FG Energiewirtschaft
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Institute
Dieser Impuls stellt Zwischenergebnisse der ESYS Arbeitsgruppe „Strommarktdesign“ vor. Ein Schwerpunkt der Arbeitsgruppe ist ein verbessertes Marktdesign für die Sektorenkopplung, dessen wesentlicher Bestandteil eine wirksame CO2-Bepreisung ist. Aufgrund der aktuellen klimapolitischen Debatten und der damit verbundenen großen Dynamik hat die Arbeitsgruppe dieses Impuls mit besonderem Fokus auf dem CO2-Preis
verfasst. Die Gesamtergebnisse werden 2020 in einer ausführlichen Studie veröffentlicht, die auch weitere Aspekte des Strommarktdesigns umfasst.
Why is Germany’s energy transition so expensive? Quantifying costs from wind energy decentralisation
(2021)
This paper discusses the efficiency of promotion schemes for renewable energy sources using the example of onshore wind energy in Germany. We analyse whether the scheme incentivised a cost-minimal capacity build-up by developing a model to derive two cost-minimal benchmark scenarios, which are then compared to the historical capacity build-up between 1995 and 2015. The costs of the two cost-minimising benchmark scenarios are significantly lower than those of the historical build-up. The benchmark cost reduction largely stems from greater efficiency—fewer turbines are being constructed overall but they are being placed at better wind sites so the annual production of wind energy remains unchanged. Hence, aggregated turbine land use is also significantly down in these scenarios. Furthermore, we compare costs for consumers, as protecting consumers from price discrimination of producers is sometimes used to justify higher payments for low-wind sites. However, our results show that the efficiency gain from building at high wind sites outweighs the distributional effect, even from a consumer’s perspective.
Why is Germany’s energy transition so expensive? Quantifying costs from wind energy decentralisation
(2019)
In this paper, 180 papers on photovoltaic (PV) output forecasting were reviewed and a database of forecast errors was extracted for statistical analysis. The paper shows that among the forecast models, hybrid models are most likely to become the primary form of PV output forecasting in the future. The use of data processing techniques is positively correlated with the forecast quality, while the lengths of the forecast horizons and out-of-sample test sets have negative effects on the forecast accuracy. The paper also found that the use of data normalization, the wavelet transform, and the inclusion of clear sky index and numerical weather prediction variables are the most effective data processing techniques. Furthermore, the paper found some evidence of “cherry picking” in the reporting of errors and we recommend that the test sets be at least one year long to avoid any distortion in the performance of the models.