Long-term and step-by-step deep renovation approach including building owner’s ability to invest in a retrofitting optimisation model Iná MAIA, Lukas KRANZL and Andreas MÜLLER 16th IAEE European Conference, Ljubljana 2019 26.08.2019 TU Wien - Energy Economics Group (EEG)
Content Introduction Research question Method Results Conclusions and Outlook 2 IAEE - Ljubljana, 2019
Storytelling Costs with housing Energy efficient and climate neutral house Costs with new goods Step-by-step retrofitting approach Costs with vacation and free time Family grow 3 IAEE - Ljubljana, 2019
Introduction: facts about owner-occupied dwellings 40 – 90% owner-occupied dwellings Source: Housing tenure across OECD countries, del Pero et al. 2016 4 IAEE - Ljubljana, 2019
Introduction: facts about empirical evidences of step-by-step Existing Building stock volume of comprehensive and partial refurbishment, as well as repairing (in Mrd. Euro) Stand: Germany, 2010 MAY THE GRAPHS FROM GERMANY? Total Residential buildings Commercial buildings Public buildings Single stage Partial Comprehensive Repair modernisation refurbishment refurbishment work Source: adapted from Fehlhaber, 2017 – PhD Dissertation – Bewertung von Kosten und Risiken bei Sanierungsprojekten 5 IAEE - Ljubljana, 2019
Introduction: political context Building renovation passports: • Energy Performance of Buildings Directive (EPBD) 2018/844/EU introduced in Article 19a: “complementary document providing a long-term and step-by-step renovation roadmap for a specific building” • This document should guide and help building owners through the renovation process 6 IAEE - Ljubljana, 2019
Overall objective and research question Main objective: • Bridge the gap between building stock decarbonisation targets and real renovation processes • In real life , many renovation processes are performed step-by-step • But, most deep renovation modelling focus on single stage deep renovation Model under development: step-by-step retrofitting optimisation model focusing on owner-occupied dwellings Objective of this paper: explore some aspects of the optimisation’s framework Which relevant cost and building owner‘s ability to pay assumptions should be taken into account in a step-by-step optimisation model? 7 IAEE - Ljubljana, 2019
Methods: key challenges Different disposable income and affordability to pay for retrofitting CO 2 - Reduction until 2050 Building stock with Time when different building energy typologies and performance is energy efficiency improved standards Sources: Jürgen Fälchle - Fotolia.com , Amber Taufen - inman.com and Andre Haykal Jr - thriveglobal.com 8 IAEE - Ljubljana, 2019
Method: identifying main differences between retrofitting approaches Single stage Step-by-step Definition Only major renovation Retrofit measures performed according to (including whole building envelope) trigger points. Time dimension At once Over years (or decades) Effects on Faster CO 2 emission reduction Gradual CO 2 emission reduction climate targets (potentially more energy savings) Main risks If not done right, mistakes take long Include missed opportunities and lock-in time (even decades) to be corrected effects (lock-in effects) Main barrier Disruption and/or affordability Less information about right sequence of measures Material Costs At once – possibility that loans and Cost-shifting – further measures costs can incentives are available be partially anticipated Labour / At once Scaffolds and other construction site Montage Costs equipment might have to be mounted more than once Sources: adapted from Topouzi et al.2019 – Deep retrofit approaches: managing risks to minimise the energy performance gap 9 IAEE - Ljubljana, 2019
Method: overview of step-by-step optimisation framework Objective function: maximising net present value 𝐷𝐺 𝑀 𝑈 𝑈 𝑢 max 𝑂𝑄𝑊 = σ 𝑢 (1+𝑠) 𝑢 + (1+𝑠) 𝑈 𝐷𝐺 𝑢 = 𝐽𝑂𝐷 𝑢 ∗ 𝑡 − 𝐽𝐷 𝑓𝑠,𝑢 − 𝐹𝐷 𝑢 − 𝑃𝑁𝐷 𝑢 NPV, energy related net present value [EUR]; CF , cash-flow of energy related expenses [EUR]; 𝐽𝐷 𝑓𝑠,𝑢,𝑗 ∗ (𝑈 − 𝑢) 𝑀 𝑈 = L, residual value of the retrofitting measures in year T [EUR]; 𝑢 𝑀,𝑗 r , interest rate [%]; 𝑗 𝑢 t, time [a]; T , period of economic consideration [a]; Restrictions: INC, household income [EUR/a]; • Material’s aging process s, expenditure share of annual income [%/a]; • Budget restriction IC er , energy related investment cost of retrofitting measures [EUR]; EC, annual running energy costs [EUR/a]; OMC , operation and maintenance costs [EUR/a]; t L ,technical lifetime [a]; T, optimisation period time [a]; 10 IAEE - Ljubljana, 2019
Method: setting input data, example for SFH in Germany Retrofitting Material and Building vintage Household income mesures energy system Until 1918 Income ranges External wall insulation Material costs • Profile 1 – ie . 20000 €/a 1919-1948 • Profile 2 – ie . 31000 €/a Roof insulation • Profile 3 – ie . 43000 €/a 1949-1957 Labour costs • Profile 4 – ie . 57000 €/a Ground floor insulation 1958-1968 1969-1978 Windows replacement Energy carrier prices Expenditures share 1979-1983 Heating/cooling system • 6% replacement Material life time 1984-1994 • 15% DHW system replacement 1995-2001 Index for price PV installation development 2002-2009 Sources: TABULA Episcope,2012, Bundeszentrale für politische Bildung,2018, Eurostat,2018, Pfeiffer,2010 and Invert-EE/Lab,2019 11 IAEE - Ljubljana, 2019
Results Results: pre-analysis, SFH Germany Possible development of energy needs for space heating (concepts step-by- step and single stage) Examples: construction vintages 1958-1968 1958-1968 windows replacement 300 energy needs for space heating 250 floor and roof insulation windows replacement 200 [kWh/(m²a)] floor and 150 roof insulation windows 64% replacement 100 95% 50 single stage renovation 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Year Start step-by-step single stage 12 IAEE - Ljubljana, 2019
Results: exemplary case Total costs step-by-step: 42.000 Euros (including scaffold; excluding external wall insulation) Measure determined by material’s lifetime 3. Profile of budget restriction – 5% of share Budget restriction versus step-by-step retrofitting costs building vintage: 1958 -1968 40,000 10 years 35,000 postponement 30,000 25,000 Euro 20,000 2 years 15,000 postponement 10,000 5,000 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 Glazing replacement Heating system replacement Floor insulation Roof insulation PV (without battery) 13 IAEE - Ljubljana, 2019
Results: total costs for all reference buildings Step-by-step approach is only cheaper in cases, where not all measures are performed Older buildings are more expensive to deep retrofit Total costs: step-by-step versus single stage retrofitting 80,000 70,000 60,000 50,000 Euro 40,000 30,000 20,000 10,000 0 until 1918 1919-1948 1949 -1957 1958 -1968 1969 -1978 1979 -1983 1984 -1994 1995 -2001 2002-2009 1 step 2 step 3 step 4 step Single stage 14 IAEE - Ljubljana, 2019
Results: net present value for all reference buildings Interest rate: 3% Single stage has higher NPV than step-by-step in all cases Time of retrofitting becomes a relevant parameter Net present value: step-by-step versus single stage retrofitting 300,000 250,000 200,000 Euro 150,000 100,000 50,000 0 until 1918 1919-1948 1949 -1957 1958 -1968 1969 -1978 1979 -1983 1984 -1994 1995 -2001 2002-2009 1 step 2 step 3 step 4 step Single stage (2010) 15 IAEE - Ljubljana, 2019
Conclusions Which relevant cost and building owner‘s budget restriction assumptions should be taken into account in a step-by-step optimisation approach? Measure by measure cost data (material and labour costs) Four different income profile with two different expediture share -> building owner‘s budget restriction: decisive parameter, to define the time dimension, when retrofitting activities will be performed Net present value is an appropriate indicator to analyse the economic effects of time dimension of retrofitting approaches Loan, incentives and income adjustment should be included, in order to help designing policies schemes Outlook Optimisation approach: calculate the optimal retrofitting time -> distribution and different cases; in line with technical and economical aspects Techno-economic relevant synergies of measures (sequence and dependency of measures) Sensitivity analysis based on cost and income profile variations, energy prices and political scenarios 16 IAEE - Ljubljana, 2019
Thank you for your attention! Iná Maia maia@eeg.tuwien.ac.at Orig. Photo: Patrick Stargardt
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