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Replacing gas boilers with heat pumps is the fastest way to cut German gas consumption – Communications Earth & Environment



The aim of the model is to estimate the substitution of gas by heat pumps with renewable electricity in Germany in hourly resolution. We, therefore, use a deterministic approach with only a single, nominal realisation of the renewable generation for heat pumps. The limits of this approach are critically assessed below.

For modelling electricity generation in the near future, we choose the reference year 2020, in which the following installed capacities were available: 55 GW for onshore wind, 6.3 GW offshore wind and 54 GW for PV. For the years between 2022 and 2030, we include the planned addition of onshore and offshore wind and PV shown in Supplementary Fig. S4. The advantage of this reference approach is that all details of the overall system as well as climate data for the whole of Germany are included.

Approximations of the model

There are three main accompanying approximations to this model choice:

Firstly, the geographical positioning of the new onshore wind farms is important43, as the yield is greater in the north than in the south of Germany. It is realistic to assume that the new addition of wind farms will occur in a similar geographical distribution as in the 2020 inventory, as the energy transition in Germany has progressed so far that new wind farms are being re-powered in optimal locations and more wind farms have already been built in suboptimal locations as well. Multiplying onshore wind power of 2020 by a factor proportional to newly added capacity in the future is nevertheless conservative, because new wind turbines are usually bigger and higher and lead to more load hours than the capacities in 2020. In contrast, the geographical distribution of newly installed PV plants is less important and the hourly values are quite well correlated throughout Germany (except for the northern slope of the Bavarian Alps)44.

Secondly, the electricity generation by power plants other than wind, PV and gas-fired units are left unchanged in all future years. It is apparent from the modelling results in Figs. 5 and 6 that apart from the operation of heat pumps and replacement of load hours of gas-fired power plants, an increasing part of the newly added PV and wind power will be left over for other applications. Because much of the electricity in Germany is traded according to the merit order principle, coal-fired power plants will be replaced by the leftover electricity, not by the electricity used for heat pumps and the replacement of gas-firing. Future changes in the generation of coal and other electricity therefore have only a minor influence on our results for gas substitution. So has the onset of e-mobility.

Thirdly, electricity generation from renewables fluctuates from year to year, more so for wind energy than for photovoltaics. To quantify year-to-year weather variability, we also model the years 2017, 2018 and 201923 for comparison with the 2020 reference. Our modelled gas consumption results in a standard deviation of 1.2% compared to imported gas in 2020 (970 TWh). This is in line with the standard deviation of gas imports16. Since we derive the standard deviation from only four years, we multiply it by a factor of two to be on the save side, as indicated by the error bar in 2025 in Fig. 5. In some winters, typical large-scale weather conditions45 can lead to dark doldrums46 in which neither wind energy nor PV can sufficiently meet grid demand. Such periods would only reduce gas savings to some extent, as these typical weather patterns do not cause very cold temperatures.

Limitations of the model

The model focuses on newly added renewable generation capacities and newly added heat pumps and neglects changes in fossil generation capacities. With the coal phase-out scheduled only for 2030 and an overcapacity of LNG terminals being installed due to the Russo-Ukrainian war14, it is unlikely that the reduction in fossil generation capacity will lead to unexpected allocations of renewable electricity and thus have an impact on the results presented here. Furthermore, the model only considers the generation and consumption of electricity, not the details of electricity transmission in the power grid, where congestion may occur. This is a limitation of the model, especially in the very fast scenario for 2024 and 2025, where 70% of all new renewable power is used by heat pumps. The load curve in Supplementary Fig. S3 shows that heat pumps do not cause the notorious demand spikes, known from air conditioners on hot days, partly due to heat storage in boilers. Additionally, heat pumps can be operated very flexibly. As buildings are a large thermal energy storage, and many buildings have a boiler, heat pumps can be operated a few hours ahead of the heat demand, e.g., in the night hours when wind power is often available and the grid load is low. In spring and autumn47, coupling heat pumps with local PV can provide heat during the day. This flexibility is not incorporated in the load curve in Supplementary Fig. S3. If heat pumps are operated in such a way that they run during times of low market prices, the power grid is considerably disburdened. Still, a combination of grid expansion48,49 and electricity storage is necessary, as mandated by the German Federal Network Agency. And in no case should the model be used longer into the future than to the point of about 80% of renewable energy in the grid. At higher percentages, balancing capacities of multi-fuel internal combustion engines/open cycle gas turbines (ICE/OCGT) plants must be added during hours when there is no sufficient wind or sunshine. According to the plans of the Federal Ministry of Economics and Climate Protection24, the 80% mark will be reached in 2030.

Gas load profiles

For quantifying the variation of gas consumption, we use the daily sigmoidal linear (sigLin) load profiles d from the Standard Load Profiles (SLP) manual19, which depends on the daily average temperature Td as follows:

$$d\left({T}_{d}\right)=\frac{A}{1+{\left(\frac{B}{{T}_{d}-40\deg C}\right)}^{C}}$$


For residential space heating, we use the profile DE_HEF04 with A = 3.1850191, B = –37.4124155 °C, C = 6.1723179, and D = 0.0761096, while for gas cooking, we use the profile DE_HKO03 with A = 0.4040932, B = –24.4392968 °C, C = 6.5718175, and D = 0.7107710. Both on all weekdays.

Since air temperature varies across Germany, we modelled with hourly temperature data from the German Weather Service (DWD)50 in the most populous metropolitan areas, see Supplemental Note 10. Hanover is closest to the median of these sites, and all data were modelled with the temperature data from Hanover. The standard deviation due to the choice of locality (multiplied by a factor of 2 to be on the safe side) is represented in Fig. 5 by the error bar in 2026.

As shown in the Supplementary Note 2, hourly load profiles h are taken as a function of hourly outdoor temperature Th and normalised to 1.

Mathematical procedures

The amount of gas Gi,2020 consumed in each hour of 2020 in a sector s = {residential space heating, cooking, the chemical, paper and food processing industries} is: Gs,2020 = d(Td) * h(Th) * gs,2020, where gs,2020 is a factor chosen so that the sum of gas consumption over the whole year 2020 in each sector is matched to the values given in the main text. Please, note that d and h have no unit, while g has unit TWhg, with g standing for gas.

To calculate the future quantity Gs,year of substituted gas in the year = {2022, 2023 … 2030}, we multiply Gs,2020 by a factor fyear,scenario, which depends on the scenario = {installers’ roadmap, accelerated, fast, very fast}. Note that we choose f to be the same for all sectors s, using the scenarios for the heat pumps in residential space heating shown in Fig. 4 in units of million. To obtain f, we therefore scale these numbers so the factor is 0 in 2020 and 1 if 16 million heat pumps are installed, as listed in Supplementary Table S5. Then, Gs,2020 * fyear,scenario yields the amount of substituted gas in TWhg. To calculate the required electricity in TWhe, Gs,2020 * fyear,scenario is divided by the (momentary) COP. For private space heating, Gs,2020 * fyear,scenario is divided by a quadratic polynomial shown in Fig. 3, which is 5.4 – 0.013 * (Theat − Th) − 0.00062 * (Theat − Th)2. A parameterisation of the heating water temperature (as a function of Th) in the various building efficiency classes A to G is shown in Fig. 2 and listed in Supplementary Table S4.

The hourly sum of all these TWhe values required by heat pumps, H, is compared with the added PV and wind electricity calculated according to Supplementary Fig. S4, specifically by multiplying the hourly PV and wind electricity values of 2020 by the factors given in Supplementary Table S1. If H is greater than the added renewable electricity R, the amount of gas, Gpp, used by gas-fired power plants to meet this additional demand is calculated, taking an efficiency of 50% for these power plants, as explained in the main text: Gpp = (H − R)/0.5. In this case, Gpp is subtracted from the gas substituted by heat pumps, and the net amount is marked as “used for heat pumps” in Fig. 6. If H is smaller than R, Gpp is negative, which means that at least part of R substitutes gas by reducing the output of the gas-fired power plants. That amount is labelled as “load hour reduction in gas power plants” in Fig. 6. If there is more R available than can be used for reducing the output of the gas-fired power plants, this remaining power is labelled as “other use” in Fig. 6. In Fig. 5, the sum of both “used for heat pumps” and “reduction of load hours in gas power stations” is shown. Finally, the columns in Fig. 5 labelled “no heat pumps” are a calculation with H = 0. The modelling results are listed in Table S7.

We do not calculate the investment and the price. Private investment, though, is kept to a minimum by limiting our model to the installation of heat pumps with only minor changes to the heating circuit.

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