A model-based assessment of climate and energy targets for the German residential heat system

Journal of Cleaner Production 142 (2017) 3151e3173

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

A model-based assessment of climate and energy targets for the German residential heat system Erik Merkel a, *, Russell McKenna a, Daniel Fehrenbach b, Wolf Fichtner a a b

Institute for Industrial Production (IIP), Chair of Energy Economics, Karlsruhe Institute of Technology (KIT), Hertzstraße 16, 76187 Karlsruhe, Germany European Institute for Energy Research (EIFER), Emmy-Noether-Straße 11, 76131 Karlsruhe, Germany

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 January 2016 Received in revised form 22 October 2016 Accepted 26 October 2016 Available online 28 October 2016

The residential building sector has an important role to play in the energy transition due to a high share of final energy consumed and a considerable amount of CO2 emitted. Ambitious targets in Germany relate amongst other things to a primary energy reduction of 80% and an increased usage of renewable energy sources in heat supply to 60% in 2050. Existing research in this area both lacks detail in modelling decentralised heat supply in residential buildings and fails to adequately quantitatively analyse this target achievement for Germany. In order to overcome these limitations, a novel model-based approach is presented in which the developed TIMES-HEAT-POWER optimisation model is coupled with a decentralised energy system optimisation model to determine optimal and realistic technology configurations, and a building stock simulation model to adequately and consistently project the evolution of the building stock in Germany. This novel configuration of models is then used to investigate the evolution of the electricity system and the residential heat system in Germany in the context of key energypolitical targets up to 2050. The national goals related to primary energy reduction and the share of renewable energy sources in final energy demand in the residential heat sector are missed in the Reference Scenario. On the other hand, target achievement requires deep insulation measures and a supply-side technology shift away from gas and oil boilers towards heat pumps and solar thermal. The scenario analysis reveals a significant sensitivity of the deployment of micro-Combined heat and power technologies (mCHP) and heat pumps to, amongst other things, the evolution of fuel prices, renewable electricity technologies, heat and electricity demand as well as technological progress. Further model extension can be identified inter alia in broadening the system boundaries to integrate further sectors (tertiary, industrial) or incorporating different user categories and decision rationales. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Energy system analysis Target assessment Residential heat supply Simulation Mixed integer programming

1. Introduction 1.1. Background and motivation Climate change, depleting finite energy resources and energy security are significant reasons for shifting to renewable energy and increasing energy efficiency. Amongst other targets for energy efficiency and renewable energy, the European Union (EU) has committed itself to reduce greenhouse gas (GHG) emissions by 80e95% relative to the level of 1990. In this context, buildings

* Corresponding author. E-mail addresses: [email protected] (E. Merkel), [email protected] (R. McKenna), [email protected] (D. Fehrenbach), wolf.fi[email protected] (W. Fichtner). http://dx.doi.org/10.1016/j.jclepro.2016.10.153 0959-6526/© 2016 Elsevier Ltd. All rights reserved.

account for about 40% and 36% of the total European end energy consumption and GHG emissions respectively (De Groote and Rapf, 2015). Around 75% of the total European building floor area is accounted for by residential buildings (Economidou et al., 2011), where the vast majority (up to 80%) of energy use and emissions is related to heating applications. Hence residential heating systems represent a prime target for action towards goal achievement. For example, in Germany the heat system is a particular focus in the Federal Government's Energy Concept. This aims at a reduction in heat demand by 20% up to the year 2020 as well as the primary energy demand by 80% until 2050, in buildings and relative to the level of 2008 respectively. In addition, the minimum share of renewable energy sources in heat consumption should be 14% by the year 2020 (currently about 13%, cf. BMWi (2016b)). Whilst the present contribution focuses on the European

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(especially German) context, it should be noted here that improving energy efficiency in all types of buildings is a globallyrelevant research field. For example, Zhou et al. (2016) analyse the energy efficiency savings through a retrofit of an office building in China, through a combination of building simulation, surveys of the building occupants and implementation of measures such as insulation, window replacement and Heating, Ventilation and AirConditioning (HVAC) system optimisation. They demonstrate that a combination of technical and behavioural measures can achieve annual energy savings of 57%. In addition, Huang et al. (2012) comparatively analyse eleven building energy saving technologies in the Chinese context, based on a multi criteria assessment using economic, environmental and social criteria. The results highlight some trade-offs between the different technologies, e.g. renewable technologies have a high sustainability score but also higher specific emission-reduction costs, which are also highly sensitive to the weights of individual criteria. Finally, Huang et al. (2016) review and analyse the existing energy-saving policies relating to buildings in Japan and China, giving an overview of current policies, identifying obstacles and providing recommendations to overcome these. The Energy Performance in Buildings Directive (EPBD, cf. European Commission (2012)) defines minimum performance standards for all buildings that undergo an energy-related innovation and specifies an evaluation methodology, with which to energetically assess buildings. In this context, several European Member States have implemented policies to encourage the households’ energy-related investments,1 for example in insulation and/or renewable energies. In Ireland, one such instrument is the Greener Homes Scheme, which was implemented in 2006 in order to provide grants for the installation of certain renewable energy technologies in/on residential buildings. Whilst this Scheme resulted in around 32,000 installations with a total cost of about 74 Vmillion over the period 2006e2010 (Kennedy and Basu, 2013), there is evidence that this uptake could have been improved if the Scheme better accounted for the heterogeneity amongst the target groups as well as fully considered both economic and noneconomic determinants of an investment (or not) in renewable technologies. The EPBD also stipulates the use of Energy Performance Certificates (EPCs), which contain and display the building's energy and GHG impact, and benchmarks these compared to other buildings in a similar class (age, type etc.). Indeed, EPCs represent a very useful data source for evaluating buildings and developing or enhancing existing building stock models (Dineen et al., 2015; Gangolells et al., 2016), a methodology which in principle is applicable to all EU Member States. EPCs are an example of an attempt to correct the specific market failure of incomplete/imperfect information, where the target group (in this case households) is very large and the products are similar (residential buildings), cf. Sorrell et al. (2004). Instead of just considering the up-front cost when buying/renting a house, buyers are supposed to be better informed through EPCs, and thus at least in theory able to account for the lifecycle energyrelated costs. This is also the motivation behind Debacker et al.’s (2013) analysis of the economic and environmental lifecycle costs of various heating and ventilation systems for new dwellings in a Belgian context. But whilst the authors illustrate that quite different heating systems are likely to be selected when economic and environmental criteria are juxtaposed, the preferences based on lifecycle costs generally coincide with those based on the up-front costs.

1 For an overview of these and other measures in the context of the EPBD, the interested reader is referred to the website of the “Concerted Action EPBD” Project at http://www.epbd-ca.eu/, accessed 28.08.2016.

Whilst the supply and use of heat in buildings is an important general field to focus on, there are more specific areas to which attention should be directed in the first instance. For example, a recent review of the current market situation for energy efficiency services in residential buildings in the EU found that, whilst the picture across the EU is quite heterogeneous, the largest potentials for cost-effective energy efficiency improvements lie in the (improved) insulation of, and installation of more efficient boilers in, multi-family houses (Labanca et al., 2015). In addition, there is substantial scope for GHG emission reductions through the exploitation of so called Low Carbon Technologies (LCTs) for heat and power supply, e.g. cogeneration/Combined heat and power (CHP) and heat pumps (OECD/IEA, 2011). 1.2. Decentralised heating in a systems context The supply and use of heat in buildings in the EU is dominated by decentralised applications, which means that around 87% of heat for European buildings is typically generated in or near to the object it supplies (Connolly et al., 2013). Hence approaches to analyse the heat system need to take into account its local nature. Examples include Nielsen (2013) and Mattinen et al. (2014), who present a bottom-up methodology to analyse and visualise residential building sector energy consumption and GHG emissions. Karschin and Geldermann (2015) develop a mixed-integer linear programme to analyse the heat and electricity supply in bioenergy villages through small-scale district heating networks. These methods are focussed on and only applicable at the local, i.e. at the building and/or community, level. They therefore require detailed data relating to the analysed energy system and infrastructure, and do not consider the macroeconomic interactions within the whole energy system. However, the evolution of heating systems in residential buildings is a complex matter to evaluate from a system perspective, due to the influence of a large number of socio-demographic and technological factors. LCTs which operate at the interface between residential heat and electricity systems, especially but not only heat pumps and CHP technologies, have strong interdependencies with the overarching energy system supply infrastructure. For example, they are strongly dependent on prices for electricity and fossil energy carriers, which themselves are determined based on multifaceted interactions at the level of the entire system. Whilst others have attempted to assess the market penetration of these LCTs based on diffusion theory (e.g. for microcogeneration based on hydrogen in the Netherlands see Taanman et al. (2008)), it is argued here that the contribution of these LCTs to meeting ambitious energy and emissions targets, amongst other things, can only adequately be assessed in a whole system modelbased approach at the national or international level. In this context, model-based energy system analysis, with its far reaching and sophisticated methods, provides appropriate prerequisites for a quantitative assessment of the system's evolution. This results in a multitude of models with a national scope in which the heat system of the residential buildings is considered. Indeed, several studies have already addressed this challenge with national energy system models for the analysis of residential heat supply in Germany. An overview of those studies focusing on decentralised heat supply in energy system models with a national focus is given along with selected criteria in Table 1 below. The criteria include the geographical and sectoral focus, the planning horizon, the methodological approach, whether the power plant park and building retrofit measures are modelled endogenously or exogenously, the consideration of object-based (decentralised) micro-CHP technologies, as well as the level of detail in modelling heat systems. Finally, the

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Table 1 Characterisation of selected studies focusing on decentralised heat supply in energy system models with national focus with the help of selected criteria. Study

Time Region Sector horizon (households/ tertiary/industry/ mobility)

Methodological approach for modelling the heat system

Integration with power plant park

Inclusion of object-based mCHP technologies

Level of modelling Consideration of Building retrofit technology mix measures modelled detail of heat effect in heat system supply systems endogenously

Assoumou and Maïzi (2011) Anandarajah and Strachan (2010) Bartels (2009) €user Bettgenha (2011) Blesl et al. (2004) Blesl et al. (2007) Chen et al. (2007) Chiodi et al. (2013) Dodds (2014) Eikmeier et al. (2006) García-Gusano et al. (2015) Henkel (2012) Kannan and Strachan (2009) Kranzl et al. (2013) Matthes et al. (2013) Nitsch et al. (2012) Rosenberg et al. (2013) Schlesinger et al. (2010) ~ es et al. Simo (2008) Steinbach (2013) Stengel (2014) Wünsch et al. (2014)

FR

x/x/x/x

2050

Opt.*

x

e

e

m

e

UK

x/x/x/x

2050

Opt.

x

e

e

l

e

DE DE

x/x/x/x/-/-/-

2030 2020

Opt. Sim.

x e

e e

e x

m l

e e

DE

x/x/x/x

2020

Opt.*

x

e

x

m

e

DE

x/x/x/x

2050

Opt.*

x

e

x

m

e

CN

x/x/x/x

2050

Opt.

x

e

e

m

e

IE

x/x/x/x

2050

Opt.*

x

e

e

l

e

UK DE

x/x/x/x x/x/x/-

2050 2020

Opt. Sim.

x e

x x

x e

h l

e e

ES

x/x/x/x

2050

Opt.*

x

e

e

m

e

DE UK

x/-/-/x/x/x/x

2025 2070

Sim. Opt.

e x

e e

e e

l l

e e

AU, LT x/-/-/-

2030

Sim.

e

e

x

m

e

DE

x/x/x/x

2030

Sim.

e

x

x

m

e

DE

x/x/x/x

2050

Opt.

x

x

e

l

e

NO

x/x/x/x

2050

Opt.*

x

e

e

m

e

DE

x/x/x/x

2050

Sim.

x

e

e

l

e

PT

x/x/x/x

2030

Opt.*

x

x

e

l

e

DE

x/-/-/-

2020

Sim.

e

e

x

m

e

DE DE

x/-/-/x/x/x/-

2030 2030

Sim. Sim.

e e

e x

x e

m l

e e

l ≙ low; m ≙ medium; h ≙ high; Opt. ≙ Optimisation; Sim. ≙ Simulation; x ≙ existent; - ≙ not existent; * ≙ implemented in TIMES.

consideration of the “technology mix effect”, as explained in Section 2, is also evaluated.

1.3. Own contribution The above discussion of previous studies and the summary presented in Table 1 represent the motivation and have defined the model design for the present contribution. From this overview, it becomes clear that up to now no energy system model for the decentralised heat system with a national focus exists that satisfies the following criteria:
Model application to Germany,
Study horizon to 2050 and therefore accordance with quantified energy and climate targets,
Focus on energy and climate targets of the heat system of the residential buildings within the goal assessment,


Optimisation approach and investigation from a normative perspective,
Integration with the electricity system for a model-endogenous integration of the repercussions on the heat system,
Integration of object-based (innovative) mCHP technologies, e.g. fuel cells and Stirling engines,
Consideration of model endogenous building retrofit measures, which allow for a coherent assessment of the interactions between the heat technologies and the building envelope in the heat system,
Integration of a high level of modelling detail of the heat supply systems,
Consideration of the technology mix effect in the heat system. To address the identified research gap, an approach is developed to analyse and assess the electricity and heat system of residential buildings in Germany. The approach is rooted in model-based energy system analysis and relies on the coupling of

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partial models which address certain aspects of the investigated energy system. Therefore, an in-depth analysis is enabled which from a normative perspective allows the investigation of the evolution and the optimisation of the heat system of residential buildings in Germany up to 2050. For the first time to the authors’ knowledge, an analysis is conducted that focuses explicitly on the evaluation of energy-political targets, i.e. on primary energy demand and share of renewables, in the residential building sector by means of the example of Germany. Thus this approach is suitable for addressing questions like the following: How can the development of the heat system of the German residential sector as well as its degree of achievement of energy and climate targets in a reference evolution be evaluated? What is the target heat system of the German residential sector like under minimal costs? What is the relative contribution of individual heat technologies and building retrofit measures to meeting national climate and energy targets? How can the (economic, technical, environmental) trade-offs between competing technologies and their combinations to heat systems in a system context be assessed? How can the techno-economic potential of emerging technologies at the interface of electricity and heat, e.g. mCHP plants and heat pumps be evaluated? The remainder of the paper is organised as follows. In Section 2 the methodology underlying the analysis is presented. For this, a model-based approach that relies on the coupling of a simulation and two optimisation models is outlined. Section 3 aims at deriving and discussing results obtained from the model runs. Therefore, scenarios are first defined and data assumptions for the model runs set out that lead to the model results which are further contrasted in the scenario analysis and finally discussed and critically appraised. The paper closes in Section 4 with a summary.

2. Methodology In this section the methodology underlying the analysis is presented. First, an overview of the modelling approach based on the coupling of three partial models is given. Subsequently, the modelling of energy and climate targets of the residential heat sector in the TIMES-HEAT-POWER model is explained. 2.1. Overview of the modelling approach Fig. 1 gives an overview of the modelling approach developed in this study. The model coupling is motivated by increasing detail in modelling key aspects of the residential heat system by outsourcing these aspects to upstream models, thereby decreasing model complexity and solution time. More precisely, these aspects refer to the evolution of the residential building stock both in quantity and energetic properties (e.g. renovation standard) as well as to the optimal sizing of the individual components of decentralised heat systems, both of which constitute a complex system and planning task in themselves. As a result, the soft-linking of the individual models enables a much more detailed technical and economic depiction of partial energy systems than would be feasible in one larger model. The TIMES-HEAT-POWER model is situated at the core of the modelling. It constitutes a bottom-up, technology-driven, partialequilibrium, multi-periodic optimisation model that encompasses the electricity system and the residential heat system in Germany. It further determines the technology choice in both partial systems up to the year 2050 under the premise of least total discounted system cost. For this, various heat technology systems including heat pumps, biomass-fired boilers and mCHP systems are

Fig. 1. Overview of the modelling approach.

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implemented. Moreover, thermal insulation measures are implemented as energy saving measure in TIMES-HEAT-POWER. As such, they are characterised by the level of conserved space heat as well as their associated cost. The individual measures target the buildings’ walls, floors, ceilings and windows and are further aggregated to a bundle of measures yielding three different insulation packages instead of considering each measure individually. These packages are also distinguished by their effectiveness, i.e. the amount of conserved energy as well as level of investment cost in ascending order. For a detailed model description and application in different contexts the reader is referred to the appendix as well as to Fehrenbach et al. (2014), Merkel et al. (2014) and Merkel (2016). For the 140 residential building demand classes, the TIMES-HEATPOWER model receives input from two other partial models, as detailed in the following. A model of building stock and energy demand is soft-linked with the TIMES-HEAT-POWER model. The former simulates the demand for space heat and domestic hot water for the German residential building stock up to the year 2050. It is based on a bottom-up analysis of the German residential building stock according to quantitative and energetic criteria of various residential building categories. For this, the building energy model firstly incorporates the dynamics of the evolution of the building stock, accounting for rates of building demolition and new build. It also integrates the level of area-specific heat demand and its development due to building retrofit measures and higher energetic building standards. The evolution of the thermal demand for residential buildings in Germany is fed from the building stock model into the TIMES-HEAT-POWER model, thus parametrising the demand restrictions. Hereby, the demand reductions are applied to all building classes in an equal way.2 For further information on the model of building stock and energy demand the reader is referred to the appendix and McKenna et al. (2013). Additionally, a model of decentralised heat systems is coupled with the TIMES-HEAT-POWER model. The least-cost dimensioning and dispatch of individual technologies as components of predefined heat systems for selected combinations of heat systems and building archetypes of the demand classes is carried out in this model. Resulting capacities of the system components are transferred to the downstream model (cf. Fig. 1). In TIMES-HEAT-POWER individual heat technologies and storages are aggregated to systems by relating their respective capacities and storage volumes using fixed ratios. These capacities are determined in the TIMESHEAT-POWER runs being constrained by these capacity ratios. Computationally, this is ensured by inserting linear relationships between the capacities of the individual technologies implemented as a special TIMES feature, the so called user constraints, in addition to the standard code in TIMES. In the respective linear equations, the coefficients indicating the technologies’ capacity ratios are determined a priori by the model of decentralised heat systems. The pre-dimensioning proves especially useful for decentralised cogeneration systems which are comprised of a mCHP unit, a supplementary gas boiler and where applicable a solar thermal system. The accurate sizing of such systems depends on a variety of constraints relating to, inter alia, operational characteristics and model-exogenous parameters including electricity prices and remunerations. These are incorporated in the upstream model at a level of detail that would not be possible in equal measure in

2 This assumption could be critically questioned as demand reductions might differ across e.g. building size categories. However, due to the incongruity of the building classes in the model of building stock and energy demand and TIMESHEAT-POWER as set out in the appendix, this approach is deemed as the best possible procedure.

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the downstream model. The sizing of the heat technologies and storages of the systems could also be carried out and has indeed been undertaken in preliminary versions in the downstream model TIMES-HEAT-POWER. However, this proved to be at the cost of integrating less modelling detail due to, amongst other things, to a coarser time resolution, a lack of considering operational characteristics, as well as at the expense of a difficult to manage solution time of the model runs. Therefore, the complex task of the (pre-)dimensioning of heat systems, which however is a crucial step in accurately modelling decentralised heat supply systems, is outsourced to the upstream capacity model. For further information on the decentralised heat system model the reader is referred to the appendix as well as to Merkel et al. (2015) and Merkel (2016). A unique aspect of the modelling approach is the methodological extension of TIMES-HEAT-POWER rooted in mixedinteger programming. This is ensured by providing decision variables and constraints additional to the code of standard TIMES as detailed in Merkel et al. (2014). This procedure ultimately serves to eliminate the technology mix effect in the decentralised residential heat system by deriving single indivisible heat systems. This effect describes the finding that, given a non-uniform demand profile and supply technologies characterised by opposing trends in operation-time-dependent and operation-time-independent cost elements, the cost-minimal capacity and dispatch optimisation yields a mix of technologies for base-, medium and peak load operation that is not necessarily realistic in a residential heating context, and thus not desirable. For an in-depth description of the extension of the TIMES model generator for the avoidance of the technology mix effect see Merkel et al. (2014). 2.2. Modelling of energy and climate targets of the heat sector in TIMES The German residential heat system is assigned a particular focus in the Energy Concept, including targets of reducing the heat demand in buildings up to the year 2020 as well as the primary energy demand until 2050 (see Introduction). In addition, a minimum share of renewable energy sources (in building heat consumption) is set in the year 2020 and 2050. In this section it is shown how the specified energy and climate related goals are mathematically formulated and integrated into TIMES-HEATPOWER. 2.2.1. Reduction of the primary energy demand The primary energy demand is calculated on the basis of energy input flows over all existing and newly built energy conversion units over every time step ts (all symbols are defined in the nomenclature). In the prevalent network structure of the TIMES model the total primary energy demand is equally represented by the sum of the energy flows as output from the energy source process that are eventually transferred into final energy (cf. Eq. (2)). At target conformity according to inequality Eq. (1) the total primary energy demand has to be equal or less than the given level of maximum primary energy demand for the specified model year t. The primary energy target level TARPE t is hereby calculated ex-ante and model-exogenously by reducing the primary energy demand of the residential buildings of the reference year 2008 by 80% until the year 2050. In the model years in between, the targets are linearly interpolated. The primary energy requirement is calculated according to equation Eq. (2) with the help of the final energy demand of the existing and new installed heat supply technologies, multiplied by the specific primary energy factor PEFc;t . The latter is dependent on the used fuel as well as time.

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XXX p

c

FIN xp;c;t;ts $PEFc;t  TARPE t

c p 2 PH ; c 2 CFE ; t 2 T; ts 2 TS

ts

FIN xp;c;t;ts $PEFc;t ¼ xpFOUT 0 0 ;c ;t;ts

c p2PH ; p0 2 PSRC ; c 2 CFE ; c0 2 CPE ; t 2 T; ts 2 TS

2.2.2. Increase of the share of renewable energy sources To consider the goals for the minimum shares of renewable energy sources (RES) in the final energy demand of the heat system, a number of restrictions are inserted in the mathematical programme, which express the target levels. Therefore the minimum share TARRES is determined for every model year modelt exogenously. It is derived from the Energy Concept of the Federal Government, which foresees an increase to 14% in the year 2020 and 60% in the year 2050. In the resulting inequality Eq. (3) the final energy based on renewable energy sources consumed by the corresponding heat conversion technologies is put in relation to the consumed energy based on non-renewable sources in the respective technologies. This is realised by so called user constraints, a special feature that establishes a linear relationship between variables by specifying additionally implemented constraints, in the TIMES developing environment (Loulou et al., 2005).

TARRES t $

XXX p

c

ts

 XXX  FIN $ xp;c;t;ts  1  TARRES xpFIN 0 ;c;t;ts t p0

c

ts

0

c p2PCON ; p 2 PEE ; c 2 CFE ; t 2 T; ts2TS

(1)

(2)

the investigated time horizon. Therefore the specific investment of the analysed mCHP plants, i.e. (natural gas-driven) internal combustion engines, fuel cells and (solid biomass-fired) Stirling machines are depicted as well as the investment of heat pumps. As central parameters for the parametrisation of the electricity system Fig. 3 further plots the assumed evolution of prices for CO2certificates and installed capacities of RES-E technologies.3 Finally, Table 2 depicts pathways of the evolution of space heat and domestic hot water demand in residential buildings in Germany which are derived from the model of building stock and energy demand (McKenna et al., 2013). Altogether, three main pathways are identified, a low, medium and high variant, whereby the former two are derived from the energy building stock model and the latter from Kirchner et al. (2009). Moreover, the pathways of wholesale prices and retail prices for energy carriers in private households are detailed in Tables B.1 and B.2. Altogether, three variants of price evolution are identified for the scenario runs. For the assessment of target achievement it is essential to also define assumptions with regard to primary energy factors and CO2-emission factors, as outlined in Table B.3.

(3)

3. Results and discussion In this section results from the modelling approach are presented that are derived from the running and soft-linking of the individual models with the TIMES-HEAT-POWER model. As this is the downstream model (cf. Fig. 1), only results from this model are presented here (for intermediate results the reader is referred to the respective sources in Fig. 1). For this, firstly assumptions on the model data for the model instances regarding technical, economic and environmental aspects are set out. Secondly, a scenario framework is defined in which results are contrasted. Subsequently, results from the model runs are presented. The section closes with a discussion of results and a critical appraisal of the methodology and results. 3.1. Data assumptions and scenario definition This section details key data assumptions that are defined as parameters for the instances of the model runs. These further lay the foundation for the subsequent scenario definition. 3.1.1. Data assumptions Key assumptions with regard to the technical, economic and environmental parameters have to be defined for the model runs, as set out in this section. In this regard Fig. 2 depicts the assumptions for the evolution of the specific investment of selected technologies for the residential heat system, for several plant sizes over

3.1.2. Scenario definition For the scenarios underlying the model runs, on the one hand explorative scenarios, which are also called descriptive and indicative scenarios, are defined. On the other hand one target scenario, which is also denominated as prescriptive or normative, is formulated (Mai et al., 2013). This overall differentiation in scenario definition in the present study is derived from the predominant focus on the assessment of climate and energy targets for the German residential heat system and the associated assumption that these targets are unlikely to be (exactly) reached in a reference scenario. In the explorative scenarios, the evolution of the German electricity system and the heat system of the residential buildings is identified under expected changes of the boundary conditions, yet open-ended and irrespective of the evolution (Dieckhoff et al., 2014). This procedure is thus also termed forecasting (Grunwald, 2011). More specifically, the evolution in the explorative scenarios is subject to a set of framework parameters identified as essential for the investigated systems’ temporal development. Taking into account the relevance of the aggregate of certain boundary conditions, the explorative scenarios might also be considered as summative scenarios. In the Target Scenario however, a normative approach is pursued, whereby desired target states of the system

3 It should be noted that the increase in wind-onshore capacity is actually lower in the high variant than in the medium variant. However, due to the capacities of all RES-E technologies being greater in total in the high variant and to be consistent with the corresponding studies, this fact is accepted. Moreover, the suggested level of wind-onshore capacity in 2020 (39.0 GWel) is already reached by the present level (41.2 GWel in 2015, cf. BMWi (2016b)) but again kept for reasons of (study) consistency.

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Fig. 2. Assumptions for the evolution of the specific investment of selected technologies in the residential heat system (source: own assumptions based on ASUE (2014)).

Fig. 3. Assumptions for the evolution of the prices for CO2-certificates (left) and the installed capacities of RES-E technologies (right) (source: own illustration based on Faulstich et al. (2011); Nitsch et al. (2012); BMWi (2016b)).

Table 2 Variants of the evolution of space heat and domestic hot water in residential buildings in Germany (source: own calculation based on Kirchner et al. (2009) and McKenna et al. (2013)). Variant Description of variants

Demand reduction


Very moderately-strengthened energetic minimum standards to new build and modifications of existing buildings by regulatory framework 0.5% p.a. conditions (e.g. future versions of EnEV)
In accordance with the evolution of the lower variant in McKenna et al. (2013) Medium
Moderately-strengthened energetic minimum standards to new build and modifications of existing buildings by regulatory framework 1% p.a. conditions (e.g. future versions of EnEV)
In accordance with the evolution with assumptions of Schlesinger et al. (2010) in McKenna et al. (2013) High
Strongly-strengthened energetic minimum standards to new build and modifications of existing buildings by regulatory framework 1.25% p.a. conditions (e.g. future versions of EnEV)
In accordance with the evolution in Kirchner et al. (2009) Low

are defined and thus possible evolutions identified that can be attained by departing from the present (Dieckhoff et al., 2014). Analogously, this procedure is termed backcasting (Grunwald, 2011). In the Target Scenario energy and climate goals relating to the partial energy systems are thus defined as boundary conditions. They are implemented as constraints which have to be fulfilled

under problem solvability. In the model runs it is thus determined how these goals are achieved, i.e. which technologies and measures are chosen. Based on this reasoning, Table 3 defines the Reference Scenario which constitutes the foundation of the scenario analysis. Hereby, relevant attributes and their characteristic are given based on the

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Table 3 Definition of the reference scenario (REF_A). Parameter

Variant

Fuel price CO2-prices RES-expansion Demand for electricity Demand for SH and DHW Technological progress

Medium variant Medium variant Medium variant Stagnation Low demand reduction Innovation (Carbon, Capture and Storage (CCS) technology available, regression of investment of CHP technologies and heat pumps amounting to 1.5% p.a.)

scenario framework by pointing out the investigated parameters and their variants as well as the scenario name. 3.2. Results from the reference and target scenario Fig. 4 depicts the evolution of the final energy demand in the heat system of the German residential sector in the Reference and Target Scenario until 2050. The figures further distinguish the final energy carriers. It can be seen for both cases that the absolute level of final energy demand is declining. More precisely, the total final energy

Table 4 Additional restrictions in the Target Scenario. Criterion

Description of variant

Primary energy

Reduction of primary energy demand of residential buildings by 80% by 2050 compared to the level of 2008 based on the total primary energy factor Minimum share of renewable energy sources in the gross final energy consumption of heat supply of residential buildings in the amount of 60% in the year 2050 Reduction of gross electricity consumption by 25% by 2050 compared to the level of 2008

Share of RES Gross electricity consumption

outlined numerical assumptions. Particularly, the Reference Scenario belongs to the category of explorative scenarios. It is crucial for the studied scenarios to identify the determinants which have the greatest impact on the evolution of the investigated energy system. As such, in a preliminary study the fuel and CO2-certificate prices, the expansion of RES-E technologies, the demand for electricity as well as for space heat and domestic hot water and the technological progress of selected technologies were identified as the most influential parameters. Moreover, it is the overall objective of the design of the Reference Scenario to identify likely trends of these determinants in an expected reference evolution. Therefore, the characteristic of the first three mentioned parameters is based on the reference evolution in an up to date comprehensive study for the evolution of the energy system in Germany identified in Nitsch et al. (2012).4 In accordance with the mentioned study the development of the electricity demand is assumed constant over time. The evolution of heat demand is however assumed to decline according to the reference evolution in the model of building stock and energy demand (cf. McKenna et al., 2013). Furthermore, the technological progress is based on related work in the field of modelling of electricity and residential heat energy systems. For the investigated normative scenario, additional constraints are formulated and implemented in the model instances of TIMESHEAT-POWER. The additional constraints stem from the Energy Concept of the German Federal Government which directly relate to the electricity sector or the building heat sector with a time horizon of 2050.5 According to Table 4, these relate to the primary energy demand in residential buildings, share of renewable energy sources and evolution of the electricity demand. Based on identified criteria that mainly influence the investigated energy system, further (explorative) scenarios are defined. These differ from the Reference Scenario by a variation of at least one parameter. Table 5 gives an overview of the thus derived

4 It should be noted that this study is termed the “reference study” in Germany, and as such is the main and most up to date study of its kind. It is used by Government, policy makers and academics as the most consensual depiction of the development of the energy system to 2050, and for this reason is employed here. 5 Please note that in the Concept also further energy and climate goals are defined which however are referred to a different sector, e.g. the transport sector or have a cross-sector relevance, e.g. emission reduction.

demand for heat consumption in the national residential buildings in 2050 amounts to approx. 1400 PJ/a in the Reference Scenario whereas it is approx. 810 PJ/a in the Target Scenario, which corresponds to a reduction of approx. 37% and 64% based on the level of 2015 respectively in the two scenarios. The more pronounced decline in the Target Scenario can be ascribed to two factors: firstly, this scenario is per se characterised by a greater exogenous reduction of heat demand of 1.25% p.a. versus 0.5% p.a. in the Reference Scenario (cf. Tables 2 and 5). Second, the deployment of endogenous retrofit measures is more developed in the final year (cf. Fig. 6). Whereas in the Reference Scenario the fuel mix is dominated by fossil energy carriers final energy consumption is increasingly based on renewable energy carriers in the Target Scenario. This is equivalent to a considerable reduction in the use of fossil energy carriers by 2050. In the Target Scenario a technology shift to a system predominantly based on heat pumps and solar thermal systems can be observed. It is important to note here that the balancing rules for final energy in this study foresee ambient heat and solar irradiation to be integrated in final energy demand, which might be excluded according to alternative accounting principles. This is primarily due to reasons of comparability with similar studies which employ the same principle (cf. discussion of results). On the one hand, this is due to the total primary energy factors of ambient heat and solar irradiation being the lowest among the energy carriers in the heat system (cf. Table B.3) and consequently contributing least and therefore most beneficially to primary energy consumption and the primary energy target in the residential heat system respectively.6 On the other hand, ambient heat and solar irradiation are renewable energy carriers and thus favourable to achieving the renewable target (cf. Table 4). In the final year that is crucial to target achievement the consumption of solar irradiation is reduced to a minimum (approx. 10 PJ/a) so that solar thermal plants with an associated total primary energy factor equalling to 1 (cf. Table B.3) do not add to primary energy consumption significantly. Instead, heat pump only systems without

6 According to Table B.3 the total primary energy factor of district heating is the lowest. However, the development of district heating is assumed model-exogenous with its share in final energy demand being constant over time (approx. 10%). District heating is thus exempt from model-endogenous investment decisions and hence from an added beneficial contribution to the primary energy target.

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Table 5 Definition of a scenario framework. Parameter

Variant

Name

Fuel prices

Low High Low High High Medium decrease High decrease Decrease CCS non available Constant investment CHP Constant investment heat pump Exogenously given Exogenously given

REF_F1 REF_F2 REF_F1 REF_F2 REF_R REF_D1, REF_D3 REF_D2, REF_D4, TAR REF_D3, REF_D4, TAR REF_T4 REF_T1, REF_T3, REF_T4 REF_T2, REF_T3, REF_T4 TAR TAR

CO2-prices RES-expansion Demand for SH and DHW Demand for electricity Technological progress

Reduction of primary energy demand Minimum share of renewables

Fig. 4. Evolution of the final energy demand in the heat system of the German residential sector in the Reference and Target Scenarios.

solar thermal support are favoured in the final model years. This development is further accompanied by a significantly lower demand for space heat brought about by a greater deployment of thermal insulation measures (also cf. Fig. 6). In TIMES-HEAT-POWER an emphasis is put on the investigation of technologies at the interface of electricity and heat. Therefore, mCHP technologies are of special interest in the analysis. Hence Fig. 5 depicts the installed capacities of the considered mCHP-based heating systems over time, which each are comprised of one out of three CHP technologies, a supplementary gas boiler (GAS) and if applicable an additional solar thermal unit (SOL). It can be inferred that in the Reference Scenario mCHP-based heating systems show a significant techno-economic potential, peaking at approx. 130 GWth in 2030. At all times an internal combustion engine with a supplementary gas boiler and solar

thermal system is identified as the predominant cogenerating system. In addition, fuel cells with a supplementary gas boiler provide a potential up to 2045. In the Target Scenario on the other hand, the potential of cogeneration systems is significantly reduced compared to the reference case. In the peaking years 2025 and 2030, the maximum installed capacity amounts to approx. 11 GWth. This result is mainly due to the prevalence of heat pumps that are primarily selected for target achievement (not shown in the figure). In the Target Scenario and contrary to the Reference Scenario, Stirling engines are part of the technology mix among the cogeneration systems. This can be attributed to the comparatively low primary energy factor of the underlying energy carrier biomass, which more favourably contributes to the achievement of the energy and climate goals in the heat system compared to the other (gas-driven) mCHP technologies (cf. Table B.3). Moreover, although

Fig. 5. Cumulative capacity of mCHP-based heating systems in the German residential sector in the Reference and Target Scenarios.

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Fig. 6. Avoided heat demand by deployment of thermal insulation measures in the heat system of the German residential sector in the Reference and Target Scenarios.

both scenarios notably differ in absolute level of installed capacity, it becomes evident that decentralised cogeneration constitutes a transition technology for which the highest potential is identified in the mid-term horizon (around 2030). In Fig. 6 the avoided heat demand by deployment of thermal insulation measures in the heat system of the German residential sector in the Reference and Target Scenarios is illustrated. The graphs also differentiate between the three levels of insulation effectiveness. In the Reference Scenario the deployment of thermal insulation measures steadily increases over the time horizon. This means that an annual heat demand of approx. 150 PJ/a in 2030 as well as of approx. 320 PJ/a in 2050 is avoided due to building retrofits. These economically-motivated savings are in addition to the alreadyassumed decreased thermal demand of the new build in the context of the model of building stock and energy demand. On the contrary, in the Target Scenario, the increase of displaced heat demand through energetic measures relating to the building fabric is more modest throughout most years, with the exception of the year 2050. Therefore, in 2030 the saved heat demand amounts to approx. 81 PJ/a across all levels of effectiveness but peaks in 2050 at approx. 350 PJ/a. This evolution can be ascribed to two issues: firstly, the overall exogenous demand reduction is assumed to decline more steeply in the Target Scenario (0.5% p.a. in the Reference and 1.25% p.a. in the Target Scenario) which reduces the necessity of endogenous insulation measures correspondingly. Secondly, the overall premise of the Target Scenario is the goal achievement, i.e. the reduction of primary energy demand and the increase of the share of renewables. This is primarily realised by technology change (cf. Fig. 4) but requires an additional effort in the

final year, yielding an avoided energy consumption by heating technologies. Therefore, final energy induced by solar irradiation is reduced to a minimum in 2050, as with the assumed total primary energy factor it contributes to primary energy consumption in the amount of final energy (cf. Table B.3). In this regard, deployment of thermal insulation measures is crucial for the feasibility of the model instance and therefore goal achievability in the Target Scenario. The CO2-emissions based on the direct and total emission factor in the heat system of the German residential sector and both partial energy systems in the Reference and Target Scenarios are outlined in Fig. 7. As can be concluded also from this figure, the CO2-emission level constantly decreases over the period under observation for both energy systems investigated as well as for the two balancing principles (direct and total emissions). For the heat system, emission levels based on the total emission and direct factors are reduced by approx. 58% and 68% up to 2050 based on the level of 2015 for both balancing variants respectively. In the Target Scenario the corresponding figures for CO2-emission amount to approx. 80% and 98% respectively. The rigid reduction is due to the high penetration of heat pumps and solar thermal plants, the direct and total emission factors of which are significantly lower than those of the conventional gas and oil boilers prevalent in the Reference Scenario to a noticeable extent (cf. Table B.3). Also the allocation principle is part of the sharper reduction, by which the CO2-emission adherent to the electricity consumed to drive heat pumps is allocated to the electricity system following the internationally agreed source principle (cf. Matthes et al., 2013; Stengel, 2014). In addition, Fig. 8 illustrates the evolution of the primary energy

Fig. 7. CO2-emissions based on the direct and total emission factor in the heat system of the German residential sector and both partial energy systems in the Reference and Target Scenarios.

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Fig. 8. Evolution of the primary energy demand based on the non-renewable and total primary energy factor in the heat system of the German residential sector and both partial energy systems in the Reference and Target Scenarios.

demand based on the non-renewable and total primary energy factor in the heat system of the German residential sector and both partial energy systems investigated in the modelling approach in the Reference and Target Scenarios. According to the terminology in the Intergovernmental Panel on Climate Change (IPCC) publications the method for accounting primary energy used in this study is the physical energy content method (Moomaw et al., 2011). For the heat system of the residential buildings, in both scenarios a significant decline can be observed, meaning that departing from a primary energy consumption of approx. 2400 PJ/a based on the non-renewable factor and 2500 PJ/a based on the total factor in 2010 in the Reference Scenario the primary energy consumption is reduced by approx. 73% and approx. 48% in the year 2050, assuming the two calculation variants respectively. The significant spread between the figures in the final year is due to the total primary energy factors surpassing the non-renewable factors for every energy carrier (cf. Table B.3) whereby the difference is even greater for renewable energy carriers. In conclusion, the primary energy target equalling a reduction of 80% compared to the level of 2008, is missed in the Reference Scenario for both calculation principles. On the other hand, the solvability of the model run of the Target Scenario implies the feasibility of the achievement of energy and climate goals in the heating sector (cf. Fig. 4). Hereby, the primary energy demand is diminished to approx. 500 PJ/a based on the total primary energy factor which exactly coincides with the requested target and to approx. 71 PJ/a, assuming non-renewable factors. The reported spread between the figures of different calculation principles is considerably greater than in the Reference Scenario as the target heat system is more substantially based on

renewable energy carriers with an inherently larger spread of factors (see above). For the national electricity and residential heat system analysed altogether the primary energy demand has a decreasing evolution both for the two scenarios and the calculation principles. In the Target Scenario the decline is more pronounced which can be traced back to the required target achievement in the residential heat system and the electricity demand being lowered by 25% up to 2050 (cf. Table 4). Accordingly, the share of renewable energy in the final energy demand of the heat system of the German residential sector in the Reference and Target Scenarios is depicted in Fig. 9. As can be seen from both scenarios, the share is augmenting over the time horizon, reaching approx. 45% in 2030 and approx. 49% in 2050 in the Reference Scenario. Thus, whereas the renewable target for 2020 (14%) is reached the defined target for the minimum share in 2050 (60%) is clearly missed, as is the case for primary energy consumption. On the contrary, in the Target Scenario the threshold value is reached and surpassed achieving approx. 85% in 2030 and 2050. The slight decline in 2050 can be explained by the uptake of energetic retrofit measures substituting thermal production from solar thermal units (cf. Figs. 4 and 6). 3.3. Results from the scenario framework This section contrasts various essential model results within the defined scenario framework. Therefore, Fig. 10 illustrates the sensitivity of the energy output of the mCHP systems in 2030 and 2050 as determined in the model runs of the scenario framework. The sensitivity analysis reveals a pronounced dependence of

Fig. 9. Share of renewable energy in the final energy demand of the heat system of the German residential sector in the Reference and Target Scenarios.

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Fig. 10. Sensitivity of the energy output of the mCHP systems in 2030 and 2050 in the scenario framework (for scenario names cf. Table 5).

the energy output of the cogeneration units on the varied parameters. For the year 2030 an energy output of approx. 130 TWh/a is determined of which approx. 38 TWh/a are allocated to electricity and approx. 89 TWh/a to heat production. In 2050 the corresponding figures amount to 8 TWh/a of electric and 20 TWh/a of thermal production. The fuel and CO2-certificate prices can be identified as those parameters having the strongest impact on the energy output of mCHP systems compared to the reference evolution in the mid-term. Here the lower variant favours the generation from CHP yielding a nearly doubled energy output. Conversely in the higher variant the production is roughly halved. With respect to a more ambitious expansion of RES-E technologies the scenario REF_R reveals lower and higher levels in 2030 and 2050 respectively compared to the Reference Scenario (decline by 22% and rise by 9%). Here the role of the cogeneration units as superpeaking technologies for balancing fluctuating RES-E technologies becomes evident. A significant impact on the energetic output of the mCHP technologies is further encountered for the different pathways of demand reduction. Thus, the model-exogenous reduction of the thermal demand by 1.0% p.a. (scenario REF_D1) and 1.25% p.a. (scenario REF_D2) implies a generation of electricity and heat diminished by about 20% in 2030 and 51% in 2050 in scenario REF_D1, and by 26% and 88% in scenario REF_D2. Likewise, an additionally decreasing electricity demand in the scenarios REF_D3 and REF_D4 favours cogeneration in 2030 but disfavours it in 2050 compared to the scenarios of solely decreasing exogenous heat demand (scenarios REF_D1 and REF_D2). Moreover, the differences in technological progress show that the lack of an investment regression for the mCHP units brings about a significant production reduction in 2030 and 2050 compared to the Reference Scenario (cf. scenario REF_T1). On the other hand, missing economies of scale for heat pumps favours cogeneration, leading to an increased output in 2030 and 2050 (cf. scenario REF_T2). For constant investment of both technologies in scenario REF_T3 the generation is situated in between the former described scenarios. Moreover, the additional unavailability of CCS technologies in scenario REF_T4 has a positive impact on energy output from mCHP with a considerable uptake in 2030 and 2050. According to the previous findings (cf. Fig. 5) the diffusion of mCHP plants is very limited in the Target Scenario due to the strong constraints. Therefore, the cogeneration amounts to only about 13

TWh/a in 2030 and is phased out in 2050. Fig. 11 further sets out the sensitivity of the electricity consumed by heat pumps in 2030 and 2050 in the scenario framework. From the figure it is apparent that also the electricity consumption is significantly influenced by the parameters that are modified between the respective scenarios. Contrary to cogeneration, alternative evolutions of fuel and CO2-certificate prices only have minor impacts on the electricity consumption for heat pumps. However, the more pronounced increase of RES-E technologies in the electricity system causes a considerable increase of the electricity consumption from heat pumps by approx. 47% in 2030 and 58% in 2050 compared to the reference value (cf. scenario REF_R). The different evolutions of space heat reduction impact the electricity consumption and thus the diffusion of heat pumps significantly, i.e. in the year 2030 this figure is lessened by approx. 40% in the scenarios REF_D1 and REF_D2 in comparison to the Reference Scenario. Only a slight deviation from these figures can be observed for the additional decline in electricity consumption in the scenarios REF_D3 and REF_D4. For the mid-term horizon (2030) the differences in technological progress do not have a significant impact on the electricity consumed for the use in heat pumps. However, in the year 2050, this consumption is substantially reduced (approx. 39%) compared to the value in the Reference Scenario, in the scenario REF_T2 which assumes a lack of specific investment regression for heat pumps. In accordance with the deployment of heat pumps in the Target Scenario (cf. Fig. 4) the electricity consumption is considerably elevated in the investigated years 2030 and 2050 (cf. Fig. 11). Finally, the scenario analysis is performed for the CO2-emissions of the residential heat system and the total energy system in 2030 and 2050 as shown in Fig. 12. Again, a significant dependency of the investigated variable on the parameter variations in the scenarios is observed. For the year 2030 the low variant of evolution of fuel and CO2-certificate prices (scenario REF_F1) results in an increase in direct CO2-emissions from the heat system of about 40% of the reference value. Against this, in the high variant (REF_F2) this figure is lowered by about the same amount. Furthermore, an observable reduction of the direct emissions of CO2 is brought about in scenario REF_R where a more ambitious expansion of RES-E technologies is assumed due to the increased installation of heat pumps. The reduction of heat and

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Fig. 11. Sensitivity of the electricity consumed by heat pumps in 2030 and 2050 in the scenario framework (for scenario names cf. Table 5).

Fig. 12. Sensitivity of the CO2-emissions in the residential heat system and the total energy system investigated in 2030 and 2050 in the scenario framework (for scenario names cf. Table 5).

electricity demand is also linked to an emission decrease as is apparent from Fig. 12. Concerning the scenarios involving technological progress, the lowered cogeneration in the scenario REF_T1 anticipating specific investment of CHP technologies held constant also implies total and direct CO2-emissions in the heat system reduced by approx. 15% and 23%. On the other hand, absent economies of scale for heat pumps (scenario REF_T2) result in a decreased deployment of heat pumps as well as increased CO2emissions. In the Target Scenario, CO2-emissions in the investigated heat system are distinctly cut. In the year 2050 the implications are in accordance with those for the year 2030. Thus, the emissions in scenarios with alternative evolution of fuel and CO2-certificate

prices strongly depend on the heat generation from mCHP and heat pumps, which in turn is significantly influenced by the variation of the former parameters. An increased penetration of RES-E technologies as well as a reduced demand for space heat and electricity also brings about an emission decrease (cf. Fig. 12). For the scenarios analysing technological progress, CO2-emissions in the heat system depend on the resulting production from mCHP and consumption by heat pumps respectively, inducing an increase and decrease of emission respectively. Finally, the Target Scenario results in almost zero direct emissions in the residential heat system brought about by the pronounced decline of thermal demand and deployment of heat pumps.

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3.4. Discussion This section discusses results from the TIMES-HEAT-POWER model. Selected key model results are put into context with findings from related studies in the field of model-based analysis of decentralised heat supply, where applicable and comparable, in order to examine their plausibility. The final energy consumption for heat generation in the residential sector constitutes a significant and characteristic model output of the investigated energy system (cf. Fig. 4). Regarding its evolution and absolute level, related studies with a comparable research focus yield similar results. In the Reference Scenario, Schlesinger et al. (2010) determine a reduction to approx. 1600 PJ/a in 2050, whereas Nitsch et al. (2012) identify approx. 1200 PJ/a and Schlesinger et al. (2014) approx. 1200 PJ/a. In this respect, the level of final energy consumption derived in the present study (approx. 1400 PJ/a, cf. Fig. 4) presents itself as average in the cross-study analysis. In the Target Scenario, a level of approx. 810 PJ/a in the year 2050 is attained in the TIMES-HEAT-POWER model (cf. Fig. 4), which is primarily due to the strongly-strengthened exogenous energetic standards for new buildings resulting in their overall lower demand for space heat (cf. Section 3.2). The determined figure is situated somewhat lower than the corresponding figure in similar studies. More precisely, Schlesinger et al. (2010) calculate final energy demand of approx. 1000 PJ/a in their target scenario. Against this, Schlesinger et al. (2014) yield approx. 1200 PJ/a. However, it has to be pointed out as a limitation of these findings that the energy-political targets imposed in the present study and those assumed in the cited studies are not fully congruent. Specifically, the primary energy reduction target in the heat system of the residential building sector is not found in the other studies (cf. Section 1.3). The uptake of mCHP technology in the mid-term horizon is another key finding of the present study. Thus, putting into context the diffusion of cogeneration plants and their associated electricity and heat generation with the potential determined in related studies derives further insights. For the year 2030 the cogenerated total energy output of approx. 130 TWh/a as a result of the Reference Scenario proves above average compared to other studies with a similar research focus. In this regard, Nitsch et al. (2012) determine a total electricity and heat generation by object-based CHP of approx. 32 TWh/a in 2030. For the year 2050, the determined total energy output of the mCHP units (approx. 28 TWh/a in the Reference Scenario) is in the range of the potential identified in Nitsch et al. (2012) and Wünsch et al. (2014). This amounts to approx. 30 TWh/a and 35 TWh/a for object-based cogeneration units in these studies, respectively. However, a caveat to these findings is that, in Nitsch et al. (2012) and Wünsch et al. (2014) as opposed to the present study, also centralised grid-connected heat supply (CHP-based district heating) is modelled endogenously and thus explicitly within the scope of the energy system analysis. Therefore, in view of the competitive situation between the object-based and centralised CHP options, a substitution of the potential of decentralised cogeneration by centralised has to be assumed in these studies, which leads to a biased comparison of results with regard to decentralised mCHP. Overall, the generation from object-based CHP as a result from TIMES-HEAT-POWER is oriented towards the trend in Nitsch et al. (2012), however. In conclusion, the character of CHP as a bridging technology in the energy transition of the heat system identified in the present study agrees with these related studies. The share of renewables in heat supply is another key model outcome that should be compared with similar studies. In the Reference Scenario, Nitsch et al. (2012) derive a figure of approx. 53% in 2050, whereas Schlesinger et al. (2010, 2014) identify a share

of approx. 34% and 30% respectively. The share of renewables amounting to approx. 49% in TIMES-HEAT-POWER (cf. Fig. 9) is thus situated in between the comparative studies. In the corresponding target scenarios in Schlesinger et al. (2010, 2014) the share of renewables is calculated to approx. 49% and 51% respectively, and thus considerably below the share derived in the present study (approx. 85%). However, the differences with respect to an explicit minimum target share for renewables in the residential heat sector, which is incorporated in TIMES-HEAT-POWER but neglected in the other studies, again constitute a limitation to the interpretation and comparison of these findings between studies.

3.5. Critical appraisal and further work In order to evaluate the derived results set out in the previous sections the characteristics of the modelling approach have to be critically discussed. Therefore this section focuses on key aspects of the discussion and gives an outlook on further work to overcome them. The boundaries of the investigated system are confined to the German electricity system as well as to the heat system of residential buildings only. This means that relevant aspects that drive the modelling dynamics and thus the results are excluded from the analysis in the modelling. More precisely, this relates to the energy system of neighbouring countries and the integration of other partial energy sectors like the tertiary sector. Whereas by considering the former, international exchange of electricity and therefore additional demand and supply could be made part of the investigation, incorporating the latter would result especially in accounting for the prevalent heat supply and demand structure besides the one for the sector of private households. In the tertiary sector also a considerable amount of low temperature heat is consumed (690 PJ/a in the tertiary sector vs. 1800 PJ/a in the sector of private households in 2014 based on BMWi (2016a)). In this regard, also centralised heat supply could be integrated into TIMESHEAT-POWER and thus made model-endogenous, as the focus until now has only been on decentralised heat supply systems. Therefore the expansion of district heat networks could be a model result itself and thus compete with decentralised systems.7 In this respect, the role of district heat as a key decarbonisation strategy as identified e.g. in Connolly et al. (2013) could be adequately assessed. In this regard, also the obtained results especially pointing to the role of decentralised cogeneration and heat pump technology in the energy transition are limited to object-based decentralised heat supply only. They therefore clearly have to be critically assessed as these strongly depend on the assumption of the neglect of model-endogenous district heat in the developed approach. In this context also a regionalisation could be envisaged for the model by spatially referencing generation units and demand sectors. However, centralised heat supply only has a minor share in Germany's heat supply as of yet with district heating accounting for approx. 10% in total heat supply (BMWi, 2016a) (13% in Europe, cf. Introduction).8 Regionalisation could also be envisaged for RES-E

7 In fact, at previous stages of the model development it was investigated whether grid-bound heat supply could also be integrated into TIMES-HEAT-POWER. Eventually this proved infeasible as the detailed depiction of the decentralised heat system already uses existing computing capacity to a maximum and the implementation of district heat requires the introduction of spatial aggregation units e.g. at the district or neighbourhood level as well as an extension of the demand classes, all of which exponentially drives up model complexity and solution time. 8 This share is nevertheless significant in the heat supply of Germany. However, due to the need of prioritisation of modelling key aspects of the investigated energy systems the focus has to be set on decentralised heat supply which accounts for the major share in Germany's residential heat supply (approx. 90%).

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generation units like PV and wind plants as well as for the building stock. Therefore, different climate conditions affecting irradiation intensity, wind speed or temperature differences could be accounted for. For this the model could either be split up into multiple regions using the multi-region structure of the TIMES model generator (Loulou et al., 2005). Alternatively, additional categories for the supply and demand units could be defined as for example realised in Blesl et al. (2004), in addition to the already existing ones that account amongst other things for the differentiation of building size, energy standard or infrastructure. However, in view of the resulting considerable increase in complexity and inherent model solution time by e.g. an increased number of demand classes or regions as well as not significantly contributing to the quality of results with respect to the present research questions, these areas of possible model extensions are deemed of secondary importance.9 Moreover, the modelling approach adopts the normative perspective of a central planner as well as the premise of cost minimality, and therefore a sole economic rationale in the decision making process. Therefore individual perceptions and behaviour are not integrated in the analysis. Incorporating individual stakeholders such as tenants, landlords or energy supply companies as well as their varying preferences and therefore decision motives other than profitability, such as contribution to environmental protection or technology pioneer role, whilst very challenging, would result in a more realistic abstraction of the situation and thus affect results. However, this is restricted by the chosen modelling approach of optimisation which by definition precludes aspects of bargaining or learning of agents which can be addressed in alternative approaches like agent-based or system dynamics modelling. For related work in the field of modelling decentralised heat supply based on a simulation approach the reader is referred to Steinbach (2013) or Stengel (2014). Despite the attractiveness of such simulation-based approaches to capture these behavioural aspects, the chosen optimisation approach is most suitable to address the set out research questions revolving around determining a target state of an investigated energy system under an optimal criterion, which different methodological approaches (including simulation) are not able to derive. On the other hand, Cayla and Maïzi (2015) attempt to address different categories of energy end-users in an optimisation model, but their approach is based on survey data for French households which is both laborious to collect and not freely available for Germany. Furthermore, the soft-linking of the individual models in the methodological approach should be critically acclaimed. In concrete terms, repercussions between the models cannot be taken into account as the information flow between them is unidirectional. Therefore, inconsistent evolutions of model data of the same type being represented by variables and parameters might be a consequence. In particular, the development of electricity prices and generation costs, which represent an exogenous assumption in the model of decentralised heat systems and an implicit model result in TIMES-HEAT-POWER respectively, do not interrelate and might thus be diverging. In this regard, the system marginal cost of electricity supply calculated as shadow prices from the electricity demand restriction in TIMES-HEAT-POWER, which therefore represents wholesale electricity prices, increases from 6.60 Vct/kWh in 2010 to 8.46 Vct/kWh in 2020. This increase coincides with that of

9 The optimisation problem defined in the model instances of TIMES-HEATPOWER has dependent on the applied scenario approx. 1.44 million rows, 1.27 million columns and 9.32 million non-zero elements as well as approx. 2000 binary variables. The solution time in the GAMS/CPLEX environment amounts to approx. 12e72 h in dependence on the scenario.

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the retail price of electricity for private households exogenously assumed for the model of decentralised heat systems (cf. Table B.2). However, whereas the implied wholesale electricity prices fall after 2020, primarily due to an increased electricity penetration from RES-E technologies, the retail price for residential customers continues to rise, as the deployment of these technologies is financed by levies imposed on end-consumers. Whilst this assumption might be critically challenged, it is considered beyond the scope of this work to analyse the development of end-user prices, including the respective contributions of RES-E fees, network charges, taxes etc., in detail. Finally, in the modelling of the residential building stock in TIMES-HEAT-POWER the information relating to renovation cycles, and thus points in time which primarily motivate energetic retrofitting of buildings, is neither contained nor linked to the residential buildings represented as demand classes in the model. This aspect constitutes a simplification of the reality and thus the considerable increase in the deployment of thermal insulation measures in 2050 in the Target Scenario a point of critical reflexion. It has to be questioned whether these measures would still be realised for economic reasons outside of the renovation cycles. However, this shortcoming is deemed minor as the detailed modelling of thermal insulation states of the buildings and corresponding measures is not a focus in TIMES-HEAT-POWER and attempts for improvements would result in a significant increase in complexity due to a multiplication of demand classes (see above). For a modelling approach focusing in detail on thermal insulation states of the residential buildings as well as their renovation cycles see for example Stengel (2014). In this context, also the lack of investment limits and growth constraints of technology deployment in TIMESHEAT-POWER has to be addressed. Such restrictions can be based amongst others on diffusion theory or socio-technical transition theory. Therefore, the chosen approach might be brought into question, as optimisation models (with a national focus) to some extent indeed integrate such constraints to better reflect reality. However, growth and investment constraints have intentionally been left out in the modelling approach as the technical and economic feasibility of the deployment of measures is deemed subordinate to the predominant analysis of general attainability of the energy-political targets, assuming that such possible diffusion obstacles might be ultimately overcome. 4. Conclusions This paper presents and applies a novel energy system analysis framework to the German residential building and electricity sectors, in order to analyse their possible future development in the context of stringent energy-political targets. The modelling approach consists of three soft-linked models: 1) a simulation of the residential building stock; 2) an optimisation of the capacity and dispatch of decentralised heat supply in individual buildings; and 3) an energy system optimisation model of the residential heat and electricity sectors. The principal contribution of this paper consists in the combination of these three models via soft-link, thereby enabling a much more detailed technical and economic representation of partial energy systems compared to what would be feasible in one larger model. The novelty lies in the unique model coupling, the level of detail thereby achieved, and the application of this approach in order to assess energy-political targets for primary energy demand and shares of renewables in the German residential building sector. For the first time as far as the authors are aware, the previously unconsidered technology mix effect is explicitly considered and eliminated from the analysis. The scenario analysis, including 13 distinct scenario variants, demonstrates the large differences in energy system development

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between a normative (target fulfilment) and an explorative (reference) scenario. In order to achieve the ambitious targets, including reducing heat demand in buildings by 20% in 2020, reducing primary energy demand by 80% by 2050 over 2008, and attaining a share of 60% renewables in heat consumption by 2050, a strong basket of supply and demand side measures is required. On the demand side, deep building insulation measures are required in order to significantly reduce the overall heat demand in residential buildings. Compared to the Reference Scenario, in which 320 PJ/a of heat demand are saved by insulation in 2050, the Target Scenario requires an additional 30 PJ/a of insulation. On the supply side, target achievement implies an almost complete phase-out of existing gas and oil boilers by 2050, in favour of a large scale rollout of heat pump and solar thermal technologies. Only in the Reference Scenario are substantial numbers of gas and oil boilers encountered alongside mCHP technologies (mainly gas-fired ICEs). The latter can be interpreted as a bridging technology until the electricity system is largely renewable and therefore the environmental attractiveness of heat pumps increases. Hence, the fraction of non-renewable primary energy demand in the residential heat system is almost completely eliminated in the Target Scenario, compared to a level of approx. 670 PJ/a in the Reference Scenario. The results point in the same direction as existing studies, although a direct comparison is challenging due to different system boundaries with respect to end-use sectors and centralised CHP plants, as well as different and changing energy-political frameworks. If these differences are qualitatively taken into account, there is a good agreement with previous findings for total mCHP development at the national level, but in the present case with a much higher level of technology and building differentiation. The results are sensitive, to varying degrees, to changes in the input parameters, as shown in the 13 scenario variants. The development of mCHP technologies is rather sensitive to the input parameters, especially the prices for energy carriers, CO2certificates and the level of specific investment regression, as well as to a lesser extent to the fraction of RES-E generation and the overall heat demand in residential buildings. Heat pumps are much less sensitive to CO2-certificate and fuel price evolutions, instead they are favoured in the case of higher RES-E penetrations and higher heat and electricity demand. The level and type of insulation employed is most sensitive to the overall heat and electricity demand, and somewhat surprisingly shows less sensitivity to other parameters. Finally, the CO2-emissions from the residential heat system are particularly dependent on the evolution of the fuel and CO2-certificate prices as well as the target achievement. The employed approach adopts a macroeconomic, centralised planner perspective with the optimisation criterion of minimal total system cost. Hence while the presented results are valuable for energy researchers and policymakers, they are subject to some caveats. In particular, this method is not able to capture socioeconomic aspects within individual households, such as differences in tenure, income, household structure etc., which all effect energy consumption and investment decisions relating to heating technologies (cf. Jones et al. (2015)). Whilst such aspects are generally best captured in simulation models (see for instance Steinbach (2013) and Stengel (2014)), first steps towards a better differentiation along these lines have been made in the French TIMES model of the residential sector (Cayla and Maïzi, 2015). Other limitations of the applied approach include the lack of spatial differentiation within Germany (which by definition means that district heating cannot be endogenously optimised), the lack of electricity exchange with neighbouring countries and the limited sectoral coverage, which are all due to necessary limitations on complexity in order to represent decentralised residential heat

supply in a high level of detail. In addition, by assessing the feasibility and consequences of existing energy-political targets, the present paper does not directly analyse whether these are appropriate for the residential sector in the first place. Hence, especially these aspects should be addressed in future work, perhaps in the context of additional model couplings.

Appendix A. Nomenclature Abbreviation Description approx. approximately AU Autumn CCGT Combined Cycle Gas Turbine CCS Carbon, Capture and Storage CHP Combined heat and power cf. confer CO2 Carbon dioxide DCH Demand class high DCL Demand class low DCM Demand class medium DHW Domestic hot water e.g. exempli gratia EnEV German Energy Saving Regulation (Energieeinsparverordnung) EPC Energy Performance Certificate eq. Equation EU European Union FC Fuel cell GAS Gas boiler GHG Greenhouse gas HVAC Heating, Ventilation and Air-Conditioning i.e. id est IEA International Energy Agency ICE Internal combustion engine IPCC Intergovernmental Panel on Climate Change LCT Low Carbon Technologies lMFH large multi-family house (m)CHP (micro-)Combined heat and power OECD Organisation for Economic Co-operation and Development OIL Oil boiler p.a. Per annum PV Photovoltaic REF Reference RES Renewable energy sources RES-E Electricity generation from technologies based on renewable energy sources SFH Single family house SH Space heat sMFH small multi-family house SOL Solar thermal plant SP Spring STIR Stirling engine SU Summer TAR Target TFH Two-family house TIMES The Integrated Markal EFOM System WD Weekday WED Weekend day WI Winter WI-OF Wind-offshore WI-ON Wind-onshore

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Table A.1 Symbols and description. Symbol

Description

c CFE Hi p PCON PEE PH PSRC PEFc;t

Index for commodity Set of commodities for final energy carriers Heating value Index for processes Set of processes for heat supply technologies based on fossil energy carriers Set of processes for heat supply technologies based on renewable energy carriers Set of heat supply processes Set of processes for the provision of primary energy carriers Primary energy factor of c in t Target of primary energy consumption in t

TARPE t

Target of the share of renewable energy carriers in t

TARRES t t T ts TS FIN xp;c;t;ts

Index for model years Set of model years Index for time slices Set of time slices Energy flow of c in p in t and ts

FOUT xp;c;t;ts

Energy flow of c out of p in t and ts

Appendix B. Numerical assumptions

Table B.1 Variants of the evolution of wholesale prices of energy carriers in V/GJ (source: own illustration based on Nitsch et al. (2012)). Variant

Energy carrier

2015

2020

2030

2040

2050

Low

Mineral oil Natural gas Hard coal Lignite Mineral oil Natural gas Hard coal Lignite Mineral oil Natural gas Hard coal Lignite

11.1 6.0 3.2 1.2 11.6 6.4 3.5 1.2 12.3 7.0 3.9 1.3

11.6 6.1 3.4 1.2 12.7 7.0 4.0 1.3 14.1 8.1 4.9 1.4

12.7 6.6 3.8 1.3 14.5 8.3 4.9 1.4 17.2 10.5 6.3 1.7

13.9 7.0 4.3 1.3 16.4 9.6 5.7 1.5 20.7 12.7 7.7 2.0

14.9 8.1 4.7 1.4 18.0 10.6 6.4 1.7 24.0 14.9 8.9 2.2

Medium

High

Table B.2 Variants of the evolution of retail prices of energy carriers for private households in Vct/kWh (source: own illustration based on Nitsch et al. (2012) and own assumptions). Variant

Energy carrier

2015

2020

2030

2040

2050

Low

Fuel oil (light) Natural gas Firewood/Pellets District heat Hydrogen Electricity Fuel oil (light) Natural gas Firewood/Pellets District heat Hydrogen Electricity Fuel oil (light) Natural gas Firewood/Pellets District heat Hydrogen Electricity

8.0 6.7 5.1 5.6 10.0 27.1 8.4 7.2 5.6 5.6 10.8 27.1 8.9 7.8 6.3 5.6 11.7 27.1

8.2 6.8 5.5 5.8 10.3 30.5 9.0 7.9 6.5 5.8 11.8 30.5 10.0 9.1 7.9 5.8 13.6 30.5

9.0 7.4 6.1 7.1 11.1 33.7 10.3 9.3 7.9 7.1 14.0 33.7 12.2 11.8 10.2 7.1 17.7 33.7

9.9 8.2 6.9 7.5 12.3 37.2 11.6 10.8 9.2 7.5 16.2 37.2 14.7 14.3 12.4 7.5 21.4 37.2

10.6 9.1 7.6 6.8 13.6 41.1 12.8 11.9 10.3 6.8 17.8 41.1 17.0 16.7 14.4 6.8 25.1 41.1

Medium

High

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Table B.3 Primary energy factors and CO2-emission factors (source: own calculation based on Fritsche et al. (2014); Stengel (2014)).

Electricity system

Residential heat system

Energy carrier

Primary energy factor [TJ/TJ]

CO2-emission factor [tCO2/TJHi]

Non-renewable

Total

Direct

Total

Natural gas Fuel oil (light) Solid biomass Hard coal Lignite Nuclear Water Solar Irradiation Ambient heat Wind Natural gas Fuel oil (light) Solar Irradiation Ambient heat Wooden biomass District heating Hydrogena

1.121 1.142 0.002 1.041 1.029 1.091 0.006 0.000 0.359 0.016 1.126 1.145 0.000 0.060 0.005 0.600 1.135

1.123 1.144 1.032 1.042 1.029 1.099 1.007 1.000 1.420 1.018 1.128 1.148 1.000 1.060 1.066 0.600 1.137

55.796 73.344 102,108 95.930 97.010 0.000 0.000 0.000 0.000 0.000 55.796 73.344 0.000 0.000 102.108 0.000 0.000

64.372 85.078 4.686 109.306 100.415 4.603 0.770 17.361 26.549 1.529 66.260 84.849 6.901 48.535 7.675 85.289 63.960

a It is assumed here that hydrogen is generated by decentralised reformation of natural gas which is transported by pipelines (Fritsche et al., 2014). An alternative scenario described in Fritsche et al. (2014) in which hydrogen is imported and produced on a large-scale in liquid form and transported in gaseous form after vaporisation is not part of the present analysis.

Appendix C. Description of model details TIMES-HEAT-POWER Fig. C.1 depicts the structure of TIMES-HEAT-POWER outlining most relevant model components. The heat system of the residential building sector as well as the national electricity system are modelled. In both, energy converting technologies are key model components as well as the demand for energy. In the electricity system the existing conventional thermal power plant park is implemented as well as RES-E technologies, both of which are not regionally differentiated but aggregated. In the heat system several individual heat supply options are provided that are derived from current heat technologies, e.g. a gas boiler, and promising future technologies, e.g. a fuel cell. In a further modelling step the individual technologies are aggregated to heat supply systems, i.e. a composite of them linking inter alia solar thermal systems or the

cogeneration systems to a primary technology, e.g. a gas boiler, as it is found in reality. Whereas in the electricity system the demand is aggregated over all sectors it is highly differentiated in the heat system. This is due to the heterogeneity of the residential buildings differing in amongst others building size and consumption level. Therefore, the total German building stock is repartitioned following the criteria set out in Fig. C.2. Furthermore, the heat supply systems are assigned to the respective demand classes according to infrastructural criteria (cf. Fig. C.2). Moreover, implementing the entire energy supply chain TIMESHEAT-POWER is able to account for energy over its various stages of transformation, i.e. from primary energy to useful energy. Consequently, primary energy demand and CO2-emissions are determined model-endogenously and might as decision variable thus be constraint by maximum levels.

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Fig. C.1. Overview of the structure of TIMES-HEAT-POWER.

Specifically, capacities of the technologies in the electricity and residential heat system as well as their temporal dispatch are determined model-endogenously as decision variables. These also include thermal insulation measures that are implemented and act as an energy-saving technology. On the other hand, several model components are determined model-exogenously implemented as parameters. Foremost, these refer to the demand for space heat and domestic hot water as well as electricity as fundamental model drivers. Moreover, the total of parameters that characterise the technologies in their technical, economic and ecological properties are provided ex-ante and thus exogenously. This also holds for the profiles of solar irradiation and wind. The system boundaries of TIMES-HEAT-POWER are defined at a national level, encompassing the German electricity and residential heat system. A particular focus of the model is on the disaggregation of the building stock. Thus, a typology of the building stock is required with respect to the buildings' properties as electricity- and heatconsuming objects resulting in a certain number of heat demand classes. This number is however situated between the poles of on the one hand capturing the most relevant criteria to differentiate the buildings with high selectivity and on the other hand the manageability of model complexity that considerably increases with an ascending number of demand classes. In this respect, several criteria characterising residential buildings with regard to their heat demand were investigated and among them the most

relevant in the authors' view selected. In this respect, the building size and the area-specific heat demand are deemed the most relevant to have an impact on building-specific space heat and domestic hot water demand. For the building size the four categories Single family house (SFH), Two-family house (TFH), small multi-family house (sMFH) and large multi-family house (lMFH) are distinguished in accordance with related building typologies. The heat demand expressed as area-specific heat demand is related to the living area and further divided into three demand levels representing different states of building insulation, heating habits etc. Combining these two criteria yields representative building types that across the building size and area-specific heat demand differ in per building heat demand. Cluster analysis identifies further five building demand classes which the derivation of heat demand classes is finally based on. With respect to investment decisions in new heat supplying technologies being a key element of TIMES-HEAT-POWER the buildings’ infrastructure pointing to the access to the gas and district heating network, the installed heat technology of existing buildings as well as its year of replacement are considered further most relevant criteria in characterising residential buildings in the light of electricity and heat consumption. Finally, the heat demand is distinguished by appliance meaning space heat and domestic hot water. Altogether, the classification amounts to 140 heat demand classes.

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Fig. C.2. Overview of heat demand classes in the modelling approach.

The temporal disaggregation of the time-dependent parameters on TIMES-HEAT-POWER is outlined in Fig. C.3. It can be observed that the time span of 5 years is represented by one model year over the time horizon up to 2050. In this respect, the model year represents an average year of investment and dispatch decisions. It can further be seen that the time structure follows a repartition in time slices of different length and frequency. In this regard, every model year is represented by the four seasons as well as the weekdays being differentiated between working day and weekend day. Moreover, every day is represented by a time interval of different duration. This is selected according to the variability of the parameters, i.e. thermal and electrical demand as well as wind and solar profiles.

the model is situated based on mixed linear integer programing. For a given combination of a defined decentralised heat system and residential building the total annual cost of heat and electricity supply are minimised in the objective function. The system boundaries are therefore defined by the residential building as opposed to the ones in TIMES-HEAT-POWER that encompass the partial energy systems on the national level. The optimal dimensioning is thus realised for every combination of heat supply system (cf. heat supply technologies in Fig. C.1) and building object according to the demand classes in Fig. C.2. Furthermore, the decision variables relate to the capacity of the heat supply technologies and heat storage as well as their temporal dispatch. Additionally, the emissions of CO2 and primary energy demand are accounted for.

Fig. C.3. Temporal disaggregation of TIMES-HEAT-POWER.

Model of decentralised heat systems Fig. C.4 gives a schematic overview of the methodological approach of the model of decentralised heat systems. At the core,

Technical, economic and ecological parameters to characterise technologies and buildings serve as input data to the model.

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Fig. C.4. Overview of methodological approach of the model of decentralised heat systems.

The temporal disaggregation of the model of decentralised heat systems is depicted in Fig. C.5. The temporal resolution is significantly higher than in TIMES-HEAT-POWER due to an overall lower model complexity. As can be seen from Fig. C.5 a chronological year is repartitioned in altogether 6048 time segments which was found in preliminary model runs to be a good compromise between a sufficient level of detail considered in the model runs and an

acceptable solution time. More precisely, data of three representative weeks of every season (spring/autumn, summer and winter) are integrated into the model amounting to 9 weeks representing a full calendric year. Moreover, every week is modelled by its days in chronological order. At the lowest level of temporal resolution, a day is represented by 15-min time steps.

Fig. C.5. Temporal disaggregation of the model of decentralised heat systems.

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Model of building stock and energy demand The schematic approach of the model of building stock and energy demand is set out in Fig. C.6

Fig. C.6. Overview of methodological approach of the model of building stock and energy demand.

In this modelling approach the evolution of the physical building stock is reconciled with the development of the thermal heat demand of the buildings in a bottom-up simulation model for the system boundaries of Germany. In this regard, the building stock is projected for each of the years between 2015 and 2050, determined by the endogenous variables building demolitions and new buildings whereby building addition and replacement is distinguished based on the building size categories that are also used in TIMESHEAT-POWER.10 Regarding the spatial resolution, the model also differentiates between the old and new Federal States in Germany, as projections of living area considerable differ between those. The projection of the building stock is further complemented by the projection of area-specific energy consumption for space heat and hot water in the second module of the model (cf. Fig. C.6). These projections are based on the pathways of evolution of regulatory framework conditions for energetic standards for new build and modification of the existing buildings. In the modelling approach the development of the demand for living space, the replacement rate of existing buildings as well as the specific energy demand for new buildings serve as main model-exogenous input parameters. In summary, the model of building stock and energy demand allows for trajectories of the evolution of space heat and domestic hot water demand for the residential building stock in Germany. These serve as input data for the parameterisation of heat demand for the demand classes in TIMES-HEAT-POWER. References Anandarajah, G., Strachan, N., 2010. Interactions and implications of renewable and climate change policy on UK energy scenarios. Energy Policy 38, 6724e6735. http://dx.doi.org/10.1016/j.enpol.2010.06.042. Assoumou, E., Maïzi, N., 2011. Carbon value dynamics for France: a key driver to support mitigation pledges at country scale. Energy Policy 39, 4325e4336. http://dx.doi.org/10.1016/j.enpol.2011.04.050. ASUE, 2014. CHP data 2014/2015, Working Group for Energy Conservation and Environmentally Friendly Energy Use (BHKW-Kenndaten 2014/2015, Arbeitsgemeinschaft für sparsamen und umweltfreundlichen Energieverbrauch e.V.). Berlin. Bartels, M., 2009. Cost Efficient Expansion of District Heating Networks in Germany. Oldenbourg Industrieverlag, München.

10 Please note that in TIMES-HEAT-POWER however the demand classes are based on the clustering of archetypes defined by building size and specific heat demand which is not the procedure in the model of building stock and energy demand. This ultimately leads to an incongruity of heat demand classes in the model of building stock and energy demand and in TIMES-HEAT-POWER and thus to an inaccuracy in the soft-linking process.

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