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ディスカッションペーパー No.40

Discussion Paper
Research Project on Renewable Energy Economics,
Graduate School of Economics, Kyoto University

From FIT to FIP: assessing the impact of feed-in policies on
renewable development and spot market in Germany

May 2022

Yimeng DUa, Teng MAb
a Research Project on Renewable Energy Economics, Kyoto Universityb

Graduate School of Economics, Kyoto University

ディスカッションペーパー No.40

京都大学大学院 経済学研究科 再生可能エネルギー経済学講座

From FIT to FIP: assessing the impact of feed-in policies on
renewable development and spot market in Germany
Yimeng DuResearch Project on Renewable Energy Economics, Kyoto UniversityTeng Ma
Graduate School of Economics, Kyoto University

Abstract:
This study investigates the effectiveness of feed-in policies in Germany. By adopting the autoregressive distributed lagerror correction model, we examine both the short- and long-term impacts of feed-in policies on renewable energy
deployment in terms of power generation and approved installation capacity. The estimation results show that the feedin premium scheme achieved its objective of encouraging the market integration of wind energy, but discouraged andretarded investment in solar power technologies. Additionally, our results confirm that the feed-in tariff mechanism
creates greater investment security for solar power projects.
Keywords: Feed-in Premium, Feed-in Tariff, Renewable Energy, Market Integration, Autoregressive Distributed Lag

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1. Introduction

Under the feed-in-tariff (FIT) framework, the tariff for RE electricity is set according to theactual power generation cost of each RE technology and maintained at the same level regardlessof demand--supply balance or market price. An alternative emerged with the adoption of thefeed-in premium (FIP), a market premium system announced by EEG 1 2012, which sets
incentives for demand-oriented RE feed-in and efficient marketing of RE electricity in Germany(Purkus et al., 2015). Previous German research has mainly focused on the effectiveness of theFIT policy (Dillig et al., 2016; Böhringer et al., 2017; Hitaj and L Löschel, 2019); however, ourstudy compares the impact of the market premium FIP system with the fixed-tariff FIT approach,in order to illustrate the possible changes in the investment environment of RE caused by thepolicy transition from FIT to FIP. Since the optional sliding FIP was implemented in 2012, both
onshore and offshore wind power investors have shown more willingness to participate in direct
marketing than solar energy technology investors have. Along with the EEG amendment, theshare of direct marketing of onshore wind power increased from 68.7% in 2012 to 96.1% in
2021. There has been a sharp decrease in the share of the FIT for solar energy since theenforcement of the optional FIP; meanwhile, 57.8% of solar power generation is still
incentivized based on the FIT approach in 2021 2.
2. Empirical Model

To identify the short-run relationship between changes in tariff rates of feed-in policies andRE deployment, we utilize the cointegration techniques of the autoregressive distributed lag(ARDL). The unit root tests of augmented Dickey-Fuller and Kwiatkowski-Phillips-SchmidtShin confirm that all the variables' order of integration does not exceed the value of 1 (I(0) orI(1)); this suggests that the ARDL model is appropriate for this study. Long-term elasticity isfurther estimated, since the ARDL bound test suggested that all variables have a cointegration
relationship. The ARDL in its unrestricted error correction model (ECM) form can berepresented as follows:
𝑝𝑞Δ𝑙𝑛𝑌𝑡 = β0 + β1 ∑ Δ𝑙𝑛𝑌𝑡−𝑖 + β2 ∑ Δ𝑙𝑛𝑋𝑡−𝑖 + α1 𝑙𝑛𝑌𝑡−1 + α2 𝑙𝑛𝑋𝑡−1 + λ𝑇𝑟𝑒𝑛𝑑 + ϵ𝑡 ,
𝑖=1

𝑖=1

where Δ denotes the first difference, the short run impacts are represented by 𝛽1 and 𝛽2 , 𝛼1measures the speed of adjustment back to long run equilibrium, and the estimations of 𝛼2divided by 𝛼1 and multiplied by (-1) determine the long run impacts. The dependent variables
𝑌𝑡 = (𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑡 , 𝑎𝑝𝑝𝑟𝑜𝑣𝑒𝑑𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑡 )′ are used to represent power generation and the1

The Renewable Energy Sources Act (in English), Erneuerbare-Energien-Gesetz (in German).
Authors' own calculation according to “EEG in figures: Remuneration, differential costs and EEG
surcharge from 2000 to 2022”.
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newly approved installation capacity of RE 3 facilities in month t. These indicators are used tocapture the market integration and investment intention for RE. The independent variables 𝑋𝑡
include FIP and FIT tariff rates for REs, installed capacity of RE facilities, natural gas-fired
power generation, and electricity imports. Trend is the monthly time trend. Optimal lag values
p and q are obtained under the Akaike information criterion.
The time-series data of this study cover the period from January 2015 to September 2018 forGermany's electricity spot market. Data for renewable power electricity generation are gatheredfrom the ENTSO-E (European Network of Transmission System Operators for Electricity).
Information on newly approved capacity of RE power plants is from the register data on
renewable power plants released by the Federal Network Agency4. Approved capacity is theestimated capacity to be reached after the construction work of projects is completed andprojects become fully operational. In this study, newly approved capacity of power plants ineach month is used to capture the investment intention of power producers. Previous studies
also use these indicators to measure renewable power deployment (Menz and Vachon, 2006;
Hitaj and Löschel, 2019). As policy incentive variables, we use the monthly tariff rate of feedin policies. The tariff rate of the 𝐹𝐼𝑃 is calculated every calendar month as the difference
between the predetermined reference tariff (𝑟) (legally defined for each technology under thesliding FIP scheme) and monthly market value of the electricity sold ( 𝑀𝑊 :: 𝐹𝐼𝑃 =𝑟 – 𝑀𝑊, 𝑖𝑓 𝑟 − 𝑀𝑊 ≥ 0; 𝐹𝐼𝑃 = 0, otherwise. Calculation of the monthly market value forelectricity from wind energy and solar energy is based on their own hourly electricity generation(𝑤𝑡 : and hourly market price (𝑝𝑡 : : 𝑀𝑊 =∑𝑡 𝑤𝑡 𝑝𝑡 ⁄∑𝑡 𝑤𝑡 . For both wind and solar energy, thereference tariff rates under the FIP and the tariff rates of FIT decrease regularly. To simplify thecalculation of incentives under the FIP, the reference tariff rate of the market premium for solar
power is the weighted average of the tariff rate for different scales of a project. The reference
tariff for wind power is the weighted average of the initial and basic values based on different
definitions of the initial period based on the EEGs 5. We hypothesize that a uniform reference
tariff is offered at all wind project locations, as opposed to the wind potential-dependent
incentive6.
1. Estimation results and Discussion
Regarding the power generation models' results in Table 1, there is evidence of a positively andstatistically significant impact of the FIP on encouraging power generation from both onshore andoffshore wind energy in the short term. For instance, there is a 0.63% increase in onshore wind power

3 In this study, RE sources include solar, onshore, and offshore wind power.
4 EEG register data and reference values for payment, Bundesnetzagentur.
5 The EEG 2014 states that power generators should receive the initial reference tariff for the first 5years of operation, and the basic value for the remaining 15 years. The first 12 years after theinstallation are defined as the initial period in the EEG 2017.
6 Hitaj and Löschel (2019) adopted a similar approach.
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ディスカッションペーパー No.40

generation in response to a 1% positive shock in FIP tariff rate. Additionally, as shown in columns (3)and (5) in Table 1, a further increase in onshore and offshore wind power generation is observed underthe maximum incentive level7 in the short term. These results show the ability of wind power producers
to react to market signals, through the RE aggregators, combined use of storage battery and heat storagesystems (Sheikhahmadi and Bahramara, 2020), and demand-response resources. The negative andstatistically significant coefficients of the ECM reveal that most short-run deviations of the variables
can be returned to the long-run equilibrium within a short period.
[Table 1]
Columns (1)-(3) in Table 2 show the long-run elasticities of the power generation model8. The opposite
sign of the coefficients of FIP and FIT reveal that onshore wind power production had a symmetricalresponse to the change in the FIP tariff level in the long-term equilibrium state; meanwhile, the FIT did
not exert a similar market integration impact, since tariff levels under the FIT remain independent fromthe market price. Simultaneously, we find that receiving maximum incentive would not encourage wind
power generation in the long term, which confirms the efficiency of the current incentive level under
the FIP.

[Table 2]
Moreover, the incentives assigned to RE had different effects on newly approved capacity of power
plants (Table 3). In the short run, we find that adopting a FIP has negatively affected investment
intention for solar power by decreasing the scale of newly approved projects (column (1), Table 3). For
solar energy investors, especially for producers of distributed solar power projects, FIP schemes entail
additional elements of uncertainty, including transaction costs, costs for forecasting weather and demand,and imbalanced pricing, resulting in higher financing costs (Energypedia, n.d.). The FIP policy-induced
reduction in newly approved capacity of solar power projects indicates that developers tend to invest insmall-scale projects, as direct market exceptions are granted to small power plants with capacities below
a certain threshold9. This exception gives investors an additional option to avoid potential financial risk
under the FIP. Investors' willingness to invest in solar energy shifted to medium and small-sized10 powergeneration facilities. Wehrmann (2020) expected much of the growth of solar power to be driven by

7 In this case, we assume that wind power projects receive the maximum incentive, which is the initialreference tariff for the entire 20-year operation period.
8 The long-run elasticities are calculated as the value of the long-run coefficient divided by the ECM
value and multiplied by (-1:. The long-run coefficients and ECM values are shown in Tables 1 and 3.
9 Direct marketing obligation exceptions have been granted to RE plants with installed capacities
below 500 kW since August 2014. The obligation targets were extended to power plants with installedcapacities above 100 kW from January 2016.
10 The Federal Network Agency defines small-sized power plants as power plants with installedcapacities over 100 kW and up to 750 kW.
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small projects whose capacities are below the 750-kW limit, which exempts them from auctions for RE
support.
[Table 3]
By focusing on long-term elasticities of the approved installed capacity model (columns (4)-(6) inTable 2), we find that the promotion of solar power technologies can be positively affected by the FIT
only in the long run. The FIT scheme for RE has promoted investment and encouraged new entrants
into RE, as power producers enjoy the benefits of predictability of a return on investment (Kobayashi etal., 2020). The FIP mechanism did not show a similar statistically significant effect, since theremuneration of produced energy is now market dependent, unlike the price stability of the FIT scheme(Loukidis et al., 2018). As stated in Schallenberg-Rodriguez and Haas (2012), fixed tariff FIT creates
greater investment security. The lower-risk environment can encourage the participation of smaller andmore risk-averse investors.

2. Conclusions
This study confirmed that wind energy reacts to market signals in Germany. We find that the policy
intervention of market premiums has had a sustainable market activation effect over the long term, byencouraging market integration of onshore wind power generated electricity. By contrast, our results
confirm that the FIT has encouraged the promotion of solar energy projects, while FIP significantly
decreasing the investment intention for solar projects. Solar power developers tend to invest in
distributed projects excluded from the direct marketing obligation, to avoid potential financial risk under
the FIP.

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merit-order effect: An econometric multi-country analysis. Energy & Environment. 27(1), 105-121.

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Figure 4: Amount of market premium (€/kWh: under the sliding FIP scheme2022 年 05 月

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