Target setting for RES-H/C in Upper Austria D6 of WP3 from the RES-H Policy project

Transcription

1 Target setting for RES-H/C in Upper Austria D6 of WP3 from the RES-H Policy project A report prepared as part of the IEE project "Policy development for improving RES-H/C penetration in European Member States (RES-H Policy)" February 2010 Written by Christine Öhlinger Gerhard Dell Christiane Egger With contributions from Lukas Kranzl, Gustav Resch, Andreas Müller, Energy Economics Group Vienna University of Technology Mario Ragwitz ISI Supported by

2 The project "Policy development for improving RES-H/C penetration in European Member States (RES-H Policy)" is supported by the European Commission through the IEE programme (contract no. IEE/07/692/SI ). The sole responsibility for the content of this report lies with the authors. It does not represent the opinion of the European Communities. The European Commission is not responsible for any use that may be made of the information contained therein. O.Ö. Energiesparverband, February 2010

3 Content 1 Methodology Potential assessment Review of existing scenarios and literature Energy Future "EGEM Energiespar-Gemeinden" Local energy strategies Discussion of the different approaches Top-down approach The Green-X model Assessment of RES potentials in Green-X Scenario description Results for EU The share of RES-H on overall RES deployment RES-H development Green-X results for Austria The share of RES-H on overall RES deployment RES-H development Bottom-up approach General approach and methodology Solar thermal collectors buildings Biomass heating buildings Heat pumps buildings RES-H/C in the industry sector Synthesis of buildings and industry sector Conclusions from economic analysis in WP4 (optional) Comparative analysis of literature, top-down and bottom-up approaches Stakeholder discussion process Objective of the stakeholder consultation The questionnaire Target group and responses Qualitative results Synthesis of qualitative and quantitative results of the stakeholder consultation Synthesis: RES-H/C targets References Annexes Input data and assumptions for industry modeling O.Ö. Energiesparverband 3

4 List of Figures Figure 1 Methodology for deriving RES-H/C targets... 8 Figure 2 Method ology for the definition of potentials... 9 Figure 3 Biomass potentials in Upper Austria (TJ) Figure 4 Scenario "min" for use of agricultural land Figure 5 Scenario "max-fuel" for use of agricultural land Figure 6 Scenario "max-wood chips" for use of agricultural land Figure 7 BAU and ambitious scenario for the development of solar collectors in Upper Austria until Figure 8 BAU and ambitious scenario for the use of ambient heat (heat pumps) per application in Upper Austria until Figure 9 Number of heat pumps (BAU and ambitious scenario in Upper Austria until 2030) 19 Figure 10 Method of approach regarding dynamic cost-resource curves for RES (for the model Green-X) Figure 11 RES generation until 2030 in a strengthened policy scenario in EU-27 countries.. 29 Figure 12 The share of RES on gross final energy demand until 2030 in a strengthened policy scenario in EU-27 countries Figure 13 RES-H generation until 2030 in a strengthened policy scenario in EU-27 countries Figure 14 RES-H generation (s ectors) until 2030 in a strengthened policy scenario in EU-27 countries Figure 15 New installed RES-H capacity in a strengthened policy scenario in EU-27 countries Figure 16 RES generation until 2030 in a strengthened policy scenario in Austria Figure 17 The share of RES on gross final energy demand until 2030 in a strengthened policy scenario in Austria Figure 18 RES-H generation (technologies) until 2030 in a strengthened policy scenario in Austria Figure 19 RES-H generation (sectors) until 2030 in a strengthened scenario in Austria Figure 20 New installed RES-H capacity in a strengthened policy scenario in Austria Figure 21 Principle of a S-curve diffusion approach applied in the bottom-up analysis of the building sector Figure 22 Installed solar collector area in the selected bottom-up scenario for Upper Austria39 Figure 23 Solar thermal heat generation in residential buildings in the selected bottom-up scenario for Upper Austria Figure 24 Number of buildings with biomass heating systems in the selected bottom-up scenario in Upper Austria Figure 25 Biomass fuel input for heating in residential buildings in the selected bottom-up scenario in Upper Austria Figure 26 Number of residential buildings with heat pumps in the selected bottom-up scenario in Upper Austria Figure 27 Ambient heat utilization from heat pumps in residential buildings in the selected bottom-up scenario in Upper Austria Figure 28 Projection of final energy use [PJ] in industrial processes in Austria (source Figure 29 RESolve-H/C) Impact of applying series of constraints to the final energy demand in industrial processes in Austria (source RESolve-H/C) Figure 30 Energy from solar thermal collectors Upper Austria Figure 31 Biomass energy for heating excl. CHP and industry in Upper Austria Figure 32 Energy from heat pumps in Upper Austria O.Ö. Energiesparverband 4

5 List of tables Table 1 Present use of renewable energy sources for heating in Upper Austria and technical potentials Table 2: Main input sources for scenario parameters Table 3 Input data for solar thermal bottom-up analysis (Upper Austria) Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Input data for biomass heating in buildings for the bottom-up analysis in Upper Austria Input data for geothermal / heat pumps in buildings for the bottom-up analysis in Upper Austria Matching of RES-H/C technologies to temperature levels. For the modelling, only the entries displayed in bold have been considered Projection of total final energy use and renewable heating technologies in industrial processes in Austria (sources: ODYSSEE 2009, PRIMES 2007, RESolve-H/C) Contribution [%] to the final energy input in industrial processes in Austria (source RESolve-H/C) RES-H/C technologies providing final renewable heat [PJ] for industrial processes in the year 2020 in Austria (source: RESolve-H/C) Table 10 Impact of applying series of constraints to the final energy demand in industrial processes in Austria (source RESolve-H/C) Table 11 Synthesis of the bottom-up analysis in the industry and building sector (GWh) in Austria Table 12 Synthesis of the bottom-up analysis in the industry and building sector (PJ) in Austria Table 13 Basis and suggested target ranges for 2020 and 2030 for different renewable energy sources for Upper Austria O.Ö. Energiesparverband 5

6 Introduction The RES-H Policy project The project "Policy development for improving RES-H/C penetration in European Member States (RES-H Policy)" aims at assisting Member State governments in preparing for the implementation of the forthcoming Directive on Renewables as far as aspects related to renewable heating and cooling (RES-H/C) are concerned. Member States are supported in setting up national sector specific 2020/2030 RES-H/C targets. Moreover the project initiates participatory National Policy Processes in which selected policy options to support RES-H/C are qualitatively and quantitatively assessed. Based on this assessment the project develops tailor made policy options and recommendations as to how to best design a support framework for increased RES-H/C penetration in national heating and cooling markets. The target countries/regions of the project comprise Austria, Greece, Lithuania, The Netherlands, Poland and UK countries that represent a variety in regard of the framework conditions for RES-H/C. On the European level the projects assesses options for coordinating and harmonising national policy approaches. This results in common design criteria for a general EU framework for RES-H/C policies and an overview of costs and benefits of different harmonised strategies. This report The objective of this report is to provide target ranges for the main RES-H/C technologies in Upper Austria for the years 2020 and These target ranges will serve as a discussion point for the stakeholder consultation. The main outcomes of this discussion process will also be documented in this report. The report is structured into the following parts: After a short introduction into the methodology of this report (section 1), selected results from previous, existing projects, studies and scenarios (section 2) are presented. This comparison of different scenarios is supposed to provide a first insight into the ranges that have been discussed and suggested in former projects. Section 3 will present results of the simulation tool Green-X. The selected scenario is compatible with the EU 2020 targets for renewable energy and the RES directive. The result for EU 2020 as well as for Upper Austria is documented. O.Ö. Energiesparverband 6

7 In section 4, a bottom-up methodology for RES-H/C technologies is developed. Diffusion parameters are selected and the resulting bottom-up scenario is presented. (Chapter 5 will be completed during the work in the next work package: Then the economic constraints and specific policy instruments in the modelling approach will be taken into account). In section 6 the results from the literature review, the top-down approach (Green-X) and the bottom-up methodology will be compared. Chapter 7 documents the outcome of the stakeholder consultation process and Chapter 8 gives a synthesis resulting into final target ranges for RES-H/C technologies in Upper Austria. O.Ö. Energiesparverband 7

8 1 Methodology (Core elements provided by EEG, to be adapted on regional requirements) RES H/C targets Stakeholder policy process target setting Data basis and scientific ground for target setting Revised targets Policy workshops Economic modelling results Existing scenarios Top-down approach Bottom-up approach Policy assessment WP 3: RES-H/C targets WP 4: Policy options Figure 1 Methodology for deriving RES-H/C targets 1.1 Potential assessment The possible use of RES depends - among other factors - on the available resources and the associated costs. In this context, the term "available resources" or RES potential has to be clarified. In literature, potentials of various energy resources or technologies are intensively discussed. However, no common terminology is applied. In order to contribute to the comprehension of the derived data, we start with an introduction on the applied terminology: Theoretical potential: For deriving the theoretical potential general physical parameters have to be taken into account (e.g. based on the determination of the energy flow resulting from a certain energy resource within the investigated region). It represents the upper limit of what can be produced from a certain energy resource from a theoretical point-ofview of course, based on current scientific knowledge; Technical potential: If technical boundary conditions (i.e. efficiencies of conversion technologies, overall technical limitations as e.g. the available land area to install wind turbines as well as the availability of raw materi- O.Ö. Energiesparverband 8

9 als) are considered the technical potential can be derived. For most resources the technical potential must be considered in a dynamic context e.g. with increased R&D conversion technologies might be improved and, hence, the technical potential would increase Realisable potential: The realisable potential represents the maximal achievable potential assuming that all existing barriers can be overcome and all driving forces are active. Thereby, general parameters as e.g. market growth rates, planning constraints are taken into account. It is important to mention that this potential term must be seen in a dynamic context i.e. the realisable potential has to refer to a certain year; Mid-term potential: The mid-term potential is equal to the realisable potential for the year Theoretical potential Energy generation Historical deployment Technical potential Maximal time-path for penetration (Realisable Potential) Barriers (non-economic) Economic Potential (without additional support) Policy, Society 2020 R&D Mid-term potential Additional realisable mid-term - potential (up to 2020) Achieved potential (2005) Long-term potential Figure 2 Method ology for the definition of potentials Figure 2 shows the general concept of the realisable mid-term potential up to 2020, the technical and the theoretical potential. O.Ö. Energiesparverband 9

10 2 Review of existing scenarios and literature For Upper Austria, the following existing scenarios were analysed: "Energy Future 2030" scenarios which were developed in preparation of the "2030 targets" recently adopted by the Regional Government of Upper Austria. Currently, the action plan to achieve these targets is under development. "EGEM Energiespar-Gemeinden": local energy strategies developed in the frame of an Upper Austrian support programme for municipalities 2.1 Energy Future 2030 Within the frame of the energy strategy process "Energy Future 2030", four different scenarios were developed and consumption, trends and potentials analysed. In October 2007, the regional government adopted the most ambitious scenario, the "turning point scenario" as its officials targets for 2030 which include: 100 % space heating from renewable energy sources 39 % less heat demand 100 % electricity from renewable energy sources 41% less fossil transport fuels minus 65% CO 2 In order to achieve these ambitious targets, policy packages are developed and applied for the different target groups and technologies, consisting of grants, promotional activities (energy advice, information campaigns), training as well as product and company development. The following table shows present use of renewable energy sources for heating in Upper Austria as well as realistic and technical potentials: O.Ö. Energiesparverband 10

11 Heat Present RES [TJ] Total technical potential, Potential 2030, end energy [TJ] primary energy [TJ] Min Max Total technical potential, end energy [TJ] Biomass single installations 17,980 biomass industries & companies 4,170 biomass district heating 1,170 total biomass 23,320 54,900 28,000 40,000 - solar thermal hot water (households) solar thermal heating (households) 900 2,300 4,000 6,700 solar thermal (others, companies) ambient energy heat pumps 980 2,200 3,000 - geothermal energy 300 waste heat / district heat 12,435 13,500 15,000 - total heat 37,935 46,000 62,000 - Table 1 Present use of renewable energy sources for heating in Upper Austria and technical potentials Wood Biomass The biomass potential in Upper Austria which could be used sustainably comprises the following components: in Upper Austrian forests, presently more wood grows than is harvested. Additional biomass resources are the wood that is left behind when trees are harvested. Taking into account that not all of that could be used, the additional biomass potential can be assessed with about 5 PJ/a. 1 1 see also Haas et al 2001; O.Ö. Energiesparverband 11

12 The Austrian "Bundesforste" (Austria National Forests, a public company which owns 15 % of the Austrian forest land) also estimate a potential of about 700,000 fm which is 5.04 PJ. 2. The Upper Austrian "Landesforstdirektion" (regional forest administrations) states that in Upper Austrian about 400,000 fm (2.9 PJ) wood could be harvested annually for the next 20 years. After that time the amount would be half as much. The problem is to motivate owners (often with only small wood lots) to harvest their forests. The potential of short rotation wood depending on the type - can be estimated between 0.4 and 5 PJ/a. The assessment of the additional potential from straw results from different factors, for example the choice of the plants, other non-energetic uses etc. with higher straw percentage at about 3,2 PJ/a (see Dissemond 1994; Steinmüller, Pollak 1997; Schmidt, Hantsch-Linhart 1990; Pichl 1999; Winkler-Rieder 1993). In total, the Austrian saw mill industry produces about 5.4 mio fm saw mill residues which are mainly used in wood pulp and paper industries. Theoretically these residues would be available for energetic use also 3. The amount for Upper Austria would be about 10 PJ/a. Taking into account an annual increase of the saw mill industry of about 0.7% [Schwarzbauer 1996] and in case the same share of saw mill residues as now will be used, an additional potential of about 1.2 PJ/a can be estimated. In case, the total amount of wood, which is produced by the Austrian saw mill industries and which is not exported, together with products from the particle board industries is regarded as waste wood, the potential would be much higher. Estimated that 80% of the construction wood 4, of the particle boards and of wood used for wrapping and 50% of the furniture wood 5, could be used for energetic purposes and taking into account that about 1.13 fm waste wood are already used [Obernberger 1998], the potential would be additional 9 PJ/a in Upper Austria. However, as these are very rough estimations, therefore the potential from literature will be used. 2 letter of Thomas Uher (ÖBF) to the regional minister for energy (22 December 2004) 3 instead of present material use; 4 presently 3.24 Mio fm wood from construction sites; 5 presently 1.6 Mio fm wood for wrapping and furniture; O.Ö. Energiesparverband 12

13 The additional potential of waste wood (increased use of waste wood from construction sites and from household waste and can be estimated at about 0.5 PJ. The estimations undertaken did not foresee that biomass which is presently used for non-energetic purposes or is exported would be transferred to energetic use. In total, the additional potential for solid biomass amounts to about 15 PJ/a, taking into account saw mill residues that are presently used for non-energetic purposes and increased use of waste wood up to 30 PJ/a. Ethanol Bio-diesel Biogas (electr.) (sweet corn) Short rotation wood Straw Waste wood Industrial wood residues Optimised cascading biomass utilisation Competition with current industrial (material) utilisation Forestal biomass 0 5,000 10,000 15,000 20,000 25,000 Biomass potentials in Upper Austria (TJ) Current utilisation Additional potential (min) Additional potential (max) Figure 3 Biomass potentials in Upper Austria (TJ) Use of agricultural land by 2020 for energetic purposes Agricultural land could be used for energetic purposes and different renewable energy sources would compete for land with material use of renewables. The following 3 scenarios show, how the development of the energetic and material use of agricultural land by 2020 could be. 10 Scenario min: o 10% of the agricultural land by 2020 are used for non-food purposes (materials and energetic purposes) O.Ö. Energiesparverband 13

14 o The land use is split as follows: 10% short rotation wood (wood chips) 25% maize (biogas) 10% rapeseed (biodiesel) 15% corn (Ethanol) 15% materials produced from renewable sources 25% no use of land Energy supply (TJ/a) 1,600 1,400 1,200 1, Short rotation wood Biodiesel Ethanol Electricity (biogas) Heat (biogas) Figure 4 Scenario "min" for use of agricultural land Scenario max-fuel: o 15% of the agricultural land by 2020 are used for material and energetic purposes o The land use is split as follows: 5% short rotation wood (wood chips) 10% maize (biogas) 15% rapeseed (biodiesel) 30% corn (Ethanol) 15% materials produced from renewable sources 25% no use of land O.Ö. Energiesparverband 14

15 1,800 1,600 1,400 Energy supply (TJ/a) 1,200 1, Short rotation woods Biodiesel Ethanol Electricity (biogas) Heat (biogas) Figure 5 Scenario "max-fuel" for use of agricultural land The total production of biofuel by 2020 amounts in this scenario to 904 TJ. This is less than 1.5% of the energy consumption in the transport sector by 2002 (62,000 TJ). Scenario "max-wood chips": o 15% of the agricultural land by 2020 are used for material and energetic purposes o The land use is split as follows: 30% short rotation wood (wood chips) 10% maize (biogas) 5% rapeseed (biodiesel) 15% corn (Ethanol) 15% materials produced from renewable sources 25% no use of land Although only 40% of the land is used for short rotation forests, the energetic share of short rotation wood chips is over 80%. The reason is that there are less losses by short rotation wood chips than with other technologies. O.Ö. Energiesparverband 15

16 3,500 3,000 Energy supply (TJ/a) 2,500 2,000 1,500 1, Short rotation woods Biodiesel Ethanol Electricity (biogas) Heat (biogas) Figure 6 Scenario "max-wood chips" for use of agricultural land Depending on the price situation, import of biomass raw material from regions outside Upper Austria is possible. Solar thermal The technical potential for solar thermal results on the one hand from the available roof area and on the other hand from storage technologies and the characteristics of the heat demand. It makes more sense - instead of calculating this theoretical potential - to state ambitious scenarios of potentials that can be realistically achieved. Such scenarios are presented in the following graphs which show that by 2030 about PJ could be produced from from solar thermal. In the maximum scenario, more than half of the usable roof area is utilized. This results in about 5.3 mio. square metres (assumption: 3 m 2 per housing unit in multi-family homes and 20 m 2 per housing unit in one-family homes). The technical potential that could be achieved by using this total area is about 6.7 PJ. [Dell 2005, Faninger, own analysis] O.Ö. Energiesparverband 16

17 SCENARIOS SOLAR COLLECTORS m² TJ SCENARIOS SOLAR COLLECTORS Past BAU Ambitious Past BAU Ambitious SOLAR COLLECTORS BAU 5000 SOLAR COLLCTORS AMBITIOUS TJ TJ One-fam. TJ Publ. build. TJ More-fam. TJ Swimming pool TJ One-fam. TJ Publ. Build. TJ More-fam.TJ Swimming pool TJ Figure 7 BAU and ambitious scenario for the development of solar collectors in Upper Austria until 2030 Ambient heat / heat pump The total technical potential of ambient heat equals the supply of the total heat market with heat pumps. Similar to solar thermal, scenarios to estimate the feasible potential are used. Thereby, PJ of ambient heat could be used until This refers to about 60,000 70,000 heat pumps in Upper Austria. O.Ö. Energiesparverband 17

18 3500 AMBIENT HEAT SCENARIOS TJ Past TJ BAU TJ Ambitious TJ 2500 AMBIENT HEAT BAU 3500 AMBIENT HEAT AMBITIOUS TJ TJ Total TJ Heat TJ Hot water TJ Total TJ Heat TJ Hot water TJ Figure 8 BAU and ambitious scenario for the use of ambient heat (heat pumps) per application in Upper Austria until 2030 O.Ö. Energiesparverband 18

19 HEAT PUMPS BAU (NUMBER) HEAT PUMPS AMBITIOUS (NUMBER) Number Number Heat number Hot water number Heat number Hot water number Figure 9 Number of heat pumps (BAU and ambitious scenario in Upper Austria until 2030) O.Ö. Energiesparverband 19

20 2.2 "EGEM Energiespar-Gemeinden" Local energy strategies In Upper Austria, nearly 100 municipalities are presently developing and implementing local energy strategies. This process is supported by the Upper Austrian programme "EGEM Energiespargemeinden" ("Energy saving municipality") where municipalities develop local energy strategies (including clear targets and action plans for achieving the targets). As a part of this local policy process, targets are developed, in some cases based on scenarios. In the following, two different approaches which are frequently followed to implement local energy strategies are outlined: Approach 1: Energiebaukasten By using the Energiebaukasten, municipalities can develop an energy strategy with the target to cover their energy demand to 100% by renewable energy sources within 30 years. The first step is to show that municipalities in principal can provide their energy demand with 100% renewable energy sources. The Energiebaukasten includes the following modules: modul1: survey energy consumption: data research through questionnaires for households, business, agriculture and public buildings. The results are extrapolated or estimated respectively by using known indicators or averages. modul 2: survey saving potential modul 3: survey potential renewable energy sources modul 4: design of the programme 100% RES in 30 years modul 5: implementation energy saving modul 6: implementation energy production modul public relations: from start to finish For modul 3 "survey potential of renewable energy sources", the utilised and the available potential is collected. Example for the estimation of the available potential: Solar thermal: assumption of solar colletors with a surface of 20 m 2 for each household, farm and company O.Ö. Energiesparverband 20

21 Biomass wood: the potential is calculated by using indicators (forest statistical data) and the available forest area. 35% of the annual energy yield of the usable forest area are calculated for energetic use. Biomass energy grass: the potential is calculated by using indicators (agricultural statistics). 0.2 ha per person are calculated for food supply, the remaining land could be used for energy production. Therefore, about one third of the agricultural land could be used for the cultivation of energy grass. Biomass biofuels from plant oil: the potential is calculated by using indicators (agricultural statistics, see energy grass). It is estimated that around 15% of the agricultural land (minus food supply) could be used for fuel production. Biomass plants for the production of natural gas: the potential is calculated by using indicators (agricultural statistics). 0.2 ha per capita are calculated for food supply, the remaining land could be used for energy production. Therefore, about one third of the agricultural land could be used for the cultivation of plants for the production of biogas. Based on these estimations of the potential, short, middle and long term targets in the field of renewable energy sources are decided for the municipality. Together with energy efficiency targets, these targets are the baseline for developing the action plan. Approach 2: Energy strategy Strudengau In the region Strudengau, 17 municipalities cooperate by developing their municipal energy strategy, using the following approach: Survey energy in the following steps: o Comprehensive survey of primary data of private, public and commercial consumers using a questionnaire prepared by the EGEM planner o Plausibility check o Comprehensive analysis Survey of secondary data and extrapolation: o Using data of the municipal dwelling register and of Statistik Austria o Extrapolation of the end energy use, the end energy saving potential, the proportion of the energy sources and the emission balances Design of the energy strategy: O.Ö. Energiesparverband 21

22 o Timetable o The following assumptions were i.a. made: expected growth rates were considered, e.g. increase in the building by 1% per year; energy demand of new buildings of 25 kwh/m 2,a; growth rate of inhabitants and hot water demand of 1%; increase in mobility in 1.6%; increase in electricity demand by 2% per year o Automatic efficiency ranking by economy (ranking of the measures by pay back periods) o Efficiency increasing measures: existing and realistic end energy saving potential due to retrofitting measures yields about 140,000 MWh per year, the investment costs around 310 Mio for the 17 municipalities. o Assumptions for the exchange of heating systems: all old systems (installations before 1988) and fired by fossil fuels are substituted by biomass boilers; of those installed between 1988 und 1998, 50% remain fossil fuel boilers (however efficient technology). In total, referring to the energy strategy almost 6,000 boilers will be exchanged or made more efficient respectively; this corresponds to about 68% of all installed heating systems. o The potential of solar collectors is estimated with 4 m 2 per capita, this would result in end energy saving of about 60,000 MWh/a. 2.3 Discussion of the different approaches Energy future: Different models for land use for the different energy sources, the potential energetic and material development of agricultural land use in Upper Austria is shown in three scenarios. An overview of the actual use and the feasible as well as the technical potentials of renewable energy sources for heat production in Upper Austria was worked out. Based on this estimation of the potential, the targets were decided at the political level. E-GEM Energiebaukasten: Prior target is to show that municipalities can cover their energy demand 100% by renewable energy sources. Based on the survey of the energy consumption and of the saving potential, the potential of renewable energy sources is assessed. Thereby, the used and the available potentials for the different energy sources are calculated. For example for wood, the potential is calculated by using indicators of the forest statistics and 35% of the annual energy yield of the usable forest area O.Ö. Energiesparverband 22

23 calculated for the energetic use. On basis of these estimations of the potentials, the targets in the field of renewable energy sources are decided for the municipality. Together with energy efficiency targets these targets are the baseline for developing the action plan. The potential survey is the baseline for the development of the action plan 100% renewable energy in 30 years. E-GEM Strudengau: After a comprehensive primary data collection and analysis at public, private and commercial consumers, a secondary data survey and an extrapolation are carried out. For that, data of the municipal dwellings register and of Statistik Austria are used. Based on that, an extrapolation of the end energy use, the energy saving potential, the proportion of energy sources and the emission balances are carried out, followed by developing the energy strategy for the implementation time frame 2010 until O.Ö. Energiesparverband 23

24 3 Top-down approach 3.1 The Green-X model As in previous projects such as FORRES 2020, OPTRES, PROGRESS or FU- TURES-E the Green-X model was applied to again perform a detailed quantitative assessment of the future deployment of renewable energies on country-, sectoral- as well as technology level. The core strength of this tool lies on the detailed RES resource and technology representation accompanied by a thorough energy policy description, which allows assessing various policy options with respect to resulting costs and benefits. A short characterisation of the model is given below, whilst for a detailed description we refer to The model Green-X has been developed by the Energy Economics Group (EEG) at Vienna University of Technology in the research project Green-X Deriving optimal promotion strategies for increasing the share of RES-E in a dynamic European electricity market, a joint European research project funded within the 5th framework program of the European Commission, DG Research (Contract No. ENG2-CT ). Initially focussed on the electricity sector, this tool and its database on RES potentials and costs have been extended within follow-up activities to incorporate renewable energy technologies within all energy sectors. Green-X covers geographically the EU-27, and can easily be extended to other countries such as Turkey, Croatia or Norway. It allows to investigate the future deployment of RES as well as accompanying cost comprising capital expenditures, additional generation cost (of RES compared to conventional options), consumer expenditures due to applied supporting policies, etc. and benefits i.e. contribution to supply security (avoidance of fossil fuels) and corresponding carbon emission avoidance. Thereby, results are derived at country- and technology-level on a yearly basis. The time-horizon allows for in-depth assessments up to 2020, accompanied by concise out-looks for the period beyond 2020 (up to 2030). Within the model, the most important RES-Electricity (i.e. biogas, biomass, biowaste, wind on- & offshore, hydropower large- & small-scale, solar thermal electricity, photovoltaics, tidal stream & wave power, geothermal electricity), RES- Heat technologies (i.e. biomass subdivided into log wood, wood chips, pellets, grid-connected heat -, geothermal (grid-connected) heat, heat pumps and solar thermal heat) and RES-Transport options (e.g. first generation biofuels (biodiesel and bioethanol), second generation biofuels (lignocellulotic bioethanol, O.Ö. Energiesparverband 24

25 BtL) as well as the impact of biofuel imports) are described for each investigated country by means of dynamic cost-resource curves. This allows besides the formal description of potentials and costs a detailed representation of dynamic aspects such as technological learning and technology diffusion. Besides the detailed RES technology representation the core strength of the model is the in-depth energy policy representation. Green-X is fully suitable to investigate the impact of applying (combinations of) different energy policy instruments (e.g. quota obligations based on tradable green certificates / guarantees of origin, (premium) feed-in tariffs, tax incentives, investment incentives, impact of emission trading on reference energy prices) at country- or at European level in a dynamic framework. Sensitivity investigations on key input parameters such as non-economic barriers (influencing the technology diffusion), conventional energy prices, energy demand developments or technological progress (technological learning) typically complement a policy assessment. The general modelling approach to describe renewable energy generation technologies in the model Green-X is to derive dynamic cost-resource curves for each generation and reduction option in the investigated region. Dynamic cost curves are characterised by the fact that the costs as well as the potential for electricity generation / demand reduction can change year by year. The magnitude of these changes is given endogenously in the model, i.e. the difference in the values compared to the previous year depends on the outcome of that year and the (policy) framework conditions set for the simulation year. In principle, the approach is carried out in three steps: The development of static cost-resource curves for each generation and demand reduction option, on a technology and country-level; The dynamic assessment, including a dynamic assessment of costs as well as of potential restrictions, in order to derive annual dynamic cost-resource curves. The derivation of the dynamic cost-resource curve. The technology and country-specific dynamic cost-resources for the simulation year are derived by combining the static cost-resource curves with the dynamic assessment. This dynamic cost-resource curve on the supply side contains information about actual generation costs and the possible potential for electricity generation for various technologies for the simulation year. The following figure illustrates this procedure for one technology. O.Ö. Energiesparverband 25

26 Already achieved potential of technology x in year n-2 Costs [ /kwh] New achieved potential of technology x in year n-1 Costs [ /kwh] electricity generation [GWh] electricity generation [GWh] already achieved potential ( capacity built) year n-2 New achieved potential capacity built) in year n-1 Achieved potential of technology x in at the end of year n-1 Costs [ /kwh] End of life-time in year n of the installed capacity technology x electricity generation [GWh] already achieved potential ( capacity built) year n-1 Achieved potential of technology x available in year n Costs [ /kwh] already achieved potential available in year n (capacity built) electricity generation [GWh] Figure 10 Method of approach regarding dynamic cost-resource curves for RES (for the model Green-X) 3.2 Assessment of RES potentials in Green-X From a historical perspective, the starting point for the assessment of realisable mid-term potentials in the Green-X model was geographically the European Union as of 2001 (EU-15), where corresponding data was derived for all Member States initially in 2001 based on a detailed literature survey and a development of an overall methodology with respect to the assessment of specific resource conditions of several RES options. In the following, within the framework of the study Analysis of the Renewable Energy Sources evolution up to 2020 (FORRES 2020) (see Ragwitz et al., O.Ö. Energiesparverband 26

27 2005) comprehensive revisions and updates have been undertaken, taking into account reviews of national experts etc. Consolidated outcomes of this process were presented in the European Commission s Communication The share of renewable energy (European Commission, 2004). Within the scope of this project again an intensive feedback process at the national and regional level was established. A series of six regional workshops was hosted by the futures-e consortium around the EU within The active involvement of key stakeholders and their direct feedback on data and scenario outcomes helped to reshape, validate and complement the previously assessed information. 3.3 Scenario description The following sections will show results for the so called strengthened (national) policies scenario derived by the Green-X model, which describes a realistic path that could serve as a guideline for fulfilling the 2020 target. In more detail the strengthened policy scenario can be characterised as follows: Strengthened policy scenario: Hereby it is assumed that the European RES policy framework will be improved with respect to its efficiency and effectiveness. These changes will become effective by 2011 in order to meet the agreed target of 20% RES by Improvements refer to both the financial support conditions (if necessary) as well as to non-financial barriers (i.e. administrative deficiencies etc.) where a rapid removal is also preconditioned. The fulfilment of the target of 20% RES by 2020 is preconditioned at EU level as well as at national level. For the case that a Member State (MS) would not possess sufficient potentials, MS based transfers as foreseen in the RES Directive (i.e. where MS posses the possibility to transfer their surplus to other MS) would serve as complementary option to fulfil given 2020 RES objectives. For the period beyond 2020 intensified cooperation between MS is preconditioned, meaning a step towards intensively coordinated RES support all over Europe and an enhanced sharing of corresponding costs and benefits. Overview on key input parameters Besides the comprehensive Green-X database for RES which includes potentials and costs for RES-E within Europe on a country and technology level and assumptions with respect to the overall conventional energy system are discussed below in a concise manner. O.Ö. Energiesparverband 27

28 In order to ensure maximum consistency with existing EU scenarios and projections, the key input parameters of the scenarios are derived from PRIMES modelling and from recent assessments of the European RES market (FOR- RES 2020, OPTRES, PROGRESS). Table shows which parameters are based on PRIMES and which have been defined for this study. More precisely, the PRIMES scenario used to depict the overall energy demand is the following: The European Energy and Transport Trends by 2030 / 2007 / Efficiency Case (16% demand reduction compared to baseline) Based on PRIMES Defined for this study Energy demand Reference electricity prices RES cost (based on FORRES 2020, Primary energy prices PROGRESS) Conventional supply portfolio and RES potential (based on FORRES conversion efficiencies 2020, PROGRESS) Biomass import restrictions Technology diffusion Learning rates Weighted average cost of capital (WACC) Table 2: Main input sources for scenario parameters 3.4 Results for EU The share of RES-H on overall RES deployment As is shown in the graph below the RES heating sector currently contributes more than half of the overall final energy deployment of renewable energy sources. This overall contribution within the renewable energy mix will slightly decline to about 45% until As shown in figure the share of renewable energy in the total heat demand will grow at a very similar pace as the contribution of renewable energies in total final energy consumption in the EU and will reach about 20% by O.Ö. Energiesparverband 28

29 RES generation (TWh) RES-electricity (& CHP) RES-transport RES-heat Figure 11 RES generation until 2030 in a strengthened policy scenario in EU-27 countries RES share on gross final energy demand 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 2006 electricity heat transport total final energy demand - medium Figure 12 The share of RES on gross final energy demand until 2030 in a strengthened policy scenario in EU-27 countries RES-H development As shown in Figure 3 grid connected solid biomass and non-grid connected solid biomass currently are the most important RES-H generating technologies. The RES-H generation in the EU-27 will nearly double until Heat pumps and solar heating and hot water will gain a significantly higher share until Heat pumps increase their generation until 2020 by more than a factor of ten and will continue in growth the next 10 years. Solar thermal heating and hot wa- O.Ö. Energiesparverband 29

30 ter will exceed 100 TWh/a in Also the Solid biomass (grid) triples its contribution until RES-H generation (TWh) Biogas (grid) Biowaste (grid) Solid biomass (non-grid) Heat pumps Solid biomass (grid) Geothermal heat (grid) Solar thermal heating and hot water Figure 13 RES-H generation until 2030 in a strengthened policy scenario in EU-27 countries As shown in figure 4 RES-H non grid currently contributes about 81% to the RES-H generation in the EU-27. This share will decline to 73% by The overall RES-H generation will raise its amount of currently (2009) about 790 TWh to about 1360 TWh/a by RES-H district heating & large scale generation raises its shares to 11% by RES-H CHP shows only a limited growth from 2020 onwards. RES-H generation (TWh) RES-H non-grid RES-H district heating & large scale RES-H CHP 2030 Figure 14 RES-H generation (s ectors) until 2030 in a strengthened policy scenario in EU-27 countries O.Ö. Energiesparverband 30

31 new installed annual RES-H capacity (MW) Heat pumps Solar thermal heating and hot water Solid biomass (nongrid) Geothermal heat (grid) Biowaste (grid) Solid biomass (grid) Biogas (grid) Figure 15 New installed RES-H capacity in a strengthened policy scenario in EU-27 countries 3.5 Green-X results for Austria The share of RES-H on overall RES deployment As shown in Figure 5 the total RES generation of Austria in a strengthened policy scenario will grow from nearly 81 TWh in 2006 to about 123 TWh in The fraction of RES-heat was 46% in 2006 and increases until 2020 to about 51% in 2020 and maintains roughly at this level until The contribution of RES-electricity (& CHP) rises less strongly than RES-heat. It starts in 2006 at about 44 TWh, reaches 56 TWh in 2020 and 61 TWh in RES generation (TWh) RES-electricity (& CHP) RES-transport RES-heat Figure 16 RES generation until 2030 in a strengthened policy scenario in Austria O.Ö. Energiesparverband 31

32 Figure 17 shows the share of RES on gross final energy demand until 2030 in the strengthened policy scenario in Austria. The total RES share will rise from 24.5% in 2006 to 36.9% in 2020 and reaches 42.5% in The highest share of renewables is reached in the electricity sector. Starting from 66.4% in 2006 rising up to a peak of 80.8% in 2020 it is projected to decline in the following years to 71.5% in The heat sector rises nearly linearly from 21.4% in 2006 to 35.7% in 2020 and 43.8% in Until 2010 the transport sector plays an unimportant role with a small share of below 1%. But from 2010 on it rises significantly and reaches a share of 5.9% in 2020, which is however still below the European target of 10%. Until 2030 it amounts to 8.3%. RES share on gross final energy demand 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2006 electricity heat transport total final energy demand - medium Figure 17 The share of RES on gross final energy demand until 2030 in a strengthened policy scenario in Austria RES-H development Figure 17 shows the development of the different RES-H generation technologies until 2030 in the strengthened policy scenario in Austria. The overall RES- H generation (TWh) rises from 37.3 TWh in 2006 to 62.2 TWh in 2020 and then the growth softens and the renewable heat generation reaches 69.2 TWh in The generation of biogas (grid) saturates in 2011 with an amount of 0.3 TWh per year. Solid biomass (grid) and solid biomass (non-grid) play an important role in the Austrian RES-H production. Their yearly generation rises from 3.7 TWh (grid) and 30.5 TWh (non-grid) in 2006 to 11.1 (grid) and 39 TWh (non grid) in The estimated amount for 2030 is 11.8 TWh (grid) and 42.3 TWh (non grid). Although geothermal heat (grid) could nearly double its output from 0.3 TWh in 2006 to a amount of 0.5 TWh by 2020 it remains a relatively small contribution and cannot reach more than 0,7 TWh by More important is the RES-H generation through heat pumps. It quintuples from 1.2 TWh (2006) to 6,3 TWh in In the following 10 years it could reach an amount of 8.8 O.Ö. Energiesparverband 32

33 TWh annual generation. After a strong growth until 2016 the solar thermal heating and hot water contribution stabilises at an amount of 3.4 TWh. Biowaste remains at a very low level of about 0,03 TWh throughout the period RES-H generation (TWh) Biogas (grid) Biowaste (grid) Solid biomass (non-grid) Heat pumps Solid biomass (grid) Geothermal heat (grid) Solar thermal heating and hot water Figure 18 RES-H generation (technologies) until 2030 in a strengthened policy scenario in Austria Figure 18 shows the RES-H generation with its sectors until 2030 in the strengthened policy scenario in Austria. The RES-H non grid takes the biggest share with a noticeable growth. It rises from 32.7 TWh (2006) to 48.7 TWh in 2020 and 54.5 TWh in With 6.3 TWh the RES-H district heating & large scale shows its peak amount in 2020 after steady growth before. It shows a slight decline to 5.9 TWh/a in The RES-H CHP sector shows a steady growth from 2 TWh in 2006 to 7.2 TWh in 2020 and 8.8 TWh in RES-H generation (TWh) RES-H non-grid RES-H district heating & large scale RES-H CHP Figure 19 RES-H generation (sectors) until 2030 in a strengthened scenario in Austria O.Ö. Energiesparverband 33

34 2.500 new installed annual RES-H capacity (MW) Heat pumps Solar thermal heating and hot water Solid biomass (non-grid) Geothermal heat (grid) Biowaste (grid) Solid biomass (grid) Biogas (grid) Figure 20 New installed RES-H capacity in a strengthened policy scenario in Austria O.Ö. Energiesparverband 34

35 4 Bottom-up approach In this section of the report, bottom-up based scenarios for the main RES-H/C technologies are developed. The analysis is carried out both for the building and the industry sector. In section, the general approach and methodology for this analysis is described. The next three chapters contain results for solar thermal collectors, biomass and heat pumps respectively in the building sector and in addition, an overview on the industry based results is included. 4.1 General approach and methodology The term bottom-up is used for this approach because data of the building stock, available roof area, currently existing heating systems etc. were disaggregated. Therefore, this approach can provide a detailed data basis supporting the technology specific target setting process. The objective of carrying out this bottom-up analysis is to understand the bottom-up meaning and relevance of a certain target more clearly. For example, this analysis helps to identify the share of roof area that has to be equipped with solar collectors or the share of single dwellings with wood pellets boilers. Thus, it is a tool for increasing the transparency of the target setting and can help us to assess how ambitious a certain target is. We want to state clearly that it is not an objective of this task to provide a prognosis of what will happen. Also, economic restrictions are not explicitly taken into account 6 (also this is implicitly the case by setting certain values for diffusion restrictions). Rather, the approach helps us to show the relation between certain diffusion parameters and the related energy output. Building Sector In the following, we give a rough overview on the general methodological steps that are applied for the building sector. 1. In the first step we assume a certain maximum technology penetration. The definition of the technology penetration is related to a time span (see next step): 98% of the maximum technology penetration will be achieved after a certain diffusion time (e.g. x% of buildings equipped with a certain biomass heating system or solar collectors). 6 This will be done in the model based analysis of WP4 of the project. This will include modelling the impact of various economic incentives, economic side conditions like energy price settings etc. O.Ö. Energiesparverband 35

36 2. As the second step we assume a certain diffusion time constant. The setting of this time constant has to be seen in relation to the maximum technology penetration defined in step 1. From typical historical diffusion processes we know that in the building sector usually time constants between about 30 and 60 years can be observed. 3. With steps one and two we have defined key parameters for the possible future diffusion of this technology. However, of course the actual development also depends on the maturity of a market and thus the current penetration of a technology has to be documented and taken into account in the third step. 4. With these three parameters defined in the first three steps we determine the diffusion of a technology by a standard S-Curve approach (see figure below). The concrete values of these diffusion parameters are documented below for the different technologies solar collector area (Mm²) Diffusion time Maximum technology penetration Current penetration Figure 21 Principle of a S-curve diffusion approach applied in the bottom-up analysis of the building sector 5. Besides this standard approach determining the scenario of the technology diffusion we assume a certain rate of thermal renovation within the building stock (different construction periods are treated separately). This is in particular essential for the amount of energy that is provided by a certain technology. With increasing thermal building efficiency an increasing number of heating systems can lead to a stable or even declining heat output. In our results presented below this is to some extent relevant for the biomass heating results. 6. As a last step, we are carrying out additional checks regarding basic consistency e.g. with current annual installations, total amount of RES-H in different building classes O.Ö. Energiesparverband 36

37 This methodology is applied both on residential and non-residential buildings. We are distinguishing the three main technology sectors: solar thermal collectors, biomass, RES-H/C target report RES-H Policy and heat pumps. The specific methodological approaches, assumptions and results are documented in the chapters below. Industry Sector Also for the industry sector a bottom-up approach is applied. The same restrictions apply as indicated for the building sector: the outcomes primarily serve as a base for discussion. Characteristics of industrial processes are decomposed into energy use in different industrial subsectors, distinguishing between temperature level and energy carriers currently used. Three separate methodological steps are applied for the industry sector: 1. Based on several data sources, non-electric and non-feedstock energy use in industrial processes is decomposed into energy use per energy carrier, per temperature level and per industry subsector and extrapolated to the year For the base year 2005, each of the abovementioned decomposed energy uses are assigned energy conversion technologies, based on statistical information. 3. Applying a series of substitution and exclusion rules, a set of constraints is applied which indicates which share of the industrial energy use in the country at stake is available for RES-H/C: the potential or target. The first two steps help to define the future energy use and the technologies applied, and the third step uses the detailed and decomposed picture to narrow down all possible applications of renewable energies in processes. The constraints applied are discussed in Section 4.5. Among the selected target regions, Upper Austria is the one with the highest share of RES-H and so are the targets for For scenarios reaching nearly a 100% share of RES in the heating sector, it is more and more important to check possible overlapping aspects and to avoid double counting of different RES-H technologies. With this respect, the bottom-up approach has some limitations for the case of Upper Austria. However, in work package 4 of this project, a detailed modelling analysis is carried out taking into account all these aspects. O.Ö. Energiesparverband 37

38 4.2 Solar thermal collectors buildings Methodology In the following, we will give an overview on the main steps of the methodology for the technology solar thermal collectors for heating and hot water in the building sector. 1. We are calculating the roof area for each building class from building data describing the building stock within a country. 2. For reasons of inappropriate orientation etc. we assume a certain share of the maximum roof area which is suitable for solar thermal collectors. 3. Combining step one and two with the number of buildings within each building class we determine the total maximum available roof area (Mm²). 4. For each building class we determine the max. penetration rate of suitable roof area. This refers to step 1 that we explained in section For each building class we assume the corresponding time constant (time that has to pass until an increase from 1% to 98% of the maximum penetration is achievable). 6. As the next step the current diffusion of solar thermal appliances per building class is determined. 7. As described above, we apply the standard S-curve approach for modelling of dynamic diffusion of solar thermal collectors. 8. Based on the specific average solar yield (kwh/m²/yr) we calculate the solar thermal heat generation. 9. In order to avoid excess solar thermal energy supply within a building and thus double-counting we carry out a check with the solar share on total heating and DHW consumption within each building class. A critical issue that should be further is discussed is the question of solar district heating. Actually, our approach does not explicitly distinguish between grid connected and non-grid connected solar heating systems. However, for this basic bottom-up approach we are mainly using the available roof area as a basic starting point. Since this basic potential is more or less the same for grid connected and non-grid connected systems we assume this approach as valid. O.Ö. Energiesparverband 38

39 Assumed diffusion parameters and results The following table contains the basic input data as described above: unit multiple dwellings single dwellings nonresidential buildings Solar thermal share of roof area suitable for solar thermal* max. penetration rate of solar collectors on suitable roof area diffusion time constant (time that has to pass for an increase from 1% to 99% of the maximum potential) specific average solar yield % 25% 25% 25% % 60% 60% 60% yr kwh/ m²/yr Table 3 Input data for solar thermal bottom-up analysis (Upper Austria) * 50% of the roof area is orientated towards North and therefore not suitable and 25% are not suitable due to shading and windows etc. As a result 25% of the roof area was regarded suitable. 6,0 solar collector area (Mm²) 5,0 4,0 3,0 2,0 1,0 0, Figure 22 Installed solar collector area in the selected bottom-up scenario for Upper Austria O.Ö. Energiesparverband 39

40 solar thermal heat (TJ) Figure 23 Solar thermal heat generation in residential buildings in the selected bottom-up scenario for Upper Austria Discussion of results: Upper Austria has a relatively high starting point (m² per roof area) compared to Austria. 4.3 Biomass heating buildings Methodology In the following, we will give an overview on the main steps of the methodology for the technology biomass heating systems in the building sector. This is applied separately to the sectors wood log, wood chips, wood pellets and biomass district heating: 1. As a first step we determine the maximum diffusion of the specific biomass system (e.g. wood log boilers) for every building class (% of total number of buildings in this class). 2. For each building class we assume the corresponding time constant (time that has to pass until an increase from 1% to 98% of the maximum penetration is achievable). 3. We take the current distribution of these technologies in every building class as starting point for the S-curve approach. O.Ö. Energiesparverband 40

41 4. Taking into account the mean heating energy consumption in every building class and a certain (ambitious-realistic) renovation scenario we estimate the biomass energy demand. 5. As a consistency check we compare the total biomass energy demand in the scenario with the bioenergy potential available for residential heating purposes (weak coupling, no strict restriction). The biomass potential is derived from EEA For determining the biomass potential available for heating we subtract the energy demand for electricity and transport for the different bioenergy fractions according to the Green-X scenario described above. After this comparison, it is up to the stakeholders and decision makers to decide whether biomass imports are acceptable or whether biomass exports should be possible. Feed-in of biogas into the natural gas grid could be an additional option for RES-H. Our approach for this part is to assess the possible biogas-potential for feed-in purposes (taking into account the source EEG2007 and the Green-X scenario documented above). This potential we are comparing to the natural gas consumption within a country. This comparison provides a basis for determining the potential share of biogas in natural gas consumption. Assumed diffusion parameters and results The following table contains the basic input data as described above: O.Ö. Energiesparverband 41

42 Upper Austria unit multiple dwellings single dwellings nonresidential buildings Biomass heating max. share of biomass heating systems on the total number of buildings Wood log % 32% 0,5% 0,5% Wood chips % 6% 20% 20% Wood pellets % 25% 20% 20% District heating % 25% 20% 20% diffusion time constant (time that has to pass for an increase from 1% to 99% of the maximum potential) Wood log yr Wood chips yr Wood pellets yr District heating yr Reduction of mean heating energy demand due to thermal renovation in 2020 Reduction of mean heating energy demand due to thermal renovation in 2030 % 10% 9% 14% % 18% 16% 25% Table 4 Input data for biomass heating in buildings for the bottom-up analysis in Upper Austria O.Ö. Energiesparverband 42

43 Number of buildings with biomass heating system Biomass district heating Wood Pellets Wood chips Wood log Figure 24 Number of buildings with biomass heating systems in the selected bottomup scenario in Upper Austria Biomass primary energy for heating in the building sector (TJ) Biomass district heating Wood Pellets Wood chips Wood log Figure 25 Biomass fuel input for heating in residential buildings in the selected bottom-up scenario in Upper Austria The comparison of the figures above shows that although the number of buildings with biomass heating systems triples up to 2030, the final energy only doubles in this time period. The reason for this result is the increasing thermal quality of the building stock. Not only new buildings become more and more efficient. Rather existing buildings are also increasingly subject to renovation measures. This leads to lower energy demand. In particular this is the case for wood log heating systems. On the one hand, they are partly installed in old O.Ö. Energiesparverband 43

44 buildings with a higher need for renovation. On the other hand, due to our assumptions the number of buildings with wood log systems remains almost constant. The most dynamic development can be observed for pellet heating systems in this scenario. The selected bottom-up scenario shows a clear shift from wood log to district heating, pellets and wood chips. Discussion of results For multi-family dwellings and non-residential building, it is more likely that automatic biomass heating installations (wood chips instead of log wood) will be installed. Due to the high energy efficiency standard of single-family homes and the rural settlement structure, log wood will have a higher share in this sector. Due to regional conditions (rural structure, high level of information and advice services), biomass consumption (primary energy) will increase much more than Austrian-wide, even more so as the Austrian data include large cities (Vienna) where biomass is much less used. 4.4 Heat pumps buildings Methodology In the following, we will give an overview on the main steps of the methodology for the technology heat pumps in the building sector. The basic assumption for the approach is that ambitious COP values (of 3.5-4) are only achievable in buildings with low-temperature heating systems. 1. Thus, the first step is to identify the share of buildings that is suitable for heat pumps (assuming that only in buildings with low temperature heating system and corresponding low energy house standard COP above is achievable). For all existing building classes and new buildings an S-Curve approach is applied to determine the share of correspondingly innovative renovation in existing buildings and low energy standard in new building construction, respectively. Multiplying this share with the renovation rate (or new building construction rate) and the total number of buildings gives the annual number of buildings suitable for heat pump installation. O.Ö. Energiesparverband 44

45 2. The actual number of annual heat pump installation (and total accumulated number of heat pumps, respectively) is determined by an S-curve approach assuming a certain maximum diffusion share of heat pumps in those buildings which are suitable for heat pumps. 3. The total ambient energy from heat pumps is calculated on an assumed low energy house standard for all building classes and an annual COP of 4. Deep soil for geothermal district heating is not considered in this bottom-up approaches. For countries where this technology has a considerable potential this will be taken from available literature. Country specific data The following table contains the basic input data as described above: Geothermal, heat pumps share of annually renovated and new constructed buildings that is suitable for heat pumps in 2020 new buildings % 79% 79% 79% 90% share of annually renovated and new constructed buildings that is suitable for heat pumps in 2030 % 80% 80% 80% 90% max. penetration rate of heat pumps in those buildings that is suitable % 50% 50% 50% 50% COP diffusion time constant (time that has to pass for an increase from 1% to 99% of the maximum potential) yr current average specific useful heating energy demand of buildings with heat pumps future average specific useful heating energy demand of buildings with heat pumps kwh/m²/yr kwh/m²/yr Table 5 Input data for geothermal / heat pumps in buildings for the bottom-up analysis in Upper Austria O.Ö. Energiesparverband 45

46 Figure 26 Number of residential buildings with heat pumps in the selected bottom-up scenario in Upper Austria Figure 27 Ambient heat utilization from heat pumps in residential buildings in the selected bottom-up scenario in Upper Austria Discussion of results The restriction of heat pumps to efficient buildings only (heat pumps are only applied in buildings with low-temperature heating systems and high thermal quality) defines the potential. The number of buildings with heat pumps increases by more than the factor 5 until Due to the fact that the number of efficient buildings is increasing quite slowly, in the turning point in the S-curve is not achieved until O.Ö. Energiesparverband 46

47 4.5 RES-H/C in the industry sector Methodology This section explains in more detail the methodology for determining the RES- H/C potential in industry. The core data for assessing the technologies can be found in Annex. To begin, a lot of steps are needed to find the energy use in industry in a highly decomposed manner. In short, data for individual energy carriers in all industry sectors have been taken from ODYSSEE (2009), which were then extrapolated using the PRIMES (2007) baseline scenario. Then a decomposition has taken place based on literature and in some cases expert judgements. In order to correctly attach future projections to historical data specific technologies have been introduced and matched to renewable energy carriers already in use. Then, RES-H/C technologies have been matched to temperature levels according to Table. The leading principle for determining which technology could possibly be used in a temperature level has been to use standard technology configurations, ready for uptake. Exotic configurations thus haven t been listed; for example concentrating solar thermal for the highest temperature level is not considered. A few technologies are considered less suitable for use in industry, and have thus been neglected (these entries are depicted in light grey in table 6). This regards combined heat and power from geothermal energy, underground thermal storage (or storage in lakes), solar assisted cooling and the valorisation of ambient energy using heat pumps. In the Annex more information can be found on the reasoning behind these choices. O.Ö. Energiesparverband 47

48 Level Temperature range Biomass Deep geothermal Heat pumps Solar thermal Underground thermal storage H5 Above 600 C x H4 Between 200 and 600 C x H3 Between 100 and 200 C x x x H2 Between 65 and 100 C x x x H1 Below 65 C x x x x x C3 Between +10 and +15 C x x x C2 Between -30 and +10 C x C1 Losses Below -30 C Several temperature levels Table 6 Matching of RES-H/C technologies to temperature levels. For the modelling, only the entries displayed in bold have been considered. The most crucial step in determining the targets is to cut down the potential usage areas to ultimately find the possible penetration rates. To this end, a series of four constraints is applied to find it: 1. Constraints on supply of resources: especially for biomass processes the amount of fuel input is subject to constraints in resource availability. Indigenous resources have been assessed in detail in literature. In addition to these resources, biomass can also be imported from other countries or other continents. Also for import a constraint applies: in an open market it is not very likely that one single country absorbs the full wordwide biomass potential. 2. Constraints on supply of conversion or caption equipment. Emerging technologies with small current market shares are limited in market growth. This applies for example to biomass gasification units (only few companies can design large installations), deep geothermal (only few companies have the right competences for deep drilling) and also to solar thermal, for which cumulative sales and installations can only grow sustainably up to certain percentages annually. 3. Constraints on the demand side: not all conventional energy carriers can be substituted to a full extent by RES-H equivalents. For example, coal can partly be substituted by high quality solid biomass with- O.Ö. Energiesparverband 48

49 out problems, but only up to a share of 20% (energy based) without changing the installation. Other constraints on the demand side are related to the temperature level of the process: solar thermal vacuum collectors can for example yield temperatures up to 200 C, but not above that level. Finally, most processes in industry are of a continuous nature, meaning that they require constant heat input. Some renewable technologies can only yield energy in a variable manner, which makes them less suitable for use in industry. This is for example the case for solar thermal energy. 4. Constraints because of competition: different biomass processes might depend on a same resource potential, or different technologies might be suitable for a same temperature level. The competition between the resources should be such that double counting is excluded. Applying the constraints 1 to 4 results in targets for the industry sector. These are presented in the next section. Country specific data The following table contains the basic input data as described above: The projection of total energy use in process industry considers all non-electric and non-feedstock energy use. The industry model determines the possible future penetrations of three RES-H options: solar thermal, geothermal and biomass-based technologies. The outcome is presented in Figure 28. Table 8 shows the percentages renewables in the total final energy use in industry. In Table 9 more detail is presented on the specific type of RES-H technology for biomass. For biomass two input streams have been considered, wood and waste. For the purpose of presentation this has not been indicated in the tables. Finally, Table 10 and Figure 29 show the intermediate results of the modelling process: by applying the series of constraints discussed above to the final energy demand in industrial processes the window of opportunity reduces at every consecutive step. Note, that the potential can become higher than 100% of final energy use in the first step, because competing options have not been excluded yet. The competition constraint is applied in the last modelling step. The biomass potential or availability is a result of a trade-off between use in the residential sector (as estimated through the bottom-up-approach explained above) and the industry sector. O.Ö. Energiesparverband 49

50 Unit Total energy use PJ Solar thermal PJ Geothermal PJ Biomass PJ (input) of which waste PJ (input) of which wood PJ (input) Table 7 Projection of total final energy use and renewable heating technologies in industrial processes in Austria (sources: ODYSSEE 2009, PRIMES 2007, RESolve-H/C) Figure 28 Projection of final energy use [PJ] in industrial processes in Austria (source RESolve-H/C) Solar thermal Geothermal Biomass Table 8 Contribution [%] to the final energy input in industrial processes in Austria (source RESolve-H/C) O.Ö. Energiesparverband 50

51 Technology Input Heat Electricity Bio-SNG Combined heat and power Waste Combined heat and power Wood Direct firing Wood Electricity from digestion Waste Heat only Waste Heat only Wood Bio-SNG from digestion Waste Bio-SNG from gasification Waste Bio-SNG from gasification Wood Direct geothermal heat use Geothermal Direct solar thermal heat use Solar Thermal Subtotal biomass Waste Subtotal biomass Wood Total biomass Wood and Waste Overall system conversion Wood and Waste 47% 13% 12% efficiency for biomass (weighted) Total all technologies All Table 9 RES-H/C technologies providing final renewable heat [PJ] for industrial processes in the year 2020 in Austria (source: RESolve-H/C) RES technology After demand After resources After equipment After competition side constraint constraint constraint constraint Solar thermal 16% 16% 2% 2% Geothermal 33% 33% 2% 2% Biomass 236% 33% 17% 17% All resources 285% 81% 21% 21% Table 10 Impact of applying series of constraints to the final energy demand in industrial processes in Austria (source RESolve-H/C) O.Ö. Energiesparverband 51

52 Figure 29 Impact of applying series of constraints to the final energy demand in industrial processes in Austria (source RESolve-H/C) Discussion of results As mentioned already, the data presented in the tables and figures above need to be interpreted with care. The outcome should be regarded as a starting point for a discussion rather than carved in stone. As in the current modelling exercise no economic constraints have been considered, the outcome is to be interpreted as a maximum potential, which will be further reduced in the course of the RES-H Policy project. 4.6 Synthesis of buildings and industry sector The previous chapters presented results of the bottom-up analysis for the building and the industry sector for the technologies biomass, ambient energy and solar thermal energy. The following tables show the contribution of each of these sectors for the different technologies as well as the total sum of RES-H final energy. O.Ö. Energiesparverband 52