EGU Master Journal of Renewable Energy Short Reviews
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- Levelized cost of energy (1)
- Technological Learning (1)
- Wind energy (1)
- climbing robots (1)
- offshore wind turbine (1)
- operation and maintenance (1)
- renewable energy (1)
- robotics (1)
- underwater robots (1)
2024_08
This article analyses the impact of robotics on the
operation and maintenance (O&M) of offshore wind
turbines (OWTs), with a particular emphasis on the
challenges and benefits. As the world’s reliance on
renewable energy, particularly offshore wind, increases
to reduce climate change, the growing number of
OWTs requires effective O&M. Challenges consist
of logistics, accessibility and high costs. The paper
presents the application of climbing robots, unmanned
aerial vehicles and underwater robots to overcome
these challenges.
The combination of multiple robotic platforms, such
as autonomous surface vehicles and autonomous underwater
vehicles, represents a collaborative approach
to O&M. Obstacles include the need for accurate navigation,
building trust between humans and robots,
and research into artificial intelligence.
In conclusion, the integration of robotics in O&M
presents considerable advantages, increasing efficiency,
safety and cost-effectiveness. Further progress and
research into artificial intelligence are crucial in achieving
complete automation, which will transform the
O&M of OWTs.
2021_00
The EGU Journal of Renewable Energy Short Reviews (EGUJRenEnRev) is a teaching project rather that a regular scientific journal. To publish in this journal, it is a premise to take part in the master course wind power, hydro power and biomass usage at the faculty of Energy, Building Services and Environmental Engineering of the Münster University of Applied Sciences.
Students receive an equivalent of 2.5 credit points (European Credit Transfer and Accumulation System - ECTS) for their engagement in the course and for publishing a short review article of at most 3000 words in this periodical. The publication process closely mimics the typical publication procedure of a regular journal. The peer-review process, however, is conducted within the group of course-participants.
Although being just an exercise, we think that publishing the outcome of this course in a citable manner is not only promoting the motivation of our students, but may also be a helpful source of introductory information for researchers and practitioners in the field of renewable energies. We encourage students to write their articles in English, but this is not mandatory. The reader will thus find a few articles in German language. To further encourage students practicing English writing, perfect grammar is not part of the assessment.
We especially thank our students for working with LaTeX on Overleaf, although LaTeX is new to some of them. In this way, the editorial workload was reduced to a minimum. We also thank our students for sharing their work under the creative commons attribution licence (CC-BY). I appreciate their contribution to scientific information, being available to every person of the world, almost without barriers. I also thank the corresponding authors and publishers of the cited work, for granting permission to reuse graphics free of charge. All other figures had to be replaced or removed prior to publication.
2021_07
Diese Arbeit befasst sich mit Kostentrends in Zusammenhang mit technologischem Lernen von Windenergie an Land in den USA, in Deutschland und weltweit. Ziel dieser Arbeit ist es, eine Lernkurve für Windenergie an Land zu bestimmen. Dafür wurden Daten zu Stromgestehungskosten (LCOE) und Kosten für die Installation (COP) von Windenergieanlagen (WEA) im Zeitraum von 1983 bis einschließlich 2020 gesammelt, grafisch dargestellt und weitergehend ausgewertet. Die grafische Darstellung der Datenlage verdeutlicht die zeitliche Entwicklung der Technologie. Zur Beschreibung dieser Lernkurven wurden die Progress Ratio (PR) und Learning Rate (LR) in fünf unterschiedlichen Modellen bestimmt. Anhand derer sich in Kombination mit der zukünftig installierten Leistung von WEA eine Prognose über zukünftige Kosten ableiten lässt. Die ermittelten LR bewegen sich zwischen 13 % und 28 %, woraus sich LCOE im Jahr 2030 zwischen 44,03 US$/MWh und 61 US$/MWh ergeben.