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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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DATASET - Prime locations in Offshore Wind: Evaluating revenue resilience against price cannibalisation in Western Europe

Authors: Jacquet, Paul; Jansen, Malte; Galimov, Ruslan;

DATASET - Prime locations in Offshore Wind: Evaluating revenue resilience against price cannibalisation in Western Europe

Abstract

These files were generated as part of the analysis for the article titled: Prime locations in Offshore Wind: Evaluating revenue resilience against price cannibalisation in Western Europe Abstract : The European Union's commitment to achieving carbon neutrality by 2050 relies on a substantial increase in low-carbon energy generation, including 300 GW of offshore wind capacity. However, the concentration of assets in the North Sea has already led to significant price cannibalisation, negatively impacting the merchant revenues of projects. This article investigates the differences of sensitivity to revenue cannibalisation among offshore wind farms across Europe. A cross-correlation analysis is conducted to assess the synchronicity of wind power output across various European sea basins. The concept of capture value is used to quantify the relative market value of electricity generated by each asset. Historical data reveals a constant decline in this metric across Western Europe over the past decade. Two supervised machine learning models and one analytical model are developed to study the drivers of monthly capture value and to propose forecasts over 25 years for 18 locations distributed across France, Germany, and the Netherlands. An amplification of the observed decline is anticipated, driven primarily by increased renewable penetration. Furthermore, the relative synchronicity of wind patterns is found to significantly affect the sensitivity of wind farms to price cannibalisation within countries. Notably, the French Mediterranean region, characterised by low synchronicity with other French and European renewable assets, displays potential for higher and more stable revenues. In this region, capture values are projected to remain above 0.9 until 2050, translating into revenues 10-39% higher than those of other studied wind farms at that time, assuming equal price levels and capacity factors.

Related Organizations
Keywords

Revenue Projection, Capture Value, Offshore Wind, Price Cannibilisation, Investment Planning, Correlation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average