Modelling the impact of energy policies in the building sector The modelling tool Invert/EE- Lab

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1 2011 Modelling the impact of energy policies in the building sector The modelling tool Invert/EE- Lab Lukas Kranzl Andreas Müller Marcus Hummel Energy Economics Group (EEG) at the of Energy Systems and Electrical Drives Vienna University of Technology Tel

2 #2 Content Structure and basic algorithms of the model Invert/EE- Lab (Ernstl/EE- Lab), Disaggregated descrip?on of the building stock: level of disaggrega?on, types of buildings dis?nguished, types of parameters, Selected scenarios and results (discussing the role of the rebound effect), Open ques?ons, next steps

3 Structure and basic algorithms of the model Invert/EE-Lab #3

4 Exogenous scenarios growth of building stock The Simulation-Tool: Invert/EE-Lab Database hea>ng and hot water sector (t=t 0, input of simula>on results for t 1 t n ) Building stock data - U- values - Geometry - Age - Regions - Type of use Installed hea>ng and hot water systems - η/cop/solar yield - Type of energy carriers - O&M costs Climate data (HDD, solar irradia>on ) User behavior Technology data space hea>ng and hot water Energy prices Op>ons for thermal renova>on Space hea>ng and hot water energy demand calcula>on module Simula>on algorithm Logit approach Logis>c growth model Simula>on results - Installa>on of hea>ng and hot water systems - Total energy demand by energy carriers (GWh) - Total investments (M ) - Policy programme costs (M ) etc. Diffusion restric>ons Biomass poten>als Policies Preferences for hea>ng systems,, tradi>ons, iner>a 4

5 Exogenous scenarios growth of building stock The Simulation-Tool: Invert/EE-Lab Database hea>ng and hot water sector (t=t 0, input of simula>on results for t 1 t n ) Building stock data - U- values - Geometry - Age - Regions - Type of use Installed hea>ng and hot water systems - η/cop/solar yield - Type of energy carriers - O&M costs Climate data (HDD, solar irradia>on ) User behavior Technology data space hea>ng and hot water Energy prices Op>ons for thermal renova>on Space hea>ng and hot water energy demand calcula>on module Simula>on algorithm Logit approach Logis>c growth model Simula>on results - Installa>on of hea>ng and hot water systems - Total energy demand by energy carriers (GWh) - Total investments (M ) - Policy programme costs (M ) etc. Diffusion restric>ons Biomass poten>als Policies Preferences for hea>ng systems,, tradi>ons, iner>a 5

6 Description of the building stock Exemplary countries Austria Lithuania UK Type of buildings in Invert/EE- Lab Single dwellings, dwellings small, dwellings large, shops small, shops large, hotels and restaurants large, hotels and restaurants small, hospitals, office buildings large, office buildings small, schools, industrial buildings, leisure halls and other buildings Single dwellings, dwellings, non- buildings Single dwellings, double dwellings, terrace houses, dwellings, bungalows, office buildings, general retail buildings, large shops, hotels and pubs, entertainment halls, leisure halls, clubs, manufacturing, warehouses, storage depots, parking, buildings, hospitals, churches periods of the building stock) Before 1919, , , , , , , aèr 2003 Before 1991, , aèr 2000 Before 1918, , , , , aèr 2005 #6

7 #7 Description of the building stock (2) Each of these building classes may be further dis?nguished regarding previous renova?on ac?vi?es Basic variables describing the building classes: U- Values Geometry data User profiles (according to type of use) Age Each of these building classes is combined with different hea?ng systems, resul?ng in a few hundred up to a few thousands building segments Type of hea?ng / hot water system Efficiency of hea?ng technologies Heat distribu?on system

8 Exogenous scenarios growth of building stock The Simulation-Tool: Invert/EE-Lab Database hea>ng and hot water sector (t=t 0, input of simula>on results for t 1 t n ) Building stock data - U- values - Geometry - Age - Regions - Type of use Installed hea>ng and hot water systems - η/cop/solar yield - Type of energy carriers - O&M costs Climate data (HDD, solar irradia>on ) User behavior Technology data space hea>ng and hot water Energy prices Op>ons for thermal renova>on Space hea>ng and hot water energy demand calcula>on module Simula>on algorithm Logit approach Logis>c growth model Simula>on results - Installa>on of hea>ng and hot water systems - Total energy demand by energy carriers (GWh) - Total investments (M ) - Policy programme costs (M ) etc. Diffusion restric>ons Biomass poten>als Policies Preferences for hea>ng systems,, tradi>ons, iner>a 8

9 #9 Service factor: Impact of dwelling size and building quality on average indoor temperature Rebound effect Renovation Source: IWU, 2004

10 Exogenous scenarios growth of building stock The Simulation-Tool: Invert/EE-Lab Database hea>ng and hot water sector (t=t 0, input of simula>on results for t 1 t n ) Building stock data - U- values - Geometry - Age - Regions - Type of use Installed hea>ng and hot water systems - η/cop/solar yield - Type of energy carriers - O&M costs Climate data (HDD, solar irradia>on ) User behavior Technology data space hea>ng and hot water Energy prices Op>ons for thermal renova>on Space hea>ng and hot water energy demand calcula>on module Simula>on algorithm Logit approach Logis>c growth model Simula>on results - Installa>on of hea>ng and hot water systems - Total energy demand by energy carriers (GWh) - Total investments (M ) - Policy programme costs (M ) etc. Diffusion restric>ons Biomass poten>als Policies Preferences for hea>ng systems,, tradi>ons, iner>a 10

11 #11 Basic algorithms (1): Weibull-distributions Share of buildings applying changes (s measure ) is determined by: s measure = T T= T T max max T= T min min 1 e 1 e T λ k T 1 λ k λ, k...scale und shape parameter of Weibull distriubution λ [yr]... characteristic lifetime (lifetime at which a cumulative failure rate of 63,2% occurs) T,T [yr]... minimum and maximum age of considered component min max (installation period is assumed to be uniformly distributed)

12 #12 Basic algorithms (2): The logit approach Share of a technology (i) in the building segment (b) in?me (t) is determined by: bb,t,i e s LM,b,t,i = b,t,i I λbb,t,i r e λ i b i= 1 λ r... scaled variance of the decision parameter (assuming a Type-I extrem value distribution) r... relative costs of alternative i compared to average costs of alternatives r µ µ b,t,i b,t,i b,t,i = b,t,mean µ µ b,t,mean b,t,i... adjusted costs of alternative i... mean adjusted costs weighted by market share on installation

13 #13 Recent projects and references IEE- Project Policy development for improving RES- H/C penetra?on in European Member States (RES- H Policy), 2011 Energy Scenarios up to 2020: Hea?ng energy demand. Commissioned by the Austrian Umweltbundesamt GmbH Müller et al., 2010: Hea?ng systems in the Austrian building stock: Technological requirements up to (Heizen 2050) Development of an integrated hea?ng and cooling strategy for Germany, commissioned by the German Ministry of Environment, to be finished in 2012 Intelligent Energy Europe- Projekt: ENTRANZE Policies enforcing the transi?on to nearly zero energy buildings. Dura?on April October 2014.

14 Selected scenario results for Austria: number of bulidings (reference scenario) 2500 Number of buildings (1000) NWG, NWG, , renovated NWG, NWG, < 2010, renovated a`er 2010 NWG, < 2010 WG, WG, , renovated WG, , non- renovated WG, , renovated WG, , non- renovated WG, , renovated WG, , non- renovated WG, < 1945, renovated WG, < 1945, non- renovated Source: Müller et al 2010 (Heizen 2050) #14

15 Selected scenario results for Austria: useful energy demand (reference scenario) Useful energy demand heating and hot Nutzenergiebedarf water für preparation Heizung und Brauchwassererwärmung (GWh) in GWh NWG, NWG, , renovated NWG, NWG, < 2010, renovated a`er 2010 NWG, < 2010 WG, WG, , renovated WG, , non- renovated WG, , renovated WG, , non- renovated WG, , renovated WG, , non- renovated WG, vor 1945, renovated WG, vor 1945, non- renovated Source: Müller et al 2010 (Heizen 2050) #15

16 Selected scenario results for Austria: final energy demand (reference scenario) Final energy Energiebedarf demand (HZ + heating WW) in GWh and hot water preparation (GWh) Other Electricity heat pumps District heating Biomass district heating Ambient heat Solar thermal Biomass Source: Müller et al 2010 (Heizen 2050) #16

17 #17 Policies Economic incen?ves for RES- H and/or building renova?on: Investment subsidies Tax incen?ves Bonus schemes (e.g. RHI in UK) Regulatory measures Building codes, standards RES- H use obliga?ons Informa?on, awareness raising (by seeng restric?ons, preferences etc)

18 Policy impact on RES-H penetration #18

19 #19 Important issues still to be elaborated Behaviour of different actors, e.g. building owners vs. tenants, (current work of Jan Steinbach, FhG- ISI) Beher coverage of HVAC Closer link to building physic simula?on models Inves?ga?on of the na?onal implementa?on of the EPBD (recast) and the RED Integrated policy packages for renewable hea?ng AND NZEB (renova?on and new buildings) Extension of the database to the EU building stock

20 Partners: EEG, NCRC, Fraunhofer, CENER, eerg, Oeko, SOFENA, BPIE, Enerdata, SEVEn #20 Policies to ENforce the TRAnsition to Nearly Zero- Energy buildings in Europe (ENTRANZE) Dura?on: April 2012 September 2014 Objec?ve: assist policy makers in developing policy packages achieving a fast and strong penetra?on of NZEB and RES- H/C in line with the EPBD and the RED

21 #21 Thank you for your a,en.on! Further informa.on: Lukas Kranzl Energy Economics Group tel: web:

Työpaja Aidot hankkeet nzeb:iin

nzeb - FIN, 22.8.2013 Työpaja Aidot hankkeet nzeb:iin Jarek Kurnitski Full Professor, Tallinn University of Technology Adjunct Professor, Aalto University Vice-president REHVA jarek.kurnitski@ttu.ee www.nzeb.ee