SPATIAL BUILDING STOCK MODELLING TO ASSESS ENERGY- EFFICIENCY AND RENEWABLE ENERGY IN AN URBAN CONTEXT

Transcription

1 SPATIAL BUILDING STOCK MODELLING TO ASSESS ENERGY- EFFICIENCY AND RENEWABLE ENERGY IN AN URBAN CONTEXT M. Jakob 1 ; H. Wallbaum 2 ; G.Catenazzi 1, G. Martius 1, C. Nägeli 1, B. Sunarjo 1 1: TEP Energy GmbH, Zurich, Switzerland 2: Chalmers University of Technology, Gothenburg, Sweden ABSTRACT The building stock has a decisive impact on energy consumption, greenhouse gas (GHG) emissions and sustainable development of urban areas. In previous studies the authors have assessed these impacts by applying a bottom up building stock model (BSM) including construction components and energy systems for different development scenarios. The goal of this work is to present novel improvements of the BSM which include a spatial differentiation of building characteristics, energy infrastructure and local potentials of renewable energy. As such the model is suitable to be used in the context of urban energy planning and to simulate different scenarios, for instance with regard to the feasibility of long term carbon mitigation and (primary) energy consumption reduction goals. To simulate energy demand, carbon emissions and further key indicators of the building stock the bottom-up simulation methodology has been enhanced in terms of spatial differentiation. So far, to deal with the great complexity of building stock modelling, different building types of an urban context have been clustered into cohorts with similar characteristics. As compared to these earlier approaches, we combine specific data known on the level of each individual building with generic data that is known or assumed on the level of building or spatial cohorts. These cohorts are defined by variables such as construction period, building type and utilisation, and building location. However, model calculations are done on the level of specific building archetypes, building technologies, and building components. As such assumptions on technical characteristics and building stock alterations may be brought much closer to the decision processes. This new approach of spatial building stock modelling (SBSM) offers the advantage of being able to include specific, geo-referenced building data (e.g. from surveys or from building registers) and to represent results at the level of details needed. Hence, results may be represented by building type or other building attributes, but also by hectare or neighbourhoods, using geographical information systems (GIS). Scientific results are achieved both with regard to methodology and with regard to content. Methodologically we demonstrate the feasibility of combining data and assumptions on different levels of aggregation such as building elements, individual buildings and cohorts. With regard to content we present results from a case study about feasibility of achieving the goals of the 2000-Watt- and 1-ton-CO 2 -society in the city of Zurich, referring to spatially differentiated potentials of renewable energy and energy-efficiency. Keywords: building stock, spatial simulation modelling, geographical information system (GIS), energy-efficiency, renewable energy, urban context INTRODUCTION The building stock is one of the largest energy consumer and emitter of greenhouse gases (GHG) in urban spaces. Even though buildings newly constructed in recent years have been using less and less energy, the average consumption of the building stock remains high as retrofitting rates of existing buildings remain low. Thus also potentials of renewable energies CISBAT September 4-6, Lausanne, Switzerland 1047

2 should be taken into consideration to reach ambitious GHG mitigation goals. Yet, especially for urban areas tapping energy-efficiency and renewable energy potentials entail specific challenges. Models are adequate tools to improve the understanding of the development of the energy consumption of the building stock in different scenarios. Several different methods of building stock modelling have been developed so far. Swan and Ugursal [1] and Kavgic et al. [2] give a general overview of the different modelling techniques. Especially bottom-up models have become more sophisticated with a trend towards spatially differentiated models related to geographical information systems (GIS). Markus et al. [3] use spatial data from a land use model in order to model the development of the energy demand distribution of city regions. A similar method is used by Howard et al. [4], who assess the current energy demand of New York by applying the results of a statistical regression model to the floor area distribution if the city. Biberacher et al. [5] combine spatially differentiated supply and demand modelling by optimizing local energy potentials with heat demand based on costs, efficiency and CO 2 -emissions. In previous studies the authors of this paper have assessed the impacts of the building stock on final energy consumption and GHG-emissions by simulating different scenarios with a bottom-up building stock model (BSM) that explicitly includes construction components, building technologies and energy systems [6-9]. The goal of this work is to present a further development of the BSM using a new approach of spatial building stock modelling with results from a case study about feasibility of achieving the goals of the 2000-Watt- and 1 ton CO 2 -per-capita-society in the city of Zurich. MODELLING CONCEPT The bottom-up simulation methodology previously developed by the authors [6-9] in order to simulate energy demand, carbon emissions and further key indicators of the building stock is extended in terms of spatial differentiation. As such local heating (and potentially cooling) energy demand can be compared to locally bounded renewable energy potentials or current and future energy network infrastructure. With this modelling principle of matching locally demand and potentials or supply it becomes possible to assess energy policy scenarios in an urban context much more realistically. The model adopts a cohort approach that aggregates data of individual buildings that are similar by building (construction) type, construction period, type of use (e.g. which mix of economic sectors within a building type) and location (right-pointing arrow in Figure 1). Thus, these cohorts are spatially differentiated in order to enable the modelling of spatially differentiated energy demand. Demand evolution is simulated using construction rates and retrofit measures, which differ across the city depending on urban development strategies. The demand is covered by spatially differentiated energy carrier mixes depending on the available supply infrastructure, renewable energy potentials and development strategies. In order to deal with the great complexity and heterogeneity of the building stock, different building types of an urban context are clustered into cohorts with similar characteristics. As compared to the earlier approaches, we combine specific data known on the level of each individual building (from geo-referenced building registers) with generic data that is known or assumed on the level of building or spatial cohorts (right-pointing arrow in Figure 1). The heating and hot water heating demand is calculated for all cohorts based on the official Swiss norm SIA 380/1 (EN ISO 13790) [10]. The distribution of different heating systems, building technologies and appliances is modelled based on assumed diffusion rates, which depend on the different scenarios and are differentiated for all cohorts. Based on the building characteristics of each cohort and the assumed mix of heating systems, the model calculates 1048 CISBAT September 4-6, Lausanne, Switzerland

3 the final energy demand for all cohorts for each time step up to Based on the emission and primary energy factors (PEF) which, in the case of electricity and district heating, depend on the break-down of primary energy input the model then calculates greenhouse gas (GHG) emissions and primary energy use. In a final step, cohort-specific results are allocated to the individual buildings of the georeferenced building register in order to represent results in a geographical information system. The interface between the BSM and the GIS (left-pointing arrow in Figure 1) refers to attributes that uniquely identify the different cohorts of the BSM which are building type, construction period, zone, status of patrimonial protection, and others. Figure 1: Interaction of the building stock model (BSM) with geo-referenced data and the GIS environment IMPLEMENTATION At the current stage technical implementation is quite heterogeneous: The BSM as such is implemented in MS Excel using Macros and VBA code, the interface between the BSM and the GIS environment is implemented in MS Access, and dividing the city area into different zones is done in ArcGIS. Spatially differentiated development of heat demand The development of heat demand of the buildings is carried out for all building types and is spatially differentiated. Geo-referenced data of the building stock, which covers all building types of a city, is aggregated to cohorts in GIS based on aforementioned characteristics. Based on the local characteristics of the building stock and urban development considerations the city area is divided into different development zones. Zones can consist of the historic city core, traditional, pitoresque residential areas, reorientation areas or urban development areas. These zones differ in terms of the state of their buildings, their retrofit and new construction rates and the effect of energy efficiency measures, especially in the case of the facade (see Table 1 that exemplary show retrofit rates for two different cases and Table 2 as well as Tables 4 to 6 in [11] that illustrate how U-values of retrofitted façades are differentiated). All buildings are grouped into different retrofit types based on the building type, building period, listed building status (patrimonial protection registry) and the construction zone, which includes specific assumptions about the renewal of their envelope and the building CISBAT September 4-6, Lausanne, Switzerland 1049

4 technology (e.g. external insulation of the facade is often not possible for listed buildings and for the alternative internal insulation less insulation material can be applied resulting in a higher U-value). This results in spatially differentiated retrofit measures and efficiency potentials. The specific heat demand is then calculated for a limited number of building archetypes. Each of the cohorts is characterized by such an archetype. Archetypes are specifically defined in terms of geometry and building physics data for each of its main building envelope components (façade, windows, roof, etc). Results are then extrapolated to the whole building stock, relating resulting heating demand to the aggregated cohorts. With listed buildings (e.g. old city core area) Without listed buildings (e.g. reorientation area) Construction period Pre % 0.58% 0.56% 0.45% 0.58% 0.84% 0.81% 0.65% % 0.79% 0.76% 0.61% 0.69% 1.00% 0.96% 0.77% % 0.96% 0.88% 0.75% 1.27% 1.17% 1.07% 0.92% % 1.22% 1.06% 0.38% 0.52% 1.44% 1.24% 0.45% % 0.21% 0.33% 0.41% 0.02% 0.25% 0.39% 0.48% Table 1: Annual renewal rates [% floor area/year] for the building component Facade" for Multifamily houses in the efficiency scenario as defined in [11]. The differences between the two areas stem mainly from the high percentage of listed buildings in city core areas. (Adapted from [11]) With listed buildings (e.g. old city core area) Without listed buildings (e.g. reorientation area) Construction period Pre New buildings Table 2: U-values [W/m 2 /K] for the building component Facade" for Multifamily houses in the efficiency scenario as defined in [11. The differences between the two areas stem mainly from the high percentage of listed buildings in city core areas. (Adapted from [11]) Spatially differentiated heat supply and renewable energy potentials Additionally to zoning motivated by urban development and energy demand considerations the city area is also divided into different zones depending on the availability of renewable energy potentials suitable for heating purposes. The zones are differentiated according to the local availability of such potentials or use restrictions of thereof. Typical examples are ground water, perimeters of lakes and rivers, geothermal energy, waste water purification plants, and others. Many of these potentials require a (thermal) energy network infrastructure which implies that the existing district heating network and topological considerations (e.g. distance from lake and river, traffic infrastructure) are also influencing the zoning process CISBAT September 4-6, Lausanne, Switzerland

5 Spatially matching heat demand, supply, and renewable energy potentials Zones motivated by demand related considerations are than superposed with zones of supply infrastructure and potentials (see Abbildung 1 in [11]). This allows for spatially matching supply and demand and to appraise to which extent locally available potentials may be sufficient to cover demand. RESULTS The spatial building stock model (SBSM) was applied in a case study covering the area of Zurich-Altstetten. Using specific, geo-referenced building data (from the building registry of Zurich) using geographical information systems (GIS) it became possible to represent results at the level hectares (see Figure 3). In Zurich-Alstetten areas of high demand coincide with groundwater energy potentials. Moreover it becomes apparent that high demand areas will be shrinking in the future and the demand in general is decreased in the scenario considered. Figure 2: Exemplary results comparing the spatially distributed heat demand (space heating and warm water) of the neighbourhood of Altstetten in the city of Zürich in 2010 and CONCLUSION The new approach of spatial building stock modelling (SBSM) offers the advantage of being able to include specific, geo-referenced building and energy potential data (e.g. from surveys, building registers and geological maps), to locally match demand, existing energy infrastructure and potentials and to represent results at the level of details needed or desired. Obviously the approach and the level of disaggregation depend on the data availability of the place to be investigated. Hence, results may be represented by building type or other building related attributes such as building owner, but also by spatial attributes such as hectare, neighbourhoods, using geographical information systems (GIS) if data resolution is appropriated. The transferability of the SBSM to other cities is currently under investigation. For the case of London this is within the project Smart Urban Adapt (SUA) from which preliminary results are to be expected by the end of 2013 and for other cities within other activities of TEP Energy and Chalmers University of Technology. In parallel software implementation is being transferred to a more appropriate environment. CISBAT September 4-6, Lausanne, Switzerland 1051

6 ACKNOWLEDGEMENTS Results, underlying research and development summarized in this paper was predominantly enabled through financial contributions of the city of Zurich (represented by Energy Delegates Bruno Bébié and Felix Schmid and by Heinrich Gugerli, Municipal Building Department who, together with others, also were valuable discussion partners) and own funding of TEP Energy and partly by the Climate KIC project SUA (part of the interface between the BSM and the GIS) which we gratefully acknowledge. REFERENCES 1. Swan L.G., Ugursal V.I. (2009). Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renewable and Sustainable Energy Reviews. 13 (2009) Kavgic M., Mavrogianni A., Mumovic D., Summerfield A., Stevanovic Z., Djurovic- Petrovic M. (2010). A review of bottom-up building stock models for energy consumption in the residential sector. Building and Environment. 45 (2010) Markus P., Avci N., Girard S., Keim C., Peter M. (2009). Energy demand in city regions methods to model dynamics of spatial energy consumption. ECEEE 2009 Summer Study. (2009) Howard B., Parshall L., Thompson J., Hammer S., Dickinson J., Modi V. (2012) Spatial distribution of urban building energy consumption by end use. Energy and Buildings. 45 (2012) Biberacher M. et al. (2010). Räumliche Modelle als Entscheidungsgrundlage für die Inwertsetzung regional verfügbarer Energiepotentiale zur CO 2 -neutralen Deckung des lokalen Wärmebedarfs. 56 (2010). 6. Wallbaum H., Heeren N., Jakob M., Gabathuler, M. Gross N., Martius G. (2009). Gebäudeparkmodell SIA Effizienzpfad Energie Dienstleistungs- und Wohngebäude Vorstudie zum Gebäudeparkmodell Schweiz Grundlagen zur Überarbeitung des SIA Effizienzpfades Energie. ETH Zürich, TEP Energy i.a. Bundesamt für Energie, Bern, September. 7. Wallbaum H., Jakob M., Heeren N., Gross N., Martius G. (2010). Gebäudeparkmodell Büro-, Schul- und Wohngebäude Vorstudie zur Erreichbarkeit der Ziele der 2000-Watt- Gesellschaft für den Gebäudepark der Stadt Zürich. ETH Zürich und TEP Energy i.a. Stadt Zürich, Amt für Hochbauten, Fachstelle nachhaltiges Bauen, Zürich, Mai. 8. Heeren, N., Wallbaum, H., Jakob, M. (2012). Towards a 2000 Watt society assessing buildings specific saving potentials of the Swiss residential building stock. International Journal of Sustainable Building Technology and Urban Development, May 2012: Heeren N., Jakob, M., Martius, G., Gross, N., Wallbaum, H. (2013). A component based bottom-up building stock model for comprehensive environmental impact assessment and target control. Renewable and Sustainable Energy Reviews, 20 (April 2013): SIA (2009). SIA 380/1:2009 Thermische Energie im Hochbau. Swiss society of engineers and architects, Zürich 11. Jakob M., Flury K., Gross N., Martius G., Sunarjo B., Bébié B., Heeren N., Wallbaum H. (2012 ). Energiekonzept 2050 für die Stadt Zürich - Auf dem Weg zur 2000 Watt tauglichen Wärme-Versorgung mit einem räumlich differenzierten Gebäudeparkmodell. 17. Status-Seminar «Forschen für den Bau im Kontext von Energie und Umwelt». 13./14. September 2012, ETH-Zürich 1052 CISBAT September 4-6, Lausanne, Switzerland