Introduction
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How do energy savings in the building sector affect the deployment and operation of supply-side resources for electricity, heat and hydrogen?
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To what extent should end-use energy efficiency measures be prioritised over supply-side resources in the EU’s transition towards a net-zero emissions energy system?
Methodology
Outline of scenarios
Scenario | Description |
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Low energy efficiency in buildings (LowEff) | The EU primarily relies on renewable energy sources to decarbonise energy use in buildings. Ambitions for thermal retrofitting remain below the goal of doubling renovation rates set in the European Commission’s Renovation Wave strategy (2020a). Energy performance standards under the EU Ecodesign Directive (European Union 2009) are not tightened. As a consequence, comparatively high investments in generation, networks and storage capacities are needed to help achieve net-zero GHG emission levels. LowEff should not be interpreted as a business-as-usual scenario, given (i) the net-zero outcome by 2050 and (ii) reductions in final energy consumption that go well beyond the EU Reference Scenario (Capros et al., 2021) |
Medium energy efficiency in buildings (MediumEff) | Due regard is given to the EE1st principle. Building renovation rates are doubled compared to LowEff, along with greater renovation depth. Energy performance requirements for appliances are increased. In response to energy savings in buildings, the required investments in energy supply systems to achieve the 2050 net-zero target are lower than in LowEff |
High energy efficiency in buildings (HighEff) | The EE1st principle is well established. Renovation rates and depths are further increased for both residential and non-residential buildings. Strict performance requirements drive the adoption of highly efficient appliances. This is reflected in reduced investments in electricity, heat and hydrogen supply compared with the other two scenarios. HighEff can also be framed as a future with significant barriers to renewable energy supply (Eleftheriadis & Anagnostopoulou, 2015) (e.g. delays in the issuing of construction permits), as a result of which energy efficiency measures need to make a greater contribution to minimising GHG emissions |
Definition of energy system cost
Modelling approaches
Invert/Opt | Forecast | Enertile | NetHEAT | |
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Cost itemsa | • Building renovation • Heating systems | • Electrical appliancesb | • District heating generation • Electricity generation • Electricity networksc • Hydrogen generation | • District heating networks |
Approach | Optimisation | Simulation | Optimisation | Simulation |
Temporal resolution | Yearly | Yearly | Hourly (system operation), 10-yearly (capacity expansion) | Yearly |
Spatial resolution | National | National | Local (RES potentials at 6.5 × 6.5 km grid), national (power flows) | Local (0.1 × 0.1 km grid) |
Main input variables | Building stock data, technology properties and costs, consumer energy prices | Technology costs, consumer energy prices, learning rates | Final energy demand, technology costs, fuel prices, existing capacities | Useful energy demand, fuel and electricity prices, building stock, road lengths |
Main output variables | Final energy demand by energy carrier and building type, costs, direct CO2 emissions | Final energy demand by technology and efficiency class, market shares, costs | Generation mix, primary energy consumption, installed capacities, costs, direct CO2 emissions, prices | Network length, costs, linear heat densities, buildings connected |
Model environment | Aux. software | Python | SQLite, Excel, | VB.Net | SQLite | Java | MySQL | Python | QGIS, Excel |
Licence | Commercial | Commercial | Commercial | Commercial |
Website | TU Wien (2020) | Fraunhofer ISI (2022b) | Fraunhofer ISI (2022a) | IREES (2022) |
Key applications | Hummel et al. (2023) |
Energy demand
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Supply technologies | Data on conversion efficiencies, technical lifetimes, specific investments and O&M costs are distinguished by country, taking into account technological learning and different system sizes. Data are primarily based on DEA (2021b), complemented by country-specific sources.
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Renovation options | For each building archetype, multiple renovation packages consisting of single measures (e.g. triple glazing of windows) are defined, along with their specific costs and resulting U-values. Costs are based on a dedicated method detailed in Hummel et al. (2020).
Variable | Scenario | ||
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LowEff | MediumEff | HighEff | |
Refurbishment share in total renovations (entire stock) | 65–90% | 20–50% | 10–90% |
Refurbishment share in total renovations (building segments) | 25–100% | 10–90% | 0–100% |
Modification of renovation cycles of building shell elements | 1 | 1 | 1/1.4 |
Ecodesign requirements for appliances | Existing provisions as of Dec 2020 | • 2020–2030: best four available classes • 2031–2050: best three available classes | • 2020–2030: best three available classes • 2031–2050: best two available classes |
Energy demand in transport and industry | Final energy consumption for electricity, heat, and synthetic combustibles based on the 1.5TECH scenario (European Commission 2018a) |
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White goods (refrigerators, freezers, washing machines, dryers, dishwashers, stoves)
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Information and communications technology (laptops, tablets, televisions, etc.)
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Lighting (light emitting diodes, compact fluorescent lamps, halogen lamps)
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Other energy use: aggregate of small electrical appliances that are not explicitly modelled (e.g. coffee machines), plus emerging appliances which could potentially diffuse in the market until 2050
Energy supply
Variable | Scenario | ||
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LowEff | MediumEff | HighEff | |
Fossil fuel prices | Wholesale prices for crude oil, natural gas, hard coal, uranium based on Sustainable Development Scenario in IEA (2019) | ||
Biomass | Available biomass for electricity and district heating generation in 2050 at maximum of 50% of consumption level in 2020 (Eurostat, 2022c) | ||
Coal | Phase-outs/new construction ban based on Europe Beyond Coal (2021) | ||
Carbon capture and storage | Unavailable for electricity generation, based on 1.5LIFE scenario in European Commission (2018a) | ||
Cross-border electricity transmission | Minimum status for transmission grid in 2030 according to 2018 Ten Year Network Development Plan (ENTSO-E, 2018) | ||
Nuclear power | Capacity expansion/deconstruction of nuclear power plants based on National Champions Pathway in Sensfuss et al. (2019) |
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For transmission, Enertile endogenously models the transmission of electricity between model regions using a model of net transfer capacities (Lux & Pfluger, 2020). In this analysis, each European country is represented by one node without grid restrictions within countries, known as a copper plate approach (Lunz et al., 2016). Capacity expansion is determined endogenously, taking into account CAPEX, grid losses and system service costs, e.g. for balancing or ancillary services.
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For distribution, a linear extrapolation of future network costs is applied, based on a detailed account of network charges per Member State (Eurostat, 2022d, 2022e, 2022o). While in reality, location-specific distribution network planning is governed by complex system interactions (Jamasb & Marantes, 2011), this study extrapolates distribution network charges (\(EUR/kWh\)) based on electricity demand (\(TWh\)) up to 2050.
Fuel composition | Gaseous fuels | Liquid fuels | Wholesale price trend |
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Bio-methane | 77% | van Nuffel et al. (2020) | |
Hydrogen | 10% | Endogenously determined in Enertile | |
Synthetic methane | 10% | Endogenously determined in Enertile | |
Natural gas | 3% | IEA (2019): Sustainable Development scenario | |
Bio-oil | 85% | Assumption: price bio-methane + 25% mark-up | |
Synthetic liquid hydrocarbons | 10% | Assumption: price synthetic methane + 10% mark-up | |
Fossil heating oil | 5% | IEA (2019): Sustainable Development scenario |
Air pollution impacts
Emission type | Receptor | Emission source | |
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Energy supplya | Buildingsb | ||
SO2 | Health damage | 5898–28,287 | 6826–32,742 |
Biodiversity losses | 1010–1072 | 1010–1072 | |
Crop damage | − 214 to − 202 | − 214 to − 202 | |
Material damage | 279–1336 | 279 to 1336 | |
NOX | Health damage | 5155–24,724 | 7337–35,192 |
Biodiversity losses | 2626–2787 | 2626–2787 | |
Crop damage | 808–858 | 808–858 | |
Material damage | 46–223 | 46–223 | |
PM | Health damage | 10,263–49,224 | 20,200–96,889 |
NMVOC | Health damage | 557–2,673 | 557–2673 |
Crop damage | 1010–1072 | 1010–1072 |
Results
Domain | Indicator | Unit | Scenario | ||
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LowEff | MediumEff | HighEff | |||
Cost and emissions | Energy system cost 2020–2050a | \(EU{R}_{2018}/a\) | 530.59 | 530.73 | 534.29 |
GHG emissions reduction in 2050 vs. 2020b | – | − 98.2% | − 98.8% | − 99.3% | |
Cumulative GHG emissions 2020–2050b | \(MtC{O}_{2}eq\) | 12,540 | 12,172 | 11,741 | |
Building sector c | Final energy consumption in 2050 | \(TWh\) | 3488 | 3060 | 2812 |
Annual building renovation rate (2050–2020)d | – | 0.7% | 1.4% | 1.7% | |
Energy supply | Electrical generation capacities in 2050 | \(G{W}_{el}\) | 2712 | 2613 | 2535 |
Thermal generation capacities in 2050 | \(G{W}_{th}\) | 294 | 208 | 173 | |
Hydrogen electrolyser capacities in 2050 | \(G{W}_{el}\) | 303 | 290 | 282 |
Energy system cost and investment needs
Cost itema | Scenario | ||
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LowEff | MediumEff | HighEff | |
Building renovation | 228 | 248 | 276 |
Electrical appliancesb | 36 | 38 | 41 |
Heating systems | 204 | 200 | 198 |
District heating generation | 202 | 190 | 166 |
District heating networks | 98 | 79 | 76 |
Electricity generation | 5212 | 5167 | 5023 |
Electricity networksc | 219 | 207 | 204 |
Hydrogen generation | 553 | 546 | 535 |
Total investment (2020–2050) | 6751 | 6675 | 6520 |
Buildings
Electricity supply
District heating supply
Hydrogen supply
Air pollution impacts
Scenario | Emission source | Emission type | |||
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NMVOC | NOX | PM | SO2 | ||
LowEff
| Buildings | 573,046 | 6,897,457 | 932,037 | 2,702,254 |
Energy supply | 132,274 | 4,839,720 | 68,849 | 1,163,315 | |
MediumEff
| Buildings | 571,072 | 6,855,804 | 941,435 | 2,701,009 |
Energy supply | 127,892 | 4,697,482 | 67,727 | 1,161,958 | |
HighEff
| Buildings | 570,133 | 6,859,144 | 957,270 | 2,701,942 |
Energy supply | 121,624 | 4,494,603 | 66,048 | 1,156,071 |
Scenario | Emission source | Receptor | ||||
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Biodiversity losses | Crop damage | Health damage | Material damage | Total | ||
LowEff | Buildings | 0.570 | 0.153 | 4.206 | 0.049 | 7.317 |
Energy supply | 0.422 | 0.115 | 1.772 | 0.030 | ||
MediumEff | Buildings | 0.567 | 0.152 | 4.201 | 0.049 | 7.259 |
Energy supply | 0.412 | 0.112 | 1.736 | 0.030 | ||
HighEff | Buildings | 0.568 | 0.152 | 4.216 | 0.049 | 7.199 |
Energy supply | 0.398 | 0.107 | 1.681 | 0.029 |
Discussion
Critical appraisal
Policy implications
PRIMES-2007 baselined | PRIMES-2020 baselinee | ||
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Energy efficiency target for final energy consumption in 2030 | % difference to baseline | |||
EED-2018a | 846 Mtoe | 1,253 Mtoe | − 32.5% | 864 Mtoe | − 2.1% |
EED-2021b | 787 Mtoe | 1,253 Mtoe | − 37.2% | 864 Mtoe | − 9.0% |
Scenario projections for 2030c | % difference to baseline | |||
LowEff | 800 Mtoe | 1,253 Mtoe | − 34.8% | 864 Mtoe | − 5.5% |
MediumEff | 792 Mtoe | 1,253 Mtoe | − 35.5% | 864 Mtoe | − 6.5% |
HighEff | 786 Mtoe | 1,253 Mtoe | − 36.0% | 864 Mtoe | − 7.2% |