Smart Urban Energy Development Graz-Reininghaus

Stephan Maier, Institute of Process and Particle Engineering, Inffeldgasse 13/III, stephan.maier@tugraz.at

Ernst Rainer, Institute of Urbanism, Rechbauerstraße 12, ernst.rainer@tugraz.at

Werner Lerch, Institute of Thermal Engineering, Inffeldgasse 25/B, werner.lerch@tugraz.at

Thomas Mach, Institute of Thermal Engineering, Inffeldgasse 25/B, thomas.mach@tugraz.at

Thomas Wieland, Institute of Electrical Power Systems, Inffeldgasse 18/1, t.wieland@tugraz.at

Michael Reiter, Institute of Electrical Power Systems, Inffeldgasse 18/1, m.reiter@tugraz.at

Ernst Schmautzer, Institute of Electrical Power Systems, Inffeldgasse 18/1, schmautzer@tugraz.at

Hans Schnitzer, Institute of Process and Particle Engineering, Inffeldgasse 13/III, hans.schnitzer@tugraz.at

Yvonne Bormes, Institute of Urbanism, Rechbauerstraße 12, bormes@tugraz.at

*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

0500100015002000250030003500400045005000017523504525670088760

Power [kW]Time [h]

DHW demandHeating demandCooling demand

0500100015002000250030003500400045005000017523504525670088760

Power [kW]Time [h]

DHW demandHeating demandCooling demand