Smart Urban Energy Development Graz-Reininghaus
Stephan Maier, Institute of Process and Particle Engineering,
Inffeldgasse 13/III, [email protected]
Ernst Rainer, Institute of Urbanism, Rechbauerstraße 12,
[email protected]
Werner Lerch, Institute of Thermal Engineering, Inffeldgasse
25/B, [email protected]
Thomas Mach, Institute of Thermal Engineering, Inffeldgasse
25/B, [email protected]
Thomas Wieland, Institute of Electrical Power Systems,
Inffeldgasse 18/1, [email protected]
Michael Reiter, Institute of Electrical Power Systems,
Inffeldgasse 18/1, [email protected]
Ernst Schmautzer, Institute of Electrical Power Systems,
Inffeldgasse 18/1, [email protected]
Hans Schnitzer, Institute of Process and Particle Engineering,
Inffeldgasse 13/III, [email protected]
Yvonne Bormes, Institute of Urbanism, Rechbauerstraße 12,
[email protected]
*all Graz University of Technology, Austria
Abstract
World’s growing cities need an integrated and holistic urban
development due to its complex requirements because of high density
of settlement structures including different purposes of usage. The
City of Graz is currently the fastest growing capital city in
Austria. The demand for living space has grown rapidly in recent
years and, according to forecasts, will continue to grow in the
coming decades.
Reininghaus is a former brewery site and the biggest
underdeveloped urban area in the City of Graz. The research project
ECR (Energy City Graz-Reininghaus) aims to develop urban strategies
for the new conception, construction, operation and restructuring
of the city district Graz Reininghaus. In order to cope with this
complex task, a large interdisciplinary team, including five
institutes of the Graz University of Technology, works together on
this research project.
This paper discusses the energy development of two city
quarters[footnoteRef:1] within the smart urban energy development
of the city district Reininghaus in Graz, Austria. It describes a
first brickstone for a process-oriented approach of urban
development to create flexible and adaptive developments as a
foundation not only for this project development but also for
further regional and urban planning. [1: For the purpose of this
project the city district is separated into quarters which must not
be confused with a possibly bigger city quarter.]
Highlights:
· Exploration of smart energy system networks to cover energy
demand of an urban development
· Determination of price ranges and price limits and feasibility
levels of renewable energy technologies
· Feasibility of renewable energy technologies and waste
heat
Keywords: Smart city, Urban energy development, Urban energy
systems, Process synthesis
1. Introduction
In fast growing cities green- or brownfield areas are valuable
spaces for urban development. Herein for a sustainable development
this can lead to a chance to develop urban areas with open system
boundaries of interdisciplinary considerations and planning.
Collective discussions of technical, architectural, socio-economic
and ecological aspects can lead to integrated development of
sustainable and alternative energy supply strategies, an
integration of ecological aspects (e.g. sufficient trees, wind
system), mobility, public transport, etc.
Figure 1: Localisation of Reininghaus area in city context of
Graz (source: ECR team)
For urban standards the Reininghaus area is an underdeveloped
plot of land (a former brewery area) situated about 1,800 m from
the centre of the middle-sized city Graz (ca. 270,000 inhabitants).
It offers about 110 ha space and a possible full capacity for about
12,000 future inhabitants on a maximum net floor area of about
560,000 m². Architects and other stakeholders variably focus on
different quarters of the city district. In the following case
study the quarters 1 and 4a of altogether twenty quarters will be
discussed.
Figure 2: Quarters of Reininghaus area (source: ECR team)
The quarters 1 and 4a (red marking) are located well in the
north of the district and are owned by the real estate developer
group Erber. At this area a few less well-preserved functional
buildings can be found. These buildings should facilitate the
transformation from a historic industrial site to a modern district
under the aspect of the smart city concept. The real estate
developer wants to create around 670 rental apartments for up to
1,800 people. At the core of the area childcare facilities, medical
offices, pharmacies, offices, local shops, restaurants and cultural
and educational institutions shall find place. The total investment
– including the purchase of land – is about approximately 170
million euros. International architects were invited to a two-stage
competition concerning architecture and green space proposals based
on an urban framework plan Reininghaus (developed by the City of
Graz and Graz University of Technology).
The Institutes of Electrical Power Systems, Process- and
Particle Engineering, Urbanism, the Institute of Thermal
Engineering and the Institute of Technology and Testing of Building
Materials work on a “framework energy plan” for the case study area
based on the idea of an energy self-sufficient and CO2-neutral city
district. This plan is part of the smart city development of Graz
and shall lay the foundations for an integrated smart urban
development of the city district and show alternatives of further
developments within the city Graz. The framework energy plan
concerns various fields of investigation. Electric energy, thermal
energy and embodied energy has to be taken into account as well as
the methods of urban design and the rules and mechanisms of the
local authorities.
2. Case Study
The methods are applied in a smart development of urban energy
supply of the greenhouse area Graz-Reininghaus. The city district
is situated in a brown- and green-field-area of Graz. 110 ha are
available for a new city quarter development with mixed use which
should be as energy efficient, smart and sustainable as possible.
Approximately half of this area (about 49 ha) can be used for
building sites and so partly be sealed with buildings for private
use, offices and commerce. The area was separated into 20 quarters
in the case study, in this paper the focus lies on the quarters 1
and 4a in the north of the total city quarter with an area of more
than 43,500 m². Using this area, 17,577 m² of building area and a
gross floor area of 99,694 m² can be reached. According to the
typical mix of building demand the following shares of the total
space were defined.
Table 1: Gross floor area of Reininghaus quarters 1 and 4a
Gross floor area
Quarter 1
Quarter 4a
Living
56 %
35,744 m²
61 %
21,891 m²
Office
24 %
15,237 m²
16 %
5,913 m²
Commerce
20 %
12,561 m²
23 %
8,348 m²
3. Methodology
The initial working hypothesis describes the conception of an
energy self-sufficient city district. This should be seen as a
visionary approach to force the project team to examine local
energy potentials as far as possible and to anticipate upcoming
future developments. The main focus of the examination is the aim
of the inter-linkage of buildings and industrial energy resources
as more or less sustainable energy producers. Central supply
solutions will be confronted with semi-centralized and
decentralized possibilities of inter-linkage. For example, taking
advantage of the cooling energy potential of the existing brewery
cellars or the waste heat of industrial processes already located
in the city district.
Figure 3: schematic sketch to illustrate the changing of single
system borders to a holistic approach
The implementation of this approach is performed by the
participating institutions on the basis of different tools. It
consists of calculation of demand and supply, selection and
dimensioning of energy technologies, financial aspects and
symbiotic reflections including ecological evaluation of possible
settlement structures.
3.1 Transient System Simulation to investigate the heating- and
cooling demand (TRNSYS)
The Institute of Thermal Engineering uses the simulation
environment TRNSYS (Transient System Simulation Tool) for the
simulation of thermal systems. In the TRNSYS simulation environment
the balancing of the occurring energy flows for active and passive
components in a building can be numerically modelled. This includes
space distribution components like heating, cooling and ventilation
systems, as components representing the local energy supply (e.g.
solar thermal systems, heat pumps, storage tanks, district
heating).
One of the key factors in TRNSYS’ success over the last 25 years
is its open, modular structure. The source code of the kernel as
well as the component models is open to the end users. This
simplifies extending models to make them fit due to the user’s
specific needs. A typical application for TRNSYS is the transient
simulation of buildings, in order to analyse their behaviour in
dependence of climatic conditions and the interaction with the HVAC
system. [[endnoteRef:1]], [[endnoteRef:2]]. [1: [] TRNSYS 17. A
Transient System Simulation Program: V17.01.0025. Solar Energy Lab,
University of Wisconsin – Madison, USA;2012] [2: [] Heinz A.,
Application of Thermal Energy Storage with Phase Change Materials
in Heating Systems, Dissertation at the Institute of thermal
engineering, Technical University Graz, 2007]
3.2 New methodology for dimensioning of electrical installation
equipment
In this methodology the estimation of the electrical energy
demand, the electrical energy generation and the installed power to
dimension the required electrical equipment (e.g. transformers and
medium voltage lines) for the different quarters (1-18) in the
Reininghaus area are shown. The dimensioning and selection of the
electrical equipment is done by conventionally coincidence factors
[[endnoteRef:3]], [[endnoteRef:4]] and within probabilistic
coincidence factors and these results in various maximum power
demands. [3: [] TAEV, „Technische Anschlussbedingungen für den
Anschluss an öffentliche Versorgungsnetze mit Betriebsspannungen
bis 1000 Volt“,“ Österreichs Energie, Wien, 2012.] [4: [] DIN VDE
0100-100, Errichten von Niederspannungsanlagen, 2009.]
Usually the dimensioning of electrical installation equipment
e.g. LV and MV voltage power lines, transformers and protection
devices is based on the total sum of the electric power of loads
multiplied by coincidence factors which consider the simultaneity
of the use of electric appliances. This procedure often leads to a
relevant overdimensioning of the electrical installation equipment
and therefore to high costs. The overdimensioning caused by the
conventional approach can be avoided by a new method using
utilisation factors derived by a probabilistic method where the
different groups (office, medium scaled industry, household and
industry) of loads and generators in the Reininghaus area are
observed.
3.3 Total Energy System with Process Network Synthesis (PNS)
Process Network Synthesis (PNS) is a method to optimise systems
of material- and energy flows. Methodical background is the p-graph
method using combinatorial rules [[endnoteRef:5]]. For urban and
regional planning the software tool PNS Studio is used to find
sustainable technology systems [[endnoteRef:6]]. [5: [] Friedler,
F., Varga, J. B., Feher, E., Fan L. T., 1996. Combinatorially
Accelerated Branch-and-Bound Method for Solving the MIP Model of
Process Network Synthesis, Nonconvex Optimization and Its
Applications, Computational Methods and Applications. Floudas,
C.A., Pardalos, P.M. (Eds.). Kluwer Academic Publishers, Dordrecht.
State of the Art in Global Optimization, Nonconvex Optimization and
Its Applications, Volume 7, pp. 609-626. doi:
http://dx.doi.org/10.1007/978-1-4613-3437-8_35, url:
http://link.springer.com/chapter/10.1007%2F978-1-4613-3437-8_35,
ISBN: 0-7923-4351-4.] [6: [] Narodoslawsky, M., Niederl, A.,
Halasz, L., 2008. Utilising renewable resources economically: new
challenges and chances for process development. Journal of Cleaner
Production, 16, 2, 164-170. ]
Starting point of a PNS analysis is to set up a maximum
structure. Hereby all available raw materials and resources
(including waste heat flows) can be defined as well as the
technology network which can convert them either to intermediates
which can be used in other processes or to products which can be
sold on the market. Capacities of technologies as well as
availability, amount and quality structure of materials are
user-defined. Moreover time bound availabilities of resources, the
specific demand of products, mass- and energy flows, investment and
operating costs of the whole infrastructure, cost of raw materials,
transport and selling prices for products must be defined.
Result of the PNS is the output of a maximum structure. The
method is carried out with PNS Studio [[endnoteRef:7]]. The
programme creates an optimum structure which contains an optimum
technology network. For this application the generation of the
economically most feasible technology network is in the centre of
consideration by setting the revenue for the whole system as target
value. [7: [] Friedler, F., Tarjan, K., Huang, Y.W., Fan, L.T.,
Varga, J.B., Feher, E., 2011. P-graph: p-graph.com/pnsstudio, PNS
Software Version 3.0.4. www.p-graph.com, last accessed on
21/08/2014.]
3.4 Energetic Longterm Assessment of Settlement Structures
(ELAS)
The ELAS (Energetic Longterm Assessment of Settlement
Structures) calculator was developed to analyse urban structures
ranging from single houses to whole settlement structures regarding
to their energy situation [[endnoteRef:8]]. evaluation of existing
households, buildings or settlements as well as planned projects
(new buildings, demolition, renovation, enlargement), predefined
values as default values, estimate future developments [8: [] ELAS
calculator: Energetic Longterm Assessment of Settlement Structures,
2011, www.elas-calculator.eu, last accessed on 27/08/2014.]
Core of the calculator is a fundamental data research about
site-specific data containing matters like energy consumption and
supply in relation to number of residents, mobility and distances
between different locations concerning type of usage, influence of
lifestyles, lifecycle of buildings, living space, type of energy
resources, road and waste facilities and energy cost.
Results of the ELAS-calculator contain energy demand, ecological
footprint (Sustainable Process Index – SPI), CO2 life cycle
emissions and regional economic impact (turn over, value added,
imports, jobs) of the user defined settlement. This information
gives municipalities a base for sustainable energy supply and
appropriate policy decisions or privates an impression about
individual energy consumption and its economic and ecological
effects.
5. Discussion
Relating to the planned building structures energy demand and
energy supply potentials of the quarter Reininghaus were
calculated. To create a basis for exact quarter development the
energy demand was calculated by the institutes as thermal and
electrical energy demand. These demands were then used in the
calculations finding an optimum total energy system.
5.1 Energy demand and specific energy supply solutions
Calculation of thermal energy characteristics
The climatic boundary conditions have a strong influence on the
heating load and the energy demand of a building. The buildings,
used in the simulations are assumed to be sited in Graz, whereby a
climate dataset based on hourly values, generated with METENORM
6.1.0.9 [[endnoteRef:9]], is used. The design ambient temperature
for the calculation of the heat load of buildings in Graz is
-12 °C. The interior room temperature was defined with
22 °C for the heating demand and 26 °C for the cooling
demand. [9: [] Meteotest. Meteonorm 6.1.0.9. Global Meteorological
Database for Engineers, Planner und Educations. Software and Data
on CD-Rom, Meteotest, Bern, Switzerland, 2009]
The simulations are performed for a building stock representing
two different levels of heat protection (low energy building (LE)
and passive house building (PH)). The buildings are designed
depending on the OIB guideline 6 on a national level defined
minimum level of heat protection of buildings [[endnoteRef:10]].
Due to these requirements for the LE the heat transfer coefficient
(U value) for the external wall is 0.35 W/m²K, for the ground
area is 0.40 W/m²K, for the ceiling area is 0.21 W/m²K
and for the windows is 1.4 W/m²K. For the PH the U value for
the external wall, for the ground area and for the ceiling is
0.15 W/m²K and for the windows is 0.8 W/m²K. The DHW
(domestic hot water) demand was defined depending on the SIA fact
sheet 2024 [[endnoteRef:11]]. The DHW demand for the office space
amounts 6 kWh/m²a. [10: [] Österreichisches Institut für
Bautechnik, OIB-330.6-094/11, OIB Richtlinie 6, Energieeinsparung
und Wärmeschutz, OIB Richtlinie 6 Ausgabe Oktober 2011] [11: []
SIA. Merkblatt 2024, Standard-Nutzungsbedingungen für die Energie-
und Gebäudetechnik, schweizerischer ingenieur- und
architektenverein, Ausgabe 2006]
Cooling
Low Energy building stock
DHW
Heating
Passive house building stock
Cooling
DHW
Heating
Figure 4: DHW, Heat and Cooling Power for LE and PH, Quarter 1
& 4a
Figure 4 shows the hourly data for the power demand for one year
for domestic hot water (DHW), heating and cooling for the two
different building concepts “low energy” and “passive house” as
explained in chapter 3.3.
The figure shows that the difference of the two building
concepts (level of heat protection and heat recovery in the
ventilation system) leads to substantial differences in the thermal
demand of the investigated building stock. On the basis of a higher
insulation standard the annual heating demand significantly
decreases from 7,502 MWh/a to 1,655 MWh/a. But on the
other hand the annual cooling demand increases from 589 MWh/a
to 1,370 MWh/a), as well as the length of the cooling
season.
Concurrently to the demand the needed power for the investigated
building stock is substantial different. The maximum occurring
power for heating in the low energy scenario is 4,729 kW and
1,788 kW in the passive house scenario. The maximum occurring
power for cooling is 886 kW in the low energy scenario and
1,053 kW in the passive house scenario.
Based on the gross floor area of 105,895 m² the maximum
power for heating achieves 44.7 W/m² (for cooling
8.4 W/m²) in the low energy scenario and 16.9 W/m² (for
cooling 10.0 W/m²) in the passive house scenario.
The annual energy demand for domestic hot water (DHW) for the
investigated building stock (Quarters 1 and 4a) is
1,002 MWh/a. The maximum occurring power for DHW reaches a
value of 637 kW. These figures are not affected by the
building concepts and therefore the same in both concepts. Based on
the gross floor area of 105,895 m² the maximum power for DHW
achieves 6.0 W/m².
Calculation of electrical load characteristics
The estimation of the electrical energy demand for the
individual groups (office, medium scaled industry, household and
industry) which are used in the project ECR can be done with
specific surface energies or state of the art load profiles
(household H0, medium scaled industry G0-G7) [[endnoteRef:12]]. The
existing load profiles for different groups (e.g. household,
bakery, supermarket) are estimated by an in-dept analysis. The used
profiles differ between working day, Saturday, Sunday and varying
for winter, summer and the transition period [[endnoteRef:13]] and
describe the collective electrical behaviour of each individual
group for a whole year. [12: [] Energie-Control, 2011. Zählwerte,
Datenformate und standardisierte Lastprofile, Sonstige Marktregeln
Strom, Österreich.] [13: [] Schieferdecker, B., 1999.
Repräsentative VDEW-Lastprofile, VDEW Materials. Frankfurt.]
Especially in Graz-Reininghaus the area is dominated by
households and so the electrical load profile of residential
households in an urban area have been measured by smart meters and
analysed using statistical methods. Resulting from these
measurements are power density functions for weekday, Saturday,
Sunday for winter, summer and transition period [[endnoteRef:14]]
which lead to new probabilistic coincidence factors
[[endnoteRef:15]]. [14: [] Reiter, M., 2014. Probabilistische
Auslastungsanalyse einer Verteilnetzstruktur auf Basis
statistischer Auswertungen von realen Smart-Meter-Messdaten,
Institut für Elektrische Anlagen, Technische Universität Graz.]
[15: [] Wieland, T., Reiter, M., E. Schmautzer, E., Fickert, L.,
2014. Gleichzeitigkeitsfaktoren in der elektrischen
Energieversorgung – Konventioneller & probabilistischer Ansatz.
Springer.]
Calculation of generation characteristics of PV
The generation profiles for the photovoltaic power plants are
based on long-term global irradiance measurements within a
15-minute time-step resolution for the Reininghaus area in Graz
[[endnoteRef:16]]. The dependence of PV generation output power PPV
within a 15-minute time step resolution is shown by the following
equation (1) [[endnoteRef:17]]. [16: [] Meteotest. Meteonorm
6.1.0.9. Global Meteorological Database for Engineers, Planner und
Educations. Software and Data on CD-Rom, Meteotest, Bern,
Switzerland, 2009.] [17: [] Schubert, G., 2012. Modellierung der
stündlichen Photovoltaik- und Windstromeinspeisung in Europa,“ in
12. Symposium Energieinnovation, Graz/Austria.]
(1)
The output power PPV is highly dependent on the PV area AMod,
the global radiation GMod, the ambient temperature T, the mounting
angle γE and the azimuth angle αE of the PV panels.
In this project the following three different scenarios for the
photovoltaic generation are investigated:
· no photovoltaic generation
· moderate photovoltaic generation (~7 % of rooftop surface
used)
· intensive photovoltaic generation (33 % of rooftop
surface and 60 % of the facade area (south, east, west, north)
excluding windows (30 %) used)
The photovoltaic profiles include the different orientations αE
(south, east, west and north) and the different angles γE of the PV
panels (90 (intensive) or 35° (moderate)).
Resulting electric load, generation and energy balance
The primary analysis area of Graz-Reininghaus consists of
various quarters (1-18) which includes building topologies and
individual groups (office, medium scaled industry, household and
industry). Figure 5 shows the detailed investigation for the load
and the generation units (PV intensive and moderate) for the groups
1 and 4a.
Figure 5: Annually produced and consumed energy (A), peak value
of load profile (B), installed electric generation power (C) for
the groups 1 and 4a
As shown in Figure 1 the annual energy (A) of the moderate PV as
well as the intensive PV is to not high enough to supply the
electrical load of group 1 and 4a. The solar coverage factor for
quarter 1 and 4a for intensive PV is about 65 % and 74 %
and shows how much energy can be met by the photovoltaic system,
without taking the time dependency into account.
The peak power shown in Figure 1 (electric power, load profile
(B)) of the intensive PV quarter 1 and 4a can nearly supply the
load of the quarter 1 and 4a but the installed electric generation
power is about 1,95 respectively 1,98 times higher than the peak
power of the load.
The factor between the installed electric generation power (kWp)
for the intensive PV (C) is about 44 % compared to the peak
value of the load profile. This is due to the modelling of the
different orientations (east, west, south, north) of the
facade.
To take the time dependency into account the residual power pRES
between the source (photovoltaic power pPV) and the load (load
PLoad quarter 1, 4a) for each time step Δt has to be calculated,
which is shown in equation (2) [[endnoteRef:18]]: [18: [] T.
Wieland, E. Schmautzer, B. Domenik und L. Fickert, „Optimal sizing
of electric and thermal energy storage units for residential
households with decentralized generation units in the low voltage
grid,“ in Electric Power Quality and Supply Reliability Conference
(PQ 2014), Rakvere/Estonia, 2014.]
(2)
A positive residual power (pRES > 0) can be stored
in an electrical storage unit or will be fed-back into the
electrical grid. If the electrical power (pRES < 0) is
negative, the stored energy from the electrical storage unit can be
used to supply the electrical load. Without an electrical storage
unit the electric grid has to supply the electric load [9]. Only by
the calculation of the residual power the degree of autonomy and
the degree of own-consumption
The degree of autonomy calculated for each time step Δt by the
residual load can be balanced over a period of time (e.g.
1/4 h, day, week, month, season, year) and shows how much
energy can be provided by the photovoltaic power plant to supply
the electric load. The degree of autonomy for the intensive
PV/moderate PV is for quarter 1 (42 % / 5 %) and for
quarter 4a (43 % / 6 %).
The degree of own consumption shows how much energy of the
photovoltaic plant is used by the load. The results for the
intensive PV/moderate PV is for quarter 1 (65 % / 100 %)
and for quarter 4a (59 % / 100 %). The degree of autonomy
as well as the degree of own-consumption can be increased by the
usage of electric storage units [9] significantly.
Maximum Power demand of load and the generation units
The probabilistic coincidence factors can be used to determine
the power demand of the individual groups (office, medium scaled
industry, household and industry) for the whole area of
Reininghaus. The results of the conventional approach (TAEV [1],
VDE [2]) and the probabilistic approach (99.99 % quantile) of
the calculated rated power for quarter 1, 4a and for the whole area
of Reininghaus are shown in Table 2.
Table 2: Maximum power demand (conventional and probabilistic
approach) for quarters 1 and 4a and for Reininghaus
maximum power demand
load (conventional approach)
load (new probabilistic approach, 99,99 % quantile)
intensive generation (photovoltaic)
[MW]
[MW]
[MWp]
Quarter 1
2.3
1.3
2.8
Quarter 4a
1.3
0.7
2.0
Total Reininghaus
22.4
12.5
33.2
The maximum power demand of the quarters 1 and 4a which are
shown in Table 1 can be used for the selection of the transformers
and the dimensioning of the cross-section of the medium and low
voltage lines as well as for the protective devices.
5.2 Future prospects: Power to heat
With an increasing share of electricity generated by wind power
plants and photovoltaic installations, there is a need to include
these sources into the load management of the electricity grid. The
conversion of electricity (power) into heat is an easy technology
to be integrated into the grids as soon as electricity is cheap and
available in excess. It can be used for load management and also
for energy storage. Power to heat installations can easily and
quickly be switched on and off.
Figure 8: Consumption (coloured area) and production (line) of
wind energy in the Austrian province Burgenland in August 2014
showing times of high overproduction [[endnoteRef:19]] [19: [] Net
Burgenland: http://www.netzburgenland.at/ from Sept. 2, last
accessed on 02/09/2014]
There are two possibilities to convert power into heat: electric
resistance heaters and compression heat pumps driven with electric
motors.
An electrically powered boiler is a cheap installation but
inefficient, if discussed from the viewpoint of thermodynamics.
While the energetic efficiency is quite good, the exergetic
efficiency is lousy. This is the reason why this technology has a
bad image so far. It has been used so far mainly on a small scale
(single apartments) in order to make use of cheap electricity at
night. Now more and more large installations of several MW are in
operation, especially in regions with a high amount of wind energy.
The temperature for the storage system can be very high, since
there are almost no limitations.
Compression heat pumps driven by electric motors offer a much
higher 2nd law efficiency but are more expensive and slower in
reaction to load variations. They should be used if the periods of
cheap electricity are longer and the temperatures required on the
storage side are low.
Both systems are not reversible, although a heat pump could
theoretically be used as an Organic Rankine Cycle power plant if
operated in reverse.
5.2 Total energy system
With Process Network Synthesis (PNS) a maximum structure was
generated. This maximum structure contains a variety of possible
technologies which can provide energy needed. In each of the
quarters of the case study area fossil gas driven CHP units and gas
furnaces, solarthermal plants, heat pumps with or without
integration of waste heat, photovoltaic power plants and air
conditioner can provide heat, domestic hot water, cooling energy
and electricity needed. This energy can either be provided directly
at the quarters (decentral technologies) or by big central supply
technologies (central technologies) as shown in Figure 6.
Figure 6: Maximum Structure, central and decentral
technologies
Providing the quarters 1 and 4a with energy an optimum
technology network was created by Process Network Synthesis. The
following Figure 7 shows first PNS results of a basic optimum
structure of the total energy system.
Figure 7: Optimum Structure for quarters Q1 and Q4a only
The technologies in the optimum structure for the quarters 1 and
4a only are heatpumps, solarthermal installations, cold energy from
malthouse Stamag and heat from the existing district heat supplier.
Located at the malthouse are cooling basins which have potential
water deep wells with a temperature level around 10°C. All new
technologies are suggested to be installed decentrally. The status
of this optimum structure will be further discussed in scenarios
because the economic data must be justified to withstand further
use. Around 9,000 MWh/a shall be supplied by existing district heat
net to cover the energy needed for heating demands. Additionally
242 MWh/a could be provided by solarthermal installations on the
roofs of the planned buildings. Cooling can be satisfied for the
quarters with 774 MWh/a cold energy coming from Stamag deep well.
Using heatpumps directly at the quarters 1 and 4a the energy demand
of 7,503 MWh for heating, 1,003 MWh for hot water and 589 MWh for
cooling can be covered by the described energy system. Electricity
demand of new constructed energy supply units is considered in the
optimum structure, whereas electricity demand of the buildings is
not considered. Setting required flows of electricity to fully
supply the buildings with electricity the demand will be covered
with combined heat and power (CHP) and photovoltaic (PV) units.
Afterwards the results of the PNS scenario were entered into the
ELAS calculator (Energetic Longterm Assessment of Settlement
Structures). Together with site-specific data about the case study
districts the following socioeconomical and ecological results
could be identified.
Figure 7: Energetic Longterm Assessment of Settlement Structures
(ELAS) of quarters 1 and 4a
In the results of the ELAS calculator different categories are
listed. One number is the energy consumption which summarises the
total energy demand of the quarters 1 and 4a to an amount of around
19,085 MWh. The ecological footprint and CO2 life cycle emissions
for a supply with the existing district heat system would be very
high because 90 % of the district heat comes from fossil resources.
That shows that from an ecological point of view this basic optimum
technology network is not ecologically optimal. The development of
additional employment of the quarters can develop far more
independently. The ecological footprint could be reduced
drastically with each renewable technology replacing existing
fossil fuels.
5.3 Synthesis
After the full project time the outcome of a multi-layered
analysis of the interdisciplinary group will provide a useful
optimum energy technology network and a catalogue of measurements
for a smart energy development of the city district. Continuing
feedback circles between the departments and stakeholders shall
further make it possible to create scenarios to provide the city,
stakeholders and the public with relevant information for smart
energy planning in the city. The project team understands this work
as a helpful tool open for the planning and it is open for
discussion about further technology development and integration of
smart technology solutions which can be parts of the technology
system in future. The last example given in the future prospects
part shows how such a system could look like in using power
overproduction to fill heat production gaps.
6. Conclusions
Basically urban structures can change quickly but in case of a
more or less empty strip of land also an inhomogeneous development
on different places of construction in different periods of time
leads to difficulties in defining optimum energy systems to
guarantee a smart energy supply also for changing urban density.
Parallel scenarios about energy systems for the total area as well
as energy supply for specific quarters shall improve the
possibility to find optimal pathways to supply city districts as
smart and sustainable as possible. Process oriented work in
progress during the project reveals and still is revealing pathways
which can be adopted and the consideration of the ecological,
social and economic chain which goes along with is long and
considerably tricky to handle. Ultimately actions of this and
further smart city quarter developments can draw on experiences
which are freshly made by an as far unique combination of an
interdisciplinary workflow.
Acknowledgements
The team of ECR Reininghaus wants to thank all funding partners,
project partners and experts for their support during the project
time. The research project ECR Energy City Graz Reininghaus is
funded by the City of Graz, the Federal State of Styria and the
Programme “Building of Tomorrow” of the Austrian Federal Ministry
of Transport Innovation and Technology (BMVIT) via the Austrian
Research Promotion Agency (FFG).
References
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Power [kW]Time [h]
DHW demandHeating demandCooling demand
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Power [kW]Time [h]
DHW demandHeating demandCooling demand