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Zeitschrift für Energiewirtschaft

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16-03-2022

On the Development of Wind Market Values and the Influence of Technology and Weather: a German Case Study

Authors: Thorsten Engelhorn, Thomas Möbius

Published in: Zeitschrift für Energiewirtschaft | Issue 1/2022

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Abstract

Research into renewable energy market values is a popular field in energy economics. However, most contributions abstract from market values being highly individual and mostly study (nationwide) averages, usually based on a single or a “normal” wind year, if specifying wind conditions at all, and a limited set of technologies. However, market values of renewable energy resources are not monolithic but highly diverse. In this article, to shed light on this diversity, we illustrate the historical development of onshore wind’s market value in Germany, from 2001 to 2019, for the fleet and all operating wind energy converters. We use highly granular wind speed data and a comprehensive database of wind capacities. Our results show the downward trend, the distributions, and the variance of market values. In this context, we explain why the performance of a single wind energy converter (compared to the fleet’s performance) matters in the market premium model. Hereby, we also assess the magnitude of the outperformance of technologically advanced wind turbines as compared to less advanced turbines. In the second part of our research, we analyse the effect of the inter-annual weather variability on wholesale electricity prices, and market values. Our analysis is based on 19 different years of wind speeds, corresponding offshore and solar infeed, and an electricity market model to generate weather-congruent wholesale electricity prices.

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Because wind and solar power dominate governmental strategies for an RES expansion, we include only wind and solar in the term “RES”.

Antweiler and Müsgens (2021) show that in the long run, the market reduces overcapacity, and the effect disappears.

Also shaping a WEC’s power curve: blade design, gearing, cut-in and cut-off speed.

For the German market in years 2014 and 2015, Engelhorn and Müsgens (2018) showed that younger WECs reached higher market values than older ones.

Onshore capacity 2019: 53 gigawatts; generation: 102 terawatt hours; share in RES generation: 42% (BMWi 2021).

By transmission system operators imposed by the Renewable Energy Act (EEG 2021).

Since 2017, the tariff is determined in a tender; before 2017, it was set by law. As this change does not interfere with the benchmark regime described and our analysis, we do not go into further detail about the German support mechanisms.

The market value in power purchase agreements is even more important because the full risks of wholesale price turns need to be assessed—a risk the operator is shielded against in the market premium model.

The motivation is to improve the integration into the electricity system.

All operating WECs are included, irrespective of being eligible for support payments or not.

Published on a website hosted by the transmission system operators (Netztransparenz 2021).

If total revenues were calculated, this could make a difference, depending on the constellation of the tariff and the fleet’s market value (cf. Equation 23 and Anatolitis and Klobasa 2019). Yet, we just change the timely resolution and present market values, as it is usually done (to account for seasonalities, cf. Dalla Riva et al. 2017), and for conveniance (to easily compare 19 years). Besides, the fleet’s value will be determined annually for new WECs from 2023 onwards (EEG 2021).

Service fee F omitted.

This second case did hardly occur in our observation period.

Different weather conditions lead to different performances as infeed-price-correlations and yields vary with wind speeds.

Rotor-scaling and taller towers have a large share in driving down wind’s levelised cost of energy and thus unlocking the potential of low-wind sites.

~ 500,000 observations in Part I and ~ 580,000 in Part II.

On the NUTS‑1 or NUTS-2-level, depending on data and year.

Using the correction factors used for historical calibration leads to a strong trend in WECs’ yields because the factors are close to 1 for weather-year 2001 and decrease continuously; the correction factor needs to suit the WECs installed.

Bulgaria, Cyprus, Greece, Ireland, Iceland, Malta, and Romania are not included.

European Meteorological derived HIgh resolution RES generation time series for present and future scenarios.

The mean absolute error of modelled market values of the fleet vs empirical values is 0.49 percentage points.

Annual values for 2016 to 2019 are 10, 8, 7 and 6 percentage points.

Wallasch et al. (2015) assume a weighted average cost of 3.6 to 4.4% depending on wind site; we use 4%.

Performance divided by the fleet’s value.

Within raster of 10 by 10 kilometres, orography and wind speeds can still vary and thus influence correlation.

Note that the error strongly depends on the respective year. Eising et al. (2020) collect error measures of energy system models for different years and report a range for the MAE from 1.91 to 7.88 €/MWh and for the RMSE from 2.81 to 39.09.

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Title: On the Development of Wind Market Values and the Influence of Technology and Weather: a German Case Study
Authors: Thorsten Engelhorn
Thomas Möbius
Publication date: 16-03-2022
Publisher: Springer Fachmedien Wiesbaden
Published in: Zeitschrift für Energiewirtschaft / Issue 1/2022
Print ISSN: 0343-5377
Electronic ISSN: 1866-2765
DOI: https://doi.org/10.1007/s12398-022-00319-2

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