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A Note on the Inefficiency of Technology- and Region-Specific Renewable Energy Support: The German Case

Analyse der Ineffizienz technologie- und regionenspezifischer Fördermechanismen für erneuerbare Energien am Beispiel Deutschland

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Abstract

Renewable energy (RES-E) support schemes have to meet two requirements in order to lead to a cost-efficient renewable energy mix. First, RES-E support schemes need to expose RES-E producers to the price signal of the wholesale market, which incentivizes investors to account not only for the marginal costs per kWh (\(\overline{MC}\)) but also for the marginal value per kWh (\(\overline{MV^{el}}\)) of renewable energy technologies. Second, RES-E support schemes need to be technology- and region-neutral in their design (rather than technology- and region-specific). That is, the financial support may not be bound to a specific technology or a specific region. In Germany, however, wind and solar power generation is currently incentivized via technology- and region-specific feed-in tariffs (FIT), which are coupled with capacity support limits. As such, the current RES-E support scheme in Germany fails to expose wind and solar power producers to the price signal of the wholesale market. Moreover, it is technology- and region-specific in its design, i.e., the support level for each kWh differs between wind and solar power technologies and the location of their deployment (at least for onshore wind power). As a consequence, excess costs occur which are burdened by society. This paper illustrates the economic consequences associated with Germany’s technology- and region-specific renewable energy support by applying a linear electricity system optimization model. Overall, excess costs are found to amount to more than 6.6 Bn Euro \(_{2010}\). These are driven by comparatively high net marginal costs of offshore wind and solar power in comparison to onshore wind power in Germany up to 2020.

Zusammenfassung

Fördermechanismen für erneuerbare Energien (EE) müssen zwei Bedingungen erfüllen, um das Kriterium der Kosteneffizienz zu erfüllen: Zum einen sollten Investoren unter dem Fördermechanismus das Marktpreissignal und damit den Grenzwert der Erneuerbaren Energien pro kWh in ihrem Investitionskalkül berücksichtigen. Zum anderen sollte der EE-Fördermechanismus technologie- und regionenneutral in seiner Ausgestaltung sein. In Deutschland wird die Stromerzeugung aus Wind- und Solarkraft derzeit jedoch über einen technologie- und regionenspezifischen Einspeisetarif gefördert (in Verbindung mit Obergrenzen für die insgesamt geförderte Kapazität). Hierbei spielt das Marktpreissignal aus Sicht der Investoren keine Rolle. Zudem ist der Einspeisetarif technologie- und regionenspezifisch in seinem Design, d. h., die Höhe der Förderung pro kWh unterscheidet sich nach der Technologie (Onshore Wind, Offshore Wind und Photovoltaik) und dem Aufstellungsort (zumindest für Onshore Wind). Infolgedessen fallen Zusatzkosten an, die von der Gesellschaft getragen werden müssen. Dieser Artikel analysiert die ökonomischen Konsequenzen der technologie- und regionenspezifischen EE-Förderung in Deutschland. Es wird gezeigt, dass sich die Zusatzkosten auf mehr als 6.6 Mrd. Euro\(_{2010}\) belaufen. Diese sind zurückzuführen auf vergleichsweise hohen Netto-Grenzkosten der Offshore Wind- und Solarenergie im Vergleich zur Onshore Windenergie in Deutschland bis 2020.

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Notes

  1. This is based on the assumption of imperfect information on the side of the government regarding the MC and \(MV^{el}\) of alternative technologies and regions, which prohibits the government to implement technology- and region-specific support schemes that lead to the cost-efficient renewable energy mix.

  2. By the end of 2013, 32 GW of onshore wind power was installed in Germany (ISE 2014).

  3. As explained, for example, in (Deutsche Bank 2012), all onshore wind projects currently receive the same FIT level (initial payment) for the first 5 years of operation. Afterwards, sites with highest full load hours (FLH) are paid a lower FIT level for the remaining 15 years of the contract (base payment). Sites with lower FLH, in contrast, are paid the initial payment for a longer period of time before they decline to the base payment. The period for which wind turbines receive the initial payment is determined by comparing each project’s FLH against a benchmark for the annual output (i.e., a reference yield).

  4. The term ‘technology- and region-neutral’ indicates that each kWh of renewable electricity produced contributes to achieving the RES-E target irrespective of the technology or the region of its deployment.

  5. The unit textrmC-kWh is derived by dividing the NMC/MC/\(MV^{el}\) by the accumulated full load hours over all years of the unit’s technical lifetime.

  6. The assumption that the \(\overline{MC}\) are independent of the respective technology’s penetration level implies that no space potential restrictions are binding, i.e., that favorable locations with high full load hours (FLH) are not limited. If, however, locations with high FLH are limited, the \(\overline{MC}\) would increase as the penetration increases since wind turbines/solar power system would need to be deployed at locations with lower FLH.

  7. Hence, all renewable energy capacity expansions after 2011 are endogenously determined by the model and do not necessarily correspond to the (real-world) capacity expansions actually realized in 2012 and 2013.

  8. Overall, we model Germany, Austria, France and the Netherlands. Given limited computational resources, there is a trade-off between manageable calculation times on the one hand side and a high regional and temporal resolution on the other hand side. For the analysis of the marginal value of renewables, a high temporal resolution—which captures the fluctuating characteristic of wind and solar power supply—was considered more important than modeling a large number of countries (see Sect. 3.1.3).

  9. The wind and solar power generation profiles are based on historical hourly meteorological wind speed and solar radiation data from (EuroWind 2011).

  10. The model’s optimization premise (minimization of accumulated discounted total system costs) implies a cost-based competition of electricity generation and perfect foresight.

  11. The approach of modeling a quantity-based regulation (\(\textrm{CO}_2\) cap) rather than a price-based regulation (\(\textrm{CO}_2\) price) ensures that the \(\textrm{CO}_2\) emissions reduction target is met in all scenarios simulated, which allows the results to be compared to one another. It reflects the market outcome of a \(\textrm{CO}_2\) cap-and-trade system.

  12. The security of supply constraint prescribes that the peak demand level is met by securely available capacities. Whereas the securely available capacity of dispatchable power plants within the peak-demand hour is assumed to correspond to their seasonal availability, the securely available capacity of fluctuating wind and solar power plants within the peak-demand hour is assumed to amount to the unit’s capacity credit, which typically varies between 0 and 10 % (e.g., Jägemann et al. b).

  13. For example, the model applied in Jägemann et al. (2013a, b) accounts for a peak capacity constraint as it simulates the dispatch of only 4 and 8 typical days, respectively.

  14. We note that the objective of the model is to minimize accumulated discounted total system costs.

  15. We note that under a technology- and region-neutral renewable energy RES-E target, the marginal of the technology- and region-neutral renewable energy constraint (Eq. (16)) corresponds to the \(\overline{NMC}\). Equally, under a technology- and region-specific RES-E target, the marginal of the technology- and region-specific renewable energy constraint (Eq. (18)) corresponds to the \(\overline{NMC}\) of the respective RES-E technology deployed in the respective subregion.

  16. The technical lifetime of both wind and solar power capacities is assumed to amount to 20 years in this analysis.

  17. The increase in the short-run marginal costs of power production of fossil-fuel fired (\(\textrm{CO}_2\)-emitting) power plants arises from incorporating the costs of emitting \(\textrm{CO}_2\), reflected by the price of \(\textrm{CO}_2\) emission certificates.

  18. We note that the modeled technology- and region-neutral RES-E targets for 2025 (40 %) and 2035 (55 %) (see Table 5) cover wind and solar power generation only. This reflects the assumption that wind and solar power are expected to account for the largest share of renewable energy capacity additions up to 2035, given the limited potentials for hydro power and low-cost biomass resources in generating electricity. Moreover, we note that the modeled RES-E targets (40 % in 2025 and 55 % in 2035) are related to the net electricity demand, while the German RES-E targets for 2025 (40–45 %) and 2035 (55–60 %) are related to the gross electricity consumption (CDU/CSU/SPD 2013).

  19. The TWh targets are derived by multiplying the 2020 capacity targets for solar power (52 GW), onshore wind power (50 GW) and offshore wind power (6.5 GW) with the full load hours assumed in the model; see also Table 9 of the Appendix.

  20. Note that the \(\overline{MV^{el}}\) of a specific technology varies between the two regions because of both differences in the level of full load hours and differences in the production factor profile.

  21. The development of the capacity and generation mix up to 2050 is shown in Fig. 7 of the Appendix.

  22. We note that the nuclear capacities are exogenously decommissioned in the model by 2022 reflecting current legislation in Germany.

  23. Lamont (2008) applies an illustrative optimization model to determine the cost-efficient capacity mix for five technologies (baseload, intermediate and peaking generators along with wind and solar power) using a greenfield approach to examine the effects of increased wind and solar power penetration.

  24. Between 2020 and 2050, solar power and offshore wind power investment costs are assumed to decrease by 31 and 38 %, respectively, while onshore wind power investment costs are assumed to decrease by only 11 %.

  25. The amount of wind and solar power curtailment in GWh is shown in Table 17 of the Appendix.

  26. See Table 1 and Fig. 3.

  27. We note that all wind turbines within a region are assumed to have the same production factor profile.

  28. The potential FLH of onshore wind power plants in southern Germany are assumed to be more than 5 % lower than the potential FLH of onshore wind power in southern Germany.

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  1. Formerly Institute of Energy Economics, University of Cologne, Vogelsanger Strasse 321, 50827, Cologne, Germany

    Cosima Jägemann

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Correspondence to Cosima Jägemann.

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A part of a previous version of this article is published in Jägemann (2014).

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Jägemann, C. A Note on the Inefficiency of Technology- and Region-Specific Renewable Energy Support: The German Case. Z Energiewirtsch 38, 235–253 (2014). https://doi.org/10.1007/s12398-014-0139-7

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