Master thesis by Panagiota Gianniou, August 2014
Abstract – The mitigation of climate change has been a priority to most countries’ agendas
nowadays. Energy and environmental policies have been introduced to facilitate the
achievement of national targets. At the same time, rapid urbanization has resulted in
converting cities into the main energy consumers and generators of GHG emissions.
Thus, they are an ideal platform where sustainable solutions can be applied which will
improve their durability and functionality. The concept of Smart Cities has the
potential to integrate sustainable technologies and innovative systems into urban
areas. At the same time, the building sector occupies a key place in the development
of Smart Cities. Energy demand of the building sector affects significantly national
energy balances. Furthermore, estimating energy demand of a cluster of buildings, a
district or city requires the aggregation of them. When handling aggregated energy
demand data, future energy predictions and the creation of what-if scenarios for
demand-side energy management are enabled. It also facilitates urban planning, as
well as the development of energy hubs into urban areas.
In the current Thesis, the theoretical background needed to study aggregation of
building energy demands is presented and analyzed. Two methods of aggregating
energy demands of buildings are identified and implemented on a real case-study,
being located in Sønderborg, Denmark. This consists of 16 single-family houses all
connected to the regional district heating system. These were modelled by Termite, a
newly-developed parametric tool, which uses Danish Be10 for energy simulating.
According to the first aggregation way, individual buildings’ energy simulations are
carried out. This method necessitates extensive data availability. Six different
information levels are investigated, concluding that apart from general data about
building’s functionality, floor area and age of construction, also information about the
most recent energy refurbishment state of the building is crucial for achieving high
accuracy in energy demand estimations. According to the second aggregation way,
building typologies are used, where five example buildings representing each type are
simulated. The results highlight that the specific example buildings represent quite
well the respective buildings, but present a deviation from the measured energy
demands. However, the annual aggregate heat demand of this method is found to be
very close to the measured one. Extensive discussion on the challenges and
uncertainties of the present city energy model is also presented.
Abstract – Towards the mitigation of climate chance and the reduction of green-house-gas emissions,
cities, nowadays, persistently tend to increase the power generated from renewable energy
resources. The penetration of renewables, however, implies several side-effects. Renewable
energy sources such as wind and solar energy constitute inflexible energy sources, which are
difficult to manage within an energy system. In addition, the rapid urbanization and its sideeffects
in the energy sector, further deteriorates the situation. This combination of increasing
energy demand with inflexible ways to produce energy motivates researchers to come up with
innovative and effective solutions in order to deal with such challenging issue. Such solutions
constitute the concept of smart cities.
One way to deal with such mater is to explore possible means of energy storage in smart cities.
Considering that the building sector currently occupies a fundamental role to cities, the
investigation of available capacitances in the existing building stock would be a plausible
target. Moving towards this direction, it is essential to search for applicable technologies that
can create capacitances for energy storage in buildings. Subsequently, a city scale application
of such patterns could contribute more effectively to the mitigation of peak demands. .
The current project deals with the issue of peak load management by utilizing the existing
capacities of a building with respect to its heat demand. Such capacities are “hidden” in
buildings’ passive behaviour, which is directly linked with their construction.
In order to assess buildings thermal behaviour, an existing building case is implemented in a
building simulation tool named IDA ICE. Initially, this building model is validated with the
full-scale measurements conducted.
Based on the validated model, parameter variation with three different scenarios is carried out
in order to evaluate the possibility of short-term energy storage, which indicates the flexibility
potential of the examined building model. The first scenario is a proof of concept which
examines the effectiveness of the material used as thermal mass by comparing a heavy and a
light weight construction. The second scenario investigates how accurately the simulation of
building’s thermal behaviour is. Finally, the third scenario uses a preheating pattern in order to
quantify the time interval of the evaluated flexibility potential. Based on the outcome of the
project, it could be highlighted that heavy weight construction is proved as more effective for
storing amounts of heat within its thermal mass. Additionally, a heavily constructed building
combined with a preheating pattern could lead to significant a heat storage, which could
accomplish a significant peak load shifting.MSc Thesis Emmanouil Katsigiannis s121405 (final submitted)