Stay Informed
Follow us on social media accounts to stay up to date with REHVA actualities
Rasmus Elbæk
HedegaardPostdoctoral researcherAarhus University, Department of EngineeringAarhus, Denmark | Steffen PetersenAssociate professor, head of research groupAarhus University, Department of EngineeringAarhus, Denmark |
Building
energy efficiency has been a societal priority in Denmark for almost five
decades. The oil crisis in the early 1970s was the starting point for what
would become a series of revisions made to the national building regulations,
each increasing the requirements for the energy performance of building
envelopes. These requirements have now reached a point where the agenda is set
by our climate ambitions more than the real financial gain associated with the
last centimetre of insulation material in the outer walls. Despite these strict
requirements of the current building regulations, the reality is that for many
years a large part of the building stock will continue to be characterized by
the much more lenient requirements of previous building regulations. For the
majority of these buildings, it will be expensive or practically impossible to
achieve significant reductions in energy consumption through energy renovation
measures. It is therefore clear that we cannot hope to solve our climate issues
by further insulating and air tightening our buildings alone, but that our
efforts to improve energy efficiency must also be complemented by other
measures such as the ongoing transition to a greener energy production.
The
challenge of an energy system based on a large share of renewable energy is
that the production is directly dependent on weather phenomena and thus
fluctuating by nature. The fluctuating supply of energy necessitates a high
degree of flexibility in the rest of the energy system. This may be achieved by
either establishing a large capacity for short-term storage of energy, or by
making parts of the energy consumption itself flexible. In recent years,
researchers have therefore investigated which energy-consuming activities in
our society that could potentially deliver this flexibility. Since a
degradation of consumer comfort is rarely a successful route to take, the task
is therefore to identify the parts of consumption that can be manipulated
without imposing additional costs or a loss of comfort for the consumer. Some
of these potential sources of energy flexibility include chargers for electric
vehicles and home appliances that can be configured to start only when
production is sufficient, and the electricity grid is not overloaded. Research
is also being conducted on the extent to which the heat consumption in our
buildings can be made flexible due to the large amounts of energy involved; 27%
of all energy use in European Union is for space heating according to
euroheat.org. Since the electricity grid is particularly sensitive to
imbalances between production and consumption, most of the research on the
potential of energy flexibility focuses on the electricity-based heating forms.
Nevertheless, flexibility is also relevant in district heating, where, for
example, flexible consumers can help to reduce peak loads, thereby lowering the
necessary capacity in the heat production and the associated distribution
networks. Freeing up capacity in distribution networks is highly relevant as it
opens up the possibility for lowering the supply temperature in the district
heating network, thereby lowering heat losses in the distribution grid and
increasing the efficiency of the overall district heating system.
In district
heating systems, the morning peak caused by the many coincidental showers of
consumers is a recurring daily phenomenon. The morning peak on the dimensioning
day results in a direct increase in the necessary production and distribution
capacity in the district heating network. Since the consumption of domestic hot
water, which together with night setback schemes is the primary cause of the
morning peak, is closely related to the daily routines in the household, we do
not consider this part of district heating consumption to be flexible in
practice. Therefore, if we want to reduce the morning peak in order to lower
the necessary capacity in the system, we must do so by creating a corresponding
valley in the second of the main components of district heating consumption:
space heating. Part of this consumption can be made flexible if we part with
the practice of setting a temperature set point for
our heating systems, but instead specify the range of
temperatures that we as consumers consider comfortable. Such a comfort
range can be adapted to the preferences of the individual consumer, and could
for instance define that room temperatures should be maintained between 20°C
and 23°C at all times throughout the heating season. The fact that such a
comfort range allows us to use advanced control schemes to impose subtle and
controlled temperature fluctuations in the building creates opportunities for
shifting heating consumption in time - for example by preheating the building
prior to the morning peak in consumption. The Danish building tradition's large
use of heavy building materials such as bricks and concrete creates
favourable conditions for this passive storage technique where thermal energy
is stored directly in the thermal mass of the heavy building components.
Figure 1.
Map of the modelled residential area.
To
demonstrate the potential of energy-flexible buildings, we have modelled the
district heating consumption in 159 detached single-family houses located
in the city of Aarhus, Denmark. We have used time series of hourly consumption
data has been made available to us for research purposes by the local district
heating supplier AffaldVarme Aarhus. This consumption data, derived from remotely
read Kamstrup heat
meters, is used to calibrate and validate mathematical models of the
thermodynamic properties of each individual building together with an
associated model for the daily distribution of domestic hot water consumption.
The building model thereby describe the weather-dependent consumption, while
the models for hot water consumption describe the weather-independent
consumption. Figure 2 presents the
consumption predicted by the models and compares it to the measured consumption
– first for two individual single-family houses and finally for the aggregated
159 buildings. In the case of the depictions of individual buildings, we
have highlighted some of the peculiarities that inevitably make their way into
consumption data from in-use buildings. The highlight in the first graph shows
an event where the consumption seizes for a short duration followed by a
kickback where the heating system is likely reheating the building. Since this
temporary drop in consumption was seen for multiple buildings simultaneously,
we assume maintenance work in the district heating network a likely cause. The
highlight for the second building marks a Danish holiday week, where the sudden
absence of domestic hot water peaks would suggest that the building is indeed
vacant.
Figure 2. Comparison
between measured consumption and model predictions. The top and middle graphs
depict time series for two single-family houses, while the third depict the
aggregated neighbourhood (159 single-family houses). The vertical dashed
line separates the data used to calibrate the models (left side), and the data
used for validation purposes (right side).
From
comparing the measured and predicted time series on the single-building scale,
it is clear that the stochastic nature of especially the domestic hot water
consumption is a cause of discrepancy between the models and the actual
measurements. A part of this is due to the assumptions made in the model of
domestic hot water consumption, in which we assume that the domestic hot water
consumption follows the same daily pattern on all weekdays. Similarly, we
assume a separate consumption profiles to apply to all weekend days. While the
method of calibrating the average daily weekday and weekend profiles inevitably
simplifies the true stochastic consumption patterns of residential occupants,
these effects are averaged out once we move to a larger scale. This is evident
from the third graph in Figure 2, in which we
depict the combined consumption of all of the 159 buildings. Here, the
models are capable of predicting the daily variation caused by domestic hot
water consumption with a high level of accuracy. Since the combination of the
models of building thermal dynamics and domestic hot water usage describes the
district heating consumption in the single-family houses quite accurately, we
can use them to test different strategies for utilizing energy flexible
buildings and thereby map the potential associated with them.
In Aarhus,
the coldest hour of the 2016/2017 heating season fell in the early morning
hours of January 6, 2017. Figure 2 shows a
simulation of the 159 single-family houses’ total district heating
consumption in the surrounding days, separated into the space heating component
and the hot water component respectively. The figure shows the starting point
where all the buildings are heated to a constant temperature throughout the day
and where the highest aggregated consumption for the group of buildings reaches
an hourly average of 925.3 kW. The red mark indicates the proportion of
space heating consumption that is moved as we utilize flexible consumption to
reduce the buildings' maximum district heating demand by 10 percent.
In order to avoid creating a new peak in the
hours when we preheat the buildings, it is important to coordinate which
buildings to preheat - and just as importantly when to begin preheating them.
The blue marking in Figure 3 shows how our
preheating efforts may be coordinated such that we can eliminate the
consumption in the red making on Figure 4
without causing a formation of new peaks, and thereby achieve the 10 percent
reduction of the maximum demand for district heating. The energy stored in the
thermal mass in the activated buildings during the preheating process is
released slowly as soon as the space heating is lowered and the room
temperature begins to drop towards the lower limit of the comfort range. When
the lowest permissible temperature is reached, the building's heating system
ensures that the temperature does not drop any further. One consequence of this
storage technique is that some of the stored heat is released only after the
morning rush is over, as can be seen by the elongated tail of the red marking
in Figure 4.
Figure 3.
Simulated consumption under utilization of flexible
consumers. The blue marking highlights the consumption dedicated to preheating
a group of the buildings in the area in such a way that a 10% decrease in the
peak demand is achieved.
Figure 4.
Simulated district heating consumption in the modelled 159 residential
buildings. The models of building thermal dynamics and domestic hot water
consumption are used to depict the consumption as the potentially flexible
demand for space heating and the inflexible demand for preparation of domestic
hot water. The red marking shows the part of the reference consumption that
will be shifted in time in this example (see Figure 3).
The example shows that energy-flexible
buildings are not only relevant in the smart electricity supply of the future,
but can also contribute to value creation in the district heating sector and,
by extension, district heating consumers. One challenge associated with
activating the flexibility potential associated with space heating in buildings
is that predictive control schemes are necessary to ensure that the flexibility
is utilized in the most appropriate way. One of the reasons for this is that it
is not free to move energy consumption in time: in the example, it required on
average 1.31 kWh preheating (the blue mark) to move 1 kWh out of the
morning peak (the red mark). This energy loss is due to increased heat loss
during preheating, and thus depends both on the energy efficiency of the
buildings and on how early it is necessary to start preheating. Another reason
is that, especially during transitional periods, we can easily risk that
preheating of the building in the early morning hours will end up causing the
building to overheat in the afternoon due to solar heat gains, thus resulting
in comfort-issues for the occupants. To avoid these issues, predictive control
strategies combine a model of the building's thermal dynamic properties with
forecasts of weather and internal heat loads to optimize the control strategy
in terms of both economy and thermal comfort. Obtaining models sufficiently
precise for control purposes is therefore a concrete obstacle to realizing this
so-far untapped flexibility potential. As seen in this article, the
availability of time-resolved consumption data is a good starting point to be
able to map the potential in more detail and develop methods for establishing
the necessary building models.
Figure 5.
Second order resistance-capacitance model structure used to describe space
heating demand.
This
article was based on the published research article: R. E. Hedegaard, M. H.
Kristensen, T. H. Pedersen, A. Brun and S. Petersen, ”Bottom-up modelling
methodology for urban-scale analysis of residential space heating demand
response”, Applied Energy, vol. 242, pp. 181-204, 2019, doi: 10.1016/j.apenergy.2019.03.063.
The article was prepared as part of the Local Heating Concepts project.
The authors
would like to acknowledge EUDP for financial support of the project and
associated research activities (Project number 64017-0019).
Follow us on social media accounts to stay up to date with REHVA actualities
0