc et al. [46] pointed out that the quantification
of thermal flexibility is derived from the related concept of electric
flexibility, and the definitions of flexibility that describe energy systems’
supply or demand sides can also apply to district heating systems. From
the perspective of DHS, heating equipment can consume part of the
power, which is conducive to the consumption of excess RES power. In
contrast, short-term heating shutdown has little effect on thermal
comfort. Compared to the electrical grid, where imbalances between
supply and demand result in shutdowns, this is significantly different.
Demand-side flexibility is released by thermal flexibility, and if this
flexibility is coupled with and integrated into the power system, it will
largely guarantee energy flexibility in buildings. In the current technical
environment, the connection of the heating and electricity sectors can
increase the flexibility of building energy systems. CHP and
power-to-heat technologies (P2Hs), including HPs and electric boilers
[47], are two types of technologies that may link these two sectors.
Golmohamadi et al. [48] argued that heating and power coupling
enabled the emergence of an agent entity in the energy system, which
sys
Fflexis
(t) = Pactual (t) − Pdesired (t)
(1)
sys
DHS
Fflexis
= Fflexis
γ h2p
(2)
where Pactual and Pdesired are the amount of power input or output during
the flexibility period (W), and the amount of power generation or con­
sys
DHS
sumption during normal operation (W), respectively; Fflexis and Fflexis
are
the flexibility of a given system (W), and the flexibility of DHS system
(W); γ h2p is the heat to power ratio (%). In addition, to determine the
energy flexibility of the prosumers, Balázs et al. [55] defined the total
flexibility of the residential prosumer model they studied as the sum of
the instantaneous flexibility of all devices in the prosumer in both the
upward and downward directions. The formulae are as follows:
up
up
up
up
Ftotal
(t) = Fwater
(t) + Fheating
(t) + Fbattery
(t)
(3)
down
down
down
down
down
(t) = Fwater
(t) + Fheating
(t) + Fbattery
(t) + FPV
(t)
Ftotal
(4)
up
down
where Ftotal
and Ftotal
are the total flexibility in the up and down di­
up
down
rections (W), respectively; Fwater
and Fwater
are the upward and down­
up
ward flexibility offered by the water tank (W), respectively; Fheating
and
down
Fheating
are the upward and downward flexibility offered by the heater
up
down
(W), respectively; Fbattery
and Fbattery
are the upward and downward
down
flexibility offered by the battery, respectively; and FPV
is the down­
ward flexibility offered by the photovoltaic (W).
However, Luc et al. [46] argued that differences in pricing strategies
for heat and electricity, as well as differences in flexibility technologies,
make a formal, unified quantitative definition of energy flexibility un­
available. Additionally, the way the thermal system and power system
cooperate may increase the energy system’s overall flexibility. Thermal
flexibility must be considered while evaluating the physical features of
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Y. Guo et al.
Renewable and Sustainable Energy Reviews 189 (2024) 114017
the heat demand side and TES, such as the climate, occupant behavior,
and heat storage loss.
DSM.
The effect of a single building on the heat and electricity grids is
limited. Dual demand-side management (2DSM) matches fluctuating
renewable electricity with the thermal demand of buildings at the urban
level through dynamic DSM control and interaction between thermal
and electrical systems [71]. 2DSM also monitors the distribution
network to prevent the local renewable generation or DSM activity from
posing pressure on the grid. The 2DSM market concept can leverage
existing experience to expand electricity market operations to other
energy markets, such as local geothermal energy. It enables participants
to interact in a peer-to-peer manner, breaking the direct link between
utility needs and customer responses. Mueller et al. [72] discussed ways
to match the flexibility requirements of the power grid with the flexi­
bility provided by local building energy systems. A simulation platform
covering power grid architecture and control strategy was proposed
based on multi-agent systems (MAS). Here, MAS can disassemble
complicated issues into more manageable ones at the architectural level
[73]. In addition, when analyzing the feasibility and flexibility potential
of coupling the residential heating network with the grid, Wolisz et al.
[74] noted that the realization of dynamic electricity prices, the
improvement of smart metering and control methods, as well as the
introduction of corresponding tax and incentive policies, were very
important to mobilize the enthusiasm of all stakeholders and achieve
2DSM.
3.2. Building demand response and demand side management
To achieve flexibility in energy systems, the traditional productionresponse model needs to be transformed into a future demandresponse model [43]. In the building power system, the end users can
flexibly adjust the power demand according to the power supply or price
signal of the grid, so that energy consumption and power generation can
be coordinated. Thermal DR can also use incentive-based [57] or
price-based [2] methods similar to power DR to change customers’
electricity consumption patterns. The former provides financial in­
centives for end users to adjust their heating demand at critical times
[58]. The purpose of the latter is to optimize heat consumption. In heat
and power coupling, the thermal system provides the potential for direct
or indirect flexibility for the power system. DHS can also exploit the
indirect flexibility of power systems by using heat storage devices [49].
Fig. 1 demonstrates the relationship between DR coordination and en­
ergy flexibility in energy networks. Due to subject matter and space
constraints, the discussion of gas system flexibility is not covered in this
work.
DSM is a long-term adjustment of load by changing the usage
behavior on the demand side. DSM enables anticipated changes in
power load patterns by energy efficiency, time of use, spinning reserve,
and DR [59]. With the maturity of modern control and regulation
technology, DSM has gradually become an effective and common
method of heating supply [60]. An appropriate DSM strategy is required
if the DHS is to operate without difficulty due to an increase in peak
thermal demand [61,62]. A typical example of the use of DSM in heating
systems is the building’s thermal mass storage during a certain over­
heating period, which allows the peak heating load to be shifted to an
off-peak period [63]. Building thermal mass can be building compo­
nents, such as walls, floors, and ventilation ducts, or internal objects,
such as furniture. Without affecting the comfort of occupants [64], the
indoor temperature can be deviated from the set point by means of
periodic overheating or undercooling, allowing thermal energy to be
stored or released in the thermal mass of the building and changing the
building’s heating demand for the following hours. This allows power
and heating loads to be shifted from peak demand to off-peak hours
while also reducing the maximum flow in the network. Table 1 sum­
marizes the relevant literature on the use of building thermal mass for
4. Key technologies of DHS
4.1. Prosumers and substations
Prosumers are a type of user who consumes and produces energy in a
specific region. In a power grid, prosumers take part in the generation
and use of electricity and profit from the sale of any excess power to the
grid [75]. Through specific energy management techniques, prosumers
with various demands can achieve the objective of energy sharing [76].
Prosumers can not only regulate and optimize the balance of demand
and capacity [77] but they can also coordinate the timing of multiple
energy uses, lowering the cost of energy use and easing the burden on
the network. If substations are added to the prosumers, the thermal
network will be able to better integrate heating, cooling, power grid,
heat storage, and other technologies and provide buildings with heat
energy at different temperatures and time scales [18]. Substations in­
tegrated with HPs, chillers, circulation pumps, or heat exchangers can
Fig. 1. Schematic diagram of DR coordination, flexibility source and integration for energy networks.
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Renewable and Sustainable Energy Reviews 189 (2024) 114017
Fig. 2. Categorization of DHS key technologies.
provide an ideal place for heat and power coupling. The HPs in the
substations contribute to the building’s energy flexibility: the HP can not
only operate under variable electricity prices [43] and maintain the
stable operation of the grid and heating network [36], but also can be a
kind of management interface between the heating system and the
electrical system or other external environment. Also, HPs combined
with thermal storage devices can integrate RES [78].
During the development of the DHS, several projects related to
thermal stations were carried out. Table 2 summarizes the relevant
research in the field of substations attached to DHS or prosumers. Of
these, the substation in 4GDH strives to link the low-temperature district
heating (LTDH) system and heat suppliers to the existing DHS. In DHS,
low return temperature can favor heating production efficiency and the
integration of low-temperature heat sources [79]. However, the high
temperature of 4GDH limits the type of RES integrated into the system.
4GDH cannot use a single pipe to provide both heating and cooling
services. In addition, the high-temperature medium generates high
thermal pressure and energy losses in the hydraulic isolation and hot
water feed-in of the substation. The substation in the 5GDHC can be
operated in either consumption mode (extracting heat from the
network) or production mode (supplying heat to the network),
depending on the demand on the user side. This will facilitate the
integration of more RES. Fig. 3 shows the substation in 5GDHC proposed
by Ref. [35]. It is connected to a hot and cold subnetwork. It should be
noted that the operation of a single or a few prosumers substations af­
fects the operation of others and the entire network. This indicates that
the substation in 5GDHC should be designed and operated not only to
satisfy the demand of the prosumers but also to take into account the
thermal-hydraulic coupling between the prosumers.
In addition, new technologies, including virtual sensors, data-driven
and deep learning, have been observed to be utilized in areas such as
optimal control and energy conservation management of substations.
However, these technologies need to be further practiced. The test rig
could be an effective way to validate them. As stated by Ref. [105], the
prosumer-driven, more flexible, and compatible bidirectional networks
pose a number of challenges, which include the design and control of
substations and networks as well as the improvement of the heating
market. The advances and challenges summarized in this chapter can
serve as a basis for subsequent innovative research on prosumers and
substations.
system, also known as an ultra-low temperature district heating
(ULTDH) system, with a heating temperature between 35 ◦ C and 45 ◦ C
[106]. The LTDH can be divided and constructed in the existing network
through the substation connection. ULTDH is suitable for lower tem­
peratures, where the network can be more easily integrated into the RES
or be used. Fig. 4 illustrates such a network. It consists of heating and
cooling pipes, circulation pumps, and valves [35]. A warm temperature
level pipeline and a cold temperature level pipeline, respectively, form
hot and cold subnetworks. The topology of a ULTDH includes radial
(tree), meshed, and ring [107]. The network configuration includes
single pipe, double pipe, and multi-pipe [17]. Table 3 summarizes the
research on LTDH and ULTDH.
The evaluation of the LTDH and ULTDH systems’ economic feasi­
bility is another of the development topics in network research. The
boundary conditions of the heating area and the available heat sources
have an impact on the economics of LTDH and ULTDH systems. Re­
searchers believed that the LTDH system is more efficient because it can
be directly used for heating and DHW. However, ULTDH improves the
energy utilization efficiency of waste heat and RES [88,119]. [120]
compared the economics of the two kinds of networks in Denmark and
the UK and showed that the competitive advantage of the LTDH system
increases as the heating demand decreases, and the fixed cost share of
the ULTDH system was higher. The three main factors contributing to
this result were: the cost of electricity; the penetration rate of district
heating; and the cost of single HPs. Lund compared the feasibility of
LTDH and ULTDH at the energy system level [121]. LTDH was found to
have the lowest socio-economic costs. But under certain circumstances,
ULTDH with a substation and a pressurized HP may be feasible. For the
ULTDH, Meesenburg believed that the ULTDH was feasible only when
the linear heat demand density was high, the cost of decentralized
heating units was low, or the investment cost of district heating units
was significantly lower than that of the LTDH [122]. Best et al. also
found that the higher cost of decentralized heat pumps for the prepa­
ration of DHW was offset by savings in heat distribution costs and
central heating costs [123]. This is because the better COP of the central
HP in the ULTDH system.
In the summary of the study, it was observed that the lowtemperature networks increase the utilization of RES, reduce heat los­
ses, and improve the operation of the existing DHS. However, it was also
noted the higher volume flow rate of hot water delivery from the
network and the corresponding increase in investment in various
equipment. In this case, due to the much lower temperature rating of the
ULTDH, the thermal demand of the end-users is more important to be
met by utilizing equipment such as HPs, especially for DHW [124].
However, if the water supply temperature is too high, this practice by
means of electricity consumption may not save the benefits from heat
4.2. Low-temperature network
In the new generation of DHS, 4GDH is referred to as a LTDH system
because the water supply temperature ranges from 55 to 65 ◦ C. Energyefficient buildings with low heat density can be heated using a 5GDHC
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Renewable and Sustainable Energy Reviews 189 (2024) 114017
4.3. Time-based thermal energy storage
Table 1
Summary of literature on the use of building thermal mass for DSM.
Articles
DSM or DR Methodology
Insights
[65]
Raising temperature set-point
(heat storage) and lowering
temperature set-point (heat
conservation)
[66]
Several building energy models
were created. Different
scenarios were set, including no
preheating and preheating
strategies.
[67]
Setting the indoor air
temperature set point at 20 ◦ C,
DR is achieved by increasing the
indoor air temperature to 1 ◦ C
and 3 ◦ C above the set point
respectively.
[68]
Buildings’ temperature in the
reference case is set to 22 ◦ C.
The control methods in
scenarios are scheduling-based
control and dynamic pricebased control.
Based on an optimization
model, demand-side flexibility
through rising or falling
temperature deviations, supplyside flexibility due to TES, and
the interaction of demand
flexibility and centralized TES
in the DHS are investigated.
(1) The potential for modulation of
the building’s thermal mass
depends on factors such as its
thermal insulation properties,
which vary over time;
(2) Poorly insulated buildings have a
brief autonomy, whereas passive
homes have a longer time
constant. They are contrary to
other storage solutions (e.g.,
battery, hot water tank).
(1) The new, better insulated
buildings have a longer cut-off
duration potential, reaching 6 h
in different building archetypes.
(2) More solar thermal energy can be
utilized more effectively when
thermal mass storage is used for
intraday.
DR of building SH smoothes out
fluctuations in the profile of total
urban heating demand. Compared to
the reference scenario, the total
shifted SH demand throughout the
year is 8.4 % in the DR 1 ◦ C scenario
and 9.4 % in the DR 3 ◦ C scenario.
The strategies lead to an increase in
peak demand and energy use (by 35
% up to 49 %). The increase in energy
consumption occurs mainly during
lower load and production price
periods.
(1) Heavy buildings with large time
constants have the potential for
upward temperature adjustment
(DR), whereas light buildings use
temperature downward
(operational energy efficiency)
more effectively.
(2) Allowing an upward deviation in
indoor temperature reduces total
system operating costs by 9 %
compared to allowing a
downward deviation in building
indoor temperature. Also, for
both temperature deviations, the
overall system operating costs
fall by 11 %.
(3) When demand-side flexibility
and centralized TES are utilized
together, they are
complementary.
The result demonstrates that utilizing
DSM strategies, a heat pump solar
contribution of 0.79 is attained. The
energy flexibility of the net-zero
energy house can also be improved by
using only DSM strategies without PV
or PCM TES units.
[69]
[70]
Four DSM strategies were used
in the energy flexibility study of
net-zero energy house, i.e.
overheating/cooling,
preheating/cooling,
temperature setpoint relaxation
and heat pump charging TES.
In a heating system, TES can decouple the production and use of
energy at different time scales and adjust the power used and the heat
transferred over time. Fig. 5 shows the possible distribution of TES units
in the DHS. Depending on the response time of the heat storage, TES can
be divided into seasonal thermal energy storage (STES) and short-term
heat storage.
STES can effectively store the heat energy during the non-heating
season for heating in the winter, thus improving the flexibility of en­
ergy generation, transmission, and demand. A typical application of
STES is solar thermal storage [126]. For example, Li et al. [127] studied
the performance of solar HP systems with seasonal storage tanks and
DHW storage tanks. The simulation results showed that the monthly
energy saving rate of the system was 52 % of that of the traditional
heating system. Dan et al. [128] showed recently realized and planned
small-scale solar district heating systems with STES in Europe. It was
also pointed out that the heat storage efficiency of small STES systems
was not high. STES also works with other components, such as
high-temperature HPs, to achieve efficient operation of heat source
plants. Antoniadis and Martinopoulos [129] calculated the heating and
DHW requirements based on a typical detached house in Greece. The
results showed that the system could cover more than half of the space
heating load demand in the second year. In the northern hemisphere,
especially in high-latitude countries, parameters such as the volume of
heat storage equipment have an important impact on solar heating.
Larger storage volumes will store more solar energy, but higher storage
temperatures will cause greater heat loss. Shah et al. [130] studied the
thermal storage temperature, heat pump capacity, solar collector area,
heat storage volume, drilling depth, types of heat exchangers, and other
parameters of STES and clarified the relationship between these pa­
rameters. In addition, in distributed substations, the installation of en­
ergy storage devices can provide medium- or long-term heat storage on a
time scale of days, which is beneficial for demand-side response and load
regulation [131].
Short-term TES is intended to improve the peaking capacity of
heating systems [37]. The use of short-term TES in DHS can be seen as a
beneficial way to temporarily balance excess power in the grid, which
also improves the flexibility of heating and power systems. Short-term
TES’s common working time ranges from a few hours to a few days.
For example, in CHP systems, short-term TES can decouple generation
from heat load, that is, generate electricity when the price of electricity
is high and store part of the heat for use when needed [132]. Heat pumps
can also generate and store heat when electricity prices are low [133].
The short-term TES of DHS can be mainly divided into tank TES,
network TES, and building thermal mass TES. Their key characteristics
are shown in Table 4.
The concept of the building thermal battery (BTB) is derived from the
use of thermal mass TES and the analogy with conventional recharge­
able batteries. BTB has become a part of the research on building energy
flexibility. From the heating point of view, buildings with different
insulation properties perform differently in terms of their thermal
response and energy flexibility potential. During a certain overheating
period, a well-insulated, low-energy house can reduce heating time,
while poorly insulated houses, despite their limited energy flexibility,
can absorb and transfer more energy per unit of time, thus having a
greater impact on grid load fluctuations. Additionally, the process of
building heat storage, heat charging, and heat discharging is also related
to a variety of factors, such as the building type, climate condition,
occupant behavior, internal equipment type, equipment heat gain, heat
storage start time, energy price, and so on [42,55,65,141]. In particular,
PCM can consider being integrated into building thermal mass [142].
For example, Ref [143] highlighted the opportunity for PCM to be in­
tegrated with building thermal mass. They considered that integrating
PCM with furniture could increase the effective thermal inertia of
lightweight frame buildings without construction. Johra et al. [144] also
losses in the network. So in the future, the system parameters of the
ULTDH system, including pump power consumption, heat loss from the
network, and the COP of HPs, should still be deeply investigated. Thus, it
can be demonstrated to what extent the ULTDH system can obtain better
performance advantages than LTDH. Sanitary challenges are also com­
mon to low-temperature networks. A common solution is to heat and
circulate hot water. However, the system with a storage tank has a heat
loss of 50 % [125] and the low velocity water flow is considered to favor
the growth of bacteria (especially Legionella) [38].
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Y. Guo et al.
Table 2
Summary research related to substations.
Articles
Research focus
Operating temperature
range
Research approach
Main work
[80]
Substation design
–
Concept introduction
[81]
System design and test
–
Simulation and test
[82]
System design and
performance studies
Network temperature
range: 35–45 ◦ C
Theoretical analysis
65/35 ◦ C
Theoretical analysis
DHS return temperature
range: 40–90 ◦ C
Simulation
[85]
Primary side: 110/60 ◦ C
Secondary side: 65/25 ◦ C
Theoretical and
simulation analysis
[86]
Primary side: 110/60 ◦ C
Secondary side: 55/25 ◦ C
Simulation
[87]
Case 1: 110/60 ◦ C
Case 2: 75/60 ◦ C
Theoretical and test
analysis
[22]
Network temperature
range: 6–17 ◦ C
Warm pipe range:
16–40 ◦ C
Cold pipe range: 6–30 ◦ C
Simulation
Primary side: 80/50 ◦ C
Secondary side: 60/40 ◦ C
Secondary side range:
42.2–52.2 ◦ C
Theoretical and test
analysis
Theoretical and
simulation analysis
–
Theoretical and test
analysis
[92]
Supply temperature range:
93 ◦ C–97 ◦ C
Theoretical analysis
[93]
–
Theoretical analysis
[94]
–
Theoretical analysis
[95]
–
Theoretical and test
analysis
Theoretical analysis
[97]
High-temperature case:
95/63 ◦ C.
Low- temperature case:
65/48 ◦ C.
–
[98]
Secondary side: 80/50 C
Simulation
A bidirectional thermal station design, testing, and simulation scheme was
proposed in the conception of a smart thermal grid.
(1) A numerical model of the thermal-hydraulic parameters of a DHS was
simulated.
(2) The design of the bidirectional substation test facility was given.
(1) A non-uniform temperature DHS with distributed HP and standalone TES was
proposed.
(2) The system was analyzed for design, size and thermodynamics with several
popular DH schemes.
The technical and economic feasibility of the energy cascade between the low-and
high-temperature networks was evaluated. This was centered around the design of
the substation.
(1) Different hydraulic schemes for substations were studied.
(2) Parameters such as thermal efficiency and cost were compared among different
schemes.
(1) A substation using main DHS return water was proposed, which was used to
connect the existing DHS to low energy building areas.
(2) The energy performance of the system was analyzed.
(1) LTDH based substation was designed.
(2) Energy and exergy analysis was used to analyze and compare the configuration
of this substation.
(1) The design and control concept of two solar thermal feed-in substations were
presented.
(2) Measurements of feed-in operation and energy flow at the two types of sub­
stations were compared.
A new network known as the reservoir network (design that includes substation
components) was advanced to suit the bidirectional flow of prosumers.
(1) Bidirectional, building thermal station models capable of heating and cooling
were designed.
(2) In Modelica, two substation cases were simulated and their performances
evaluated.
A bidirectional prosumer substation s was designed. Its performances such as
hydraulic characteristics were tested.
Based on a data-driven approach, prediction control method for substation was
developed, and the corresponding secondary loop supply temperature was
evaluated.
(1) A descriptive data mining based approach was developed to improve the energy
performance of substations.
(2) The method was applied to real substations to demonstrate the applicability.
An optimization scheme for DHS (containing substations) was proposed. The
scheme was based on mixed-integer linear programming and was implemented in
the R programming environment.
An intelligent and fast method for predicting daily thermal demand in large
building networks was introduced. It was based on flow and temperature data from
substations.
A virtual sensor-based method for estimating DH energy consumption was
developed and validated. In this, the problem of missing sensors in substations was
considered.
(1) A method for automatic detection of heat exchanger fouling in a substation
using virtual sensors was proposed.
(2) The method was evaluated in a real substation.
(1) A method for reducing the return temperature considering the control of a
substation was proposed.
(2) The effect of optimized control curves on the return temperature was
investigated based on an indirect substation steady state model.
(1) An MPC strategy was presented and demonstrated.
(2) Operational data from substations were used in the simulation and testing.
A DR regulation strategy to reduce the thermal peak of DHS was adopted for
substation control.
(1) A new control and automation algorithm was proposed to deal with the
complexity of decentralized solar feed-in DHS.
(2) The developed simulation test rigs contained models of the combined supply
and feed-in substation, and their test results were presented.
Standard exergy analysis was utilized to calculate multiple energy indicators for the
Dutch energy sector.
(1) The shortcomings of the traditional power plant efficiency analysis based on the
principle of conservation of energy were revealed.
(2) Two new exergy efficiency graphs were presented.
(1) DHS configurations at different temperatures were compared, including their
exergy efficiency.
(2) The energy efficiency and annual heating costs of DHW using electricity in
LTDH (including substations) were evaluated.
[83]
[84]
Substation design and
performance studies
[88]
[89]
Substation design and test
[90]
Operation control
[91]
Operation optimization
[96]
Control strategy
optimization
◦
Simulation
Simulation
[99]
Control algorithms and
hardware construction
110/60 ◦ C
Simulation and test
[100]
Energy and exergy
analysis
–
Theoretical analysis
–
Theoretical analysis
(1) Conventional system:
80/40 ◦ C
(2) Conventional system:
65/55 ◦ C
Theoretical analysis
[101]
[102]
(continued on next page)
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Renewable and Sustainable Energy Reviews 189 (2024) 114017
Table 2 (continued )
Articles
Research focus
(3) Conventional system:
60/30 ◦ C
(4) Low temperature
system: 45/25 ◦ C
–
[103]
[104]
Operating temperature
range
Software components
development
Production mode: 65 ◦ C
Consumption mode: 45 ◦ C
Research approach
Main work
Theoretical and
simulation analysis
(1) The energy and exergy properties of sensible TES were investigated.
(2) Potential loss mechanisms for typical urban TES were characterized
qualitatively and quantitatively.
A Modelica library pronet was proposed and validated to study the performance of
prosumer-based networks. The substation component was also part of this library.
Simulation
downward DSM, which includes precooling, preheating, or zone tem­
perature resetting. For describing and quantifying the heat storage and
flexibility potential of such a prosumer, the building can be analyzed as a
virtual battery [145–148].
The BTB, as a supplement and extension to the traditional
rechargeable battery concept, refers to the use of an envelope and indoor
thermal mass to store supplied heat and release this heat through radi­
ation and convection under certain conditions [149,150]. As shown in
Fig. 6, BTB can shift equivalent heat loads through thermal inertia and
certain DSM modes while maintaining indoor thermal comfort. For
example, PV and HPs could be used to preheat a room during the day
(the heat charge stage), and some of the maintained heat could be used
at night (the heat release stage). In the performance study of the BTB,
Tahersima et al. [151] regarded a large-volume concrete radiant floor as
the thermal battery of the building and conducted a control experiment
using the temperature gradient of two concrete floors of different
thicknesses. Panao et al. [149] showed that the thermal insulation
performance of buildings is the key driving factor for the thermal effi­
ciency of the building battery system. Zhang et al. [150] defined the
performance parameters of BTB. These parameters were modeled,
identified, and provided as the basic parameters for building thermal
performance analysis.
BTB can be used to quantitatively compare energy storage in
different buildings and to describe the energy flexibility potential of
buildings [152–154], and the resistance-capacitance (RC) model is a
method for studying energy flexibility in buildings [152,155] and is
closely linked to BTB. RC models are heavily used in dynamic modeling
and rapid simulation of buildings. Li et al. [156] examined thermal
dynamic analysis, thermal load estimation, building control and opti­
mization, regional and city-scale energy modeling, and RC model inte­
gration. Thermal-dynamic analysis focuses on the heat transfer
characteristics of the building envelope. In addition, the RC model can
simulate the thermal response of a building in a battery-equivalent
model of a residential HVAC system. The study of BTB requires the
following: (1) Building thermal zoning. This step includes the selection
of the external thermal mass and the internal thermal mass [157,158];
(2) order reduction Under the premise of maintaining an acceptable
level of accuracy, the RC model’s order reduction will reduce the
computational burden of model calculations and parameter estimation
[159]; (3) the parameter identification. A number of key building pa­
rameters need to be identified and used for prediction based on estab­
lished RC models and experimental data [160–162]. The BTB can use a
thermal and electrical comparison approach, drawing on the perfor­
mance parameters of a battery as a quantitative parameter for the
thermal mass storage of the building [163–166]. These building pa­
rameters can be: thermal resistance, thermal capacity, storage capacity,
storage density, exothermic power, sustained exothermic time, storage
life, etc. [145,167]; and (4) BTB measurement. The performance of the
BTB should be completely measured, and data such as room air tem­
perature and equipment heating power are measured during the heat
storage and discharge cycles of the BTB. Further, the experiments should
be combined with mathematical modeling to further analyze the vari­
ation of BTB parameters as the boundary conditions are changed.
Fig. 3. A schematic diagram of a kind of substation connected to 5GDHC
(adapted from Ref. [35]).
Fig. 4. Topology diagram of a kind of ULTDH (adapted from Ref. [35]).
indicated that the incorporation of PCM materials into wall panels or
furniture can increase the thermal insulation and heating flexibility of
the house, particularly in light weight construction. Furthermore, the
heat storage and discharge properties of the building thermal mass give
the building the ability to become a heat prosumer: the building’s in­
ternal mass can both store a portion of the heat produced by the DHS or
HVAC devices and mitigate load fluctuations in the heat and power
networks through heat discharge. This enhances the flexibility of the
energy network. This flexibility needs to be achieved through upward or
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Renewable and Sustainable Energy Reviews 189 (2024) 114017
Table 3
Summary of research on low-temperature networks.
Thermal
network
categories
Research fields
Articles
Contributions
LTDH
Literature
review
[108]
System
performance
[109]
(1) More than 40 demonstration
projects of LTDH systems in
Europe were summarized.
(2) More broadly, digital
technologies were needed for
LTDH integration processes
involving multiple heat sources
and complex systems.
(1) Dynamic modeling was proved
to be able to study LTDH better.
(2) The utilization of waste heat
from prosumers lowered the
heat demand and
environmental impact of DHS.
(1) The possibility of integrating
the prosumers into the heating
network was explored. A
software capable of analyzing
the hydraulic and thermal
characteristics of networks was
developed.
(2) The heat loss in ULTDH was
reduced by about 80 %
compared to LTDH. About 20 %
of the total heating demand
could be covered by the waste
heat from prosumers.
The study included the effect of
network layout, additional booster
pumps, layoutsand substation type
for LTDH.
A mathematical model of a district
heating and cooling pipe network
was developed. As a whole, it could
reflect inlet temperature
fluctuation, mass flow change, and
network layouts of the branched
and radiation networks.
(1) Single-pipe networks were
usually simpler and require less
pumping power than two-pipe
systems.
(2) HP energy consumption was
higher in single-pipe systems
than in two-pipe systems.
(1) A low-temperature two-pipe
loop network was proposed.
(2) The share of heat loss in the
total energy consumption of
this network was reduced to 12
%. It was suitable for low heat
density areas and had cost
advantages.
Water-restricted, decentralized
residential substations could help
alleviate Legionella problems in
heating systems.
The decentralized substation
system was designed and modified
to reduce the average loop
temperature to avoid the risk of
Legionella.
(1) A new reservoir network
capable of simultaneous
heating and cooling was
proposed.
(2) The reservoir network might be
slightly higher in terms of cost
and power consumption
compared to a typical
bidirectional network. Instead,
the advantage of it is its good
[110]
Network
performance
[111]
[112]
[113]
[114]
Sanitation
[115]
[116]
ULTDH
System design
and modeling
[22]
Table 3 (continued )
Thermal
network
categories
Research fields
Articles
[117]
[35]
System
performance
[88]
[118]
Contributions
scalability and robust
operability.
(1) A networks design method of
using linear program was
proposed. The network had
only one warm pipe and one
cold pipe.
(2) The buildings and the energy
hub were selected and sized for
optimal energy conversion
units.
(1) A prosumer-dominated bidi­
rectional network topology
concept was presented.
(2) A mathematical model of this
network was derived, which
consists of the thermohydraulic
steady state equations as well
as physical models of the
network components.
(1) Traditional heating systems
with higher temperatures have
better energy efficiency than
ULTDH.
(2) HP solutions are needed when
ULTDH is used to minimize
network heat loss. However, it
is worthwhile to investigate
whether the method of
producing DHW with
electricity consumption can
offset the heat loss.
(1) The optimal integration of
ULTDH with booster HPs was
investigated.
(2) The optimal return water
temperature of ULTDH is
21 ◦ C–27 ◦ C. The performance
of ULTDH has improved by 7
%–23 % compared to LTDH.
Fig. 5. Location of TES units in the DHS.
4.4. Model predictive control
The building DHS needs to be controlled and optimized to ensure it
has the potential to be flexible in real time. Commonly used in building
control strategies is rule-based control including on/off controllers and
proportional-integral-derivative controllers [168]. These simpler con­
trol approaches suffer from long time lags in processing responses, large
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Renewable and Sustainable Energy Reviews 189 (2024) 114017
addition, the state estimation algorithm and the optimal control solution
method are also applicable to different controller models of MPC [168].
The core of MPC is optimization, and there have been many cases in the
simulation and application of MPC [171–173]. Based on building en­
velope characteristics [174], indoor comfort [175], technical con­
straints, and weather forecasting [176], and predicting future building
behavior [175], MPC can solve problems like limiting and shifting peak
demand [177], optimizing the operation of systems [178], providing
flexible control of HVAC systems [179] and components (e.g., boilers,
HPs, chillers, heat exchangers, etc.) [180], and evaluating internal [181]
and external [182] disturbances (e.g., occupancy, solar radiation), etc.
The development of controller models in MPC is very important. The
quality of the solution depends on the accuracy of the model. Ref. [183]
highlighted the importance of predictive mathematical models in the
review of the MPC technique and illustrated that there was still a need to
develop economic predictive models for commercial applications. It was
also pointed out that the predictive model and control need to integrate
the development of the initial model, the service life of the building, the
placement of sensors, and the comfort of residents.
In MPC, the objects to be modeled can be HVAC systems, on-site
energy production systems, or energy storage systems. Models can be
either physically-based white box models [184,185] or data-driven
black box models [186,187]. For the former, it is more difficult to
directly and accurately identify the various parameters of the model
when considering the DHS for a wide range of operating conditions. The
model is more computationally intensive when partial differential
equations and large matrices are involved. These limit the adaptability
of white-box models for predictive and optimal control. For the latter,
data-driven MPC is able to overcome the complex and non-linear ther­
modynamics of buildings. It reacts to predictive variables such as the
environment, grid signals, or occupant behavior and is used in building
energy management and DSM to balance the needs of the smart grid.
The data-based black box model is able to predict internal and external
perturbations acting on the system and create dynamic models offline.
Maddalena et al. [188] analyzed and summarized the application of
data-driven technologies in HVAC in terms of occupancy and occupant
behavior forecasting, heat load and energy demand forecasting, batch
learning and building climate control, and online learning and building
climate control. In addition, barriers to be overcome and areas that have
not yet been explored were noted. Kathirgamanathan et al. [189]
reviewed the application of data-driven MPC in DSM. It was believed
that data-driven MPC with IoT could be a scalable and transferable
Table 4
Key characteristics of the short-term TES approach.
TES
methods
Derivation
Investment
expenses
Operating
features
Reference
Network
TES
Hot water in
the network
Nearly zero
[13,134]
tank TES
Heat storage
media in the
tank
High level
(Tank
construction
and media
filling)
Building
thermal
mass
TES
Thermal
storage of
building
structures and
internal
equipment
Medium level
Complex
operation and
high heat loss
Extra heat
provided during
peak heating
periods;
Larger heat
storage and
longer storage
time (2 days–2
weeks);
Better thermal
flexibility in
combination
with equipment
such as HPs
Utilization
through DSM and
cooperation with
users
[135–138]
[139,140]
oscillations when applying on/off control to set values, and poor
controller performance when deviating from the integer conditions,
which makes rule-based control unsuitable for extension to more com­
plex building levels [169]. Although many advanced control strategies
exist today, model predictive control Model predictive control (MPC)
can replace current, rule-based passive building control methods and
become the dominant control strategy for intelligent building operations
[168].
As the demand side of the energy system, the building side can be
optimally controlled for building energy flexibility using the MPC
approach. The purpose of optimal control is to maximize the flexibility
of building energy systems [134]. As one of the core strategies to opti­
mize building energy control and operation, it is very important to
develop predictive models for building energy systems. Within a limited
horizon and based on the current system state and feedback from the
network, MPC compensates for prediction errors and model mismatches
that occur during the operation and continues the optimization selection
over the entire prediction range in the following time step [170]. In
Fig. 6. The diagram of BTB.
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Renewable and Sustainable Energy Reviews 189 (2024) 114017
method. The review focused on the link between model development
and control integration. Practical requirements and feature selection
problems using passive thermal mass were also investigated. In addition,
some research gaps, such as the scalability and generality of different
modeling approaches and the lack of benchmarking for building pre­
dictions based on meter data, were also noted. As a model-free algorithm
[190], reinforcement learning (RL) and other machine learning methods
can enable agents to take optimal actions through interactive learning
from historical data, so that RL aims to solve dynamic modeling prob­
lems in MPC and achieve the optimization of rewards or goals [191].
Among them, historical data comes from the deployment of advanced
control techniques in the building, such as a large number of measuring
instruments. Agents integrate user feedback and preferences into control
logic to adapt to the environment.
To solve the problem that it is difficult to make the cold and heat load
of prosumers balance in 5GDHC systems, distributed control can install
agents in the decentralized system [192] or in the hierarchical control
[193]. Agents can be virtual or physical entities that sense mutual in­
fluence, share common attributes, and make real-time responses to the
environment [194,195]. Distributed MPC decomposes the central opti­
mization problem into local sub-problems, which are executed locally by
communication or global coordinators [196]. Distributed MPC is pri­
marily in response to optimization problems in large-scale, multi-area
buildings. For example, Mork et al. [197] proposed an easily extensible,
plug-and-play multi-region distributed MPC method. The results showed
that the distributed MPC method is better than the centralized and
decentralized methods. This study also demonstrated the applicability of
the distributed MPC approach to the control of multi-area buildings.
Fig. 7 illustrates the operating principle of MAS control, a common
decentralized control approach [198]. This method tracks and feeds
back the goals set by the system through the interaction of agents (such
as games and cooperation) at the architectural level.
The agent-based control method has been used to coordinate build­
ing electrical devices and heating [199], manage building energy (such
as RES), optimize the smart grid, and further safeguard the utility grid
[200,201]. An optimized control method based on temperature set
points was proposed to allow modular integration of any number of
sources and users. The results showed that the energy costs of the two
communities used as examples decreased by 46 % and 27 %, respec­
tively, and primary energy consumption was reduced by 52 % and 72 %,
respectively. This study showed that agent control can be used in
low-temperature networks [195]. A control approach based on
agent-based control and temperature set-point optimization was devel­
oped in Ref. [198]. This control mechanism kept the network temper­
ature close to the set point in 5GDHC and enabled the modular
integration of various heat sources and consumers. The results revealed
that the optimized bidirectional network lowered primary energy con­
sumption by 58 % and 84 %, CO2 emissions by 35 % and 78 %, and
energy costs by 53 % and 57 %, respectively. In agent-based control
systems, the behaviors of different agents are interdependent and
competitive, so all agents can achieve the common goal of minimizing
peak demand by sharing information. Vazquez-Canteli et al. [52]
reviewed the application of RL algorithms in agent collaboration,
including artificial neural networks or deep learning techniques, and
considered that the applicability of RL in MAS needs to be further
explored in the DR of MAS. Moreover, it would be an important
breakthrough to test RL and multi-agent cooperative control in an
experimental environment with real human feedback.
5. Discussion of DHS adaptation and new technologies
5.1. Adaptation of DHS
The development of 4GDH and 5GDHC emphasizes and strengthens
many new elements. The diversity of heating systems embodied by
4GDH and 5GDHC does not result in an obvious application gap. Lund
et al. [202] believed that 4GDH and 5GDHC technologies could coexist
and achieve complementary effects in a wide range of situations. As a
result, there is no need to deliberately associate the bidirectional heating
network with 4GDH or 5GDHC based on network temperature or to­
pological structures. For example, the hot summer and cold winter re­
gions in China are mostly distributed in southern China, with more
surface rivers and lakes and other rich natural cold and heat sources,
while their heating demand is not high. In this area, the heating prob­
lems in residential communities deserves to be studied. The end-user
regulation flexibility in such areas is large, the air conditioning load
fluctuation is large, and the cold and heat sources, transmission, and
distribution systems need to consider long-term operation under
low-load conditions. Considering indoor thermal comfort, heating en­
ergy efficiency, and other factors, the traditional air source HPs, gas
heating, and household air conditioning are not completely applicable.
Therefore, the distributed 5GDHC system combined with the existing
cogeneration system is a good solution to realize central heating in hot
summer and cold winter areas. For the northern urban areas with better
central heating foundations, the current DHS should be transferred to
4GDH systems.
5.2. Integration of energy networks
The following aspects of LTDH improvement need to be highlighted:
(1) the coupling and analogy of heat and power networks on various
time scales; (2) hydraulic cascades with high temperature heating net­
works; (3) safe and hygienic DHW technologies; and (4) the use of
lightweight, standardized components. In a broad sense, the integration
between the various sectors will propel the development of energy
system flexibility in the context of intelligent building energy systems.
The building energy network is composed of multiple energy subnetworks, such as electricity, heat, and gas. The modeling and analysis
of each sub-network are important for the optimal operation of the in­
tegrated energy system. It can be argued that any type of energy can be
described as a function of the interaction between a basic extensive
property and a basic intensive property, which could be utilized to
create a unified mathematical expression for the transfer of energy
[203]. In building energy systems, the "circuit model" helps reveal
commonalities in heterogeneous energy flows, including heat flows.
Methods such as matrix modeling of power systems and the equivalence
of outer port boundaries can also be applied to create a unified building
energy network. Among them, when the system’s scheduling is opti­
mized in accordance with the physical parameters of the network’s
boundary ports under challenging spatio-temporal conditions, the
"two-port network" equivalent method can quantify the impact of the
external environment on the network’s transmission characteristics
Fig. 7. Schematic diagram of multi-agent control in a bidirectional network
(adapted from Ref. [198]).
11
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Renewable and Sustainable Energy Reviews 189 (2024) 114017
[204]. Considering the slow dynamic process of thermal flow and other
energy forms, the joint analysis and scheduling operation optimization
of multi-energy networks are mainly suitable for the time scale of mi­
nutes and hours. However, on these time scales, network joint analysis
still needs to use partial differential equations to describe the dynamic
characteristics, which makes it difficult to be completely unified with
power system analysis.
can refine the whole life cycle management of DHS. This will make it
possible to manage the DHS’ whole life cycle and achieve optimal
design, energy-efficient operation, and fault diagnosis.
6. Conclusions
This work summarized the latest results of DHS and analyzed the role
and potential of important technologies for heating systems in terms of
building flexibility. This work will inspire and contribute to topics such
as heating research, policy development, energy saving, and emission
reduction. The limitations of this study were the findings of newer
research that may have been designed or implemented and the fact that
papers from non-English-speaking countries were not retrieved. These
limitations will motivate this work to continue to summarize flexible
heating technologies for greater efficiency.
The study argued that the development of heating systems reflects a
change in social relations in production. The original unidirectional,
top-down heating will be transformed into a bidirectional game and
interaction between the utility sector and the prosumers. This transition
in the concept of production is more prominent in 4GDH and 5GDHC.
Flexible heating systems give rise to the creation of regional, autono­
mous heating systems on a campus basis. This will allow the manage­
ment of heating to be transferred downward, driving the localization of
energy use and management.
In addition, the integrated energy network will promote the inte­
gration and flexible operation of multiple energy sectors such as heat,
electricity, and gas. System managers will cater to the differences in
spatial and temporal heating and cooling needs of different users, with a
focus on supply-side cost principles and demand-side incentives. These
require both the modelling and analysis of a unified network theory and
present new engineering challenges. Some new techniques, such as BTB
modelling, DT, and other digital technologies, can provide powerful
tools.
The development of new heating technologies requires attention to
the following aspects:
5.3. Building thermal battery technology for building heating flexibility
Comparing a building to a BTB allows for the use of thermal storage
in buildings to reduce heating peaks and aids in the quantitative analysis
of building energy demand profiles as well as degree of flexibility inte­
gration and DR. Deeper research into the BTB still requires: (1) the
downscaling and optimization of RC models. This involves combining
climate parameters and occupants’ behavior, considering the differences
in thermal mass storage of different buildings and their effects on the
total building heat storage. It is also required to compare the accuracy of
the downscaling methods of different RC models for simulating the
thermal properties of buildings so as to determine the best RC model
downscaling method. (2) parameter quantification and identification of
BTB Corresponding to the performance parameters of conventional
batteries, the performance parameters of the building thermal batteries
need to be clearly defined, including system performance parameters
such as thermal resistance, thermal capacity (short-term and long-term
thermal capacity), rated temperature difference, rated capacity, thermal
mass material’s storage efficiency, thermal release rate, and release
time, etc. After the construction of the reduced-order RC model, the
unknown parameters of the thermal battery can be identified by the
minimum error between the measured values and the simulated values
of the RC model using the minimization objective function. The identi­
fied parameter values will be applied in the parameter calculation of the
BTB. However, in BTB modeling, efforts are still needed to reduce pro­
cess errors and to analyze the relationship between building design
parameters and the flexibility of building thermal storage.
5.4. DHS optimization in digital twins
(1) DSM and optimized control face challenges. The first is that key
information about building energy systems (e.g., building type,
weather data, energy consumption, and system operating pa­
rameters) is difficult to extract in a comprehensive manner. This
involves a large amount of data processing and requires over­
coming different spans of time and space (information at the
building and district level may be faster to access, but that at the
system and sector level is huge and not available in a timely
manner). Second, there is a lack of standardized quantification of
energy flexibility. This relates to the regional conditions, opera­
tional phases, size composition, heat demand, and seasons of
different DHS operations in different locations. Together with
different research topics in the literature, it is difficult to repre­
sent building energy flexibility in a standardized way. Thirdly,
the flexibility of operation to consider the thermal environment
needs of indoor occupants needs to be deeply explored. Different
combinations of end-users and related facilities may lead to
different thermal comfort evaluations.
(2) BTB, as a modeling approach to study the flexibility of buildings,
is able to portray the operational characteristics of building
prosumers from the perspective of energy storage and discharge.
Preliminary research work on BTB includes describing the phys­
ical characteristics of buildings through parameter identification
and investigating the performance of BTB under different flexi­
bility events. The definition of BTB needs to be expanded in the
later work so that the scope of BTB research can be extended from
the building envelope to HP, EV, various TES, and smart home
devices. A broad BTB study will be able to reveal more compre­
hensively the flexible interaction behaviors between prosumers
and various types of energy networks. Such research, of course,
Digital technology offers new ways of managing and optimizing the
DHS. The digital twin (DT) as a digital technology is able to reflect
physical entities with highly accurate digital models and interact, pre­
dict, and give feedback to the whole system in real time [205]. For better
application of DT in heating systems, the following issues also need to be
addressed: (1) the issue of the creation of a digital model. This requires
that the current model of the DHS also be enhanced in detail, with
parameter identification for the system equipment. DT modeling also
need to be emphasized to the assessment of model uncertainty and
improve the robustness and predictability of the DT model through error
compensation [206]; (2) the issue of bidirectional interaction between
DT and physical entities. Data collection and monitoring by sensors are
essential in the transfer of information from the DHS to the model. The
addition of sensors, particularly in large DHS, enables the perception
and real-time adjustment of the hydraulic and thermal conditions of the
networks as well as the spatial circumstances within the building.
Augmented reality technology can enhance managers’ perceptions of
real heating systems based on realistic, real-time, immersive 3D images
[207]; (3) the data processing and application of DT. This requires the
collection and analysis of real-time and historical data from the DHS to
accomplish data correlation and predictive control. As the computa­
tional power of computers increases, a number of data-driven, online
MPC solvers are able to solve more optimization problems. Optimal
control methods such as RL can use previous data or states in order to
fully learn and derive an optimal policy for the system. These methods
are more robust and applicable. MPC can take full advantage of the
repeatable and controllable conditions of the DT platform to select and
optimize control solutions for building energy systems. Further, (4) DT
12
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Y. Guo et al.
needs to be guided by both theoretical frameworks and supported
by comprehensive empirical data.
(3) The DT technology has great development potential in operation
monitoring, information interaction, and the flexible and stable
operation of the thermal network. DT technology maps the real
data generated by the nodes in the thermal network (e.g., pro­
sumer, substation, etc.) in real time to the virtual world for
simultaneous analysis. However, the restricted arrangement and
untimely transmission of sensors, inaccurate measurement of
system parameters, and long computation time of mathematical
models affect the processing effect of DT. The solution to these
problems depends on the optimization of sensor hardware, in­
formation technologies such as visualization, tracking, and pre­
diction, and existing building technologies. Among them,
different types of prediction algorithms (e.g., RL, artificial neural
networks, and deep neural networks) need to continue to be
optimized for environmental fluctuations, operational failures,
inhabitant preferences, pipeline network scheduling strategies,
cost consumption, and many other factors. Finally, future
research into more accurate and faster alternative algorithms is
necessary to enable DT technologies to achieve the goals of
monitoring the system as it operates, predicting ahead of action,
and providing feedback in real time for multiple decisions.
(4) Policies and platforms that enable two-way energy trading are
needed. Government and market regulations or policies can guide
different regions in integrating local resources and establishing
locally adapted heat supply systems. New trading platforms can
encourage a wide range of users to participate in the operation
and pricing of heat networks.
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CRediT authorship contribution statement
Yurun Guo: Conceptualization, Writing – original draft, Writing –
review & editing, Visualization. Shugang Wang: Writing – review &
editing, Supervision, Validation. Jihong Wang: Supervision, Validation.
Tengfei Zhang: Supervision, Validation. Zhenjun Ma: Supervision,
Validation. Shuang Jiang: Writing – review & editing, Supervision,
Validation.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
No data was used for the research described in the article.
Acknowledgements
This work was co-supported financially by National Natural Science
Foundation of China (grant numbers 52378090, 51678102, 51508067),
the Opening Funds of State Key Laboratory of Building Safety and Built
Environment & National Engineering Research Center of Building
Technology (No. BSBE2021-08), and the Scientific Research Fund
Project of Liaoning Provincial Department of Education (No.
LJKZ0021). The authors were also grateful for those helpful inputs from
the Editor and anonymous reviewers.
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