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Offshore wind farms are great options for addressing the world's energy and climate change challenges, as well as meeting rising energy demand while taking environmental and economic impacts into account. Floating wind turbines, in specific, depict the next horizon in the sustainable renewable energy industry. In this study, a life-cycle cost analysis for floating offshore wind turbines is developed by combining the most recent data and parametric formulas from databases and relevant papers. The cost analysis models focused on cost minimization with special emphasis on Operation and Maintenance Cost (OPEX), Decommissioning Cost (DECOM), and Levelized Cost of Energy (LCOE), which are important factors in wind power economy. Given that floating wind energy is still developing, the presented scenarios should be beneficial in making future decisions. The cost analysis scenarios include on-site and off-site maintenance scenarios for OPEX. In addition, four alternative scenarios for DECOM have been examined: mechanical recycling, mechanical-incineration, incineration processes, and landfill. According to the findings of these scenarios, OPEX varies from 16.89 to 19.93 £/MWh and DECOM between 3.47 and 3.65 £/MWh, whilst the total LCOE varied from 50.67 to 66.73 £/MWh. [ABSTRACT FROM AUTHOR]

The constantly growing demand for energy, the need to ensure the security of its supply, and the progressing climate changes related to the emission of carbon dioxide and other pollutants have caused, in recent years, an increase in interest in offshore wind energy. This paper presents all the work that needs to be done to build a wind farm in the Baltic Sea. The work focuses on the description of the equipment and the necessary tests to perform in order to analyze the obtained data. The data will allow for unambiguous interpretation and the selection of a convenient location for the construction of a wind farm. The final product of the work is a cost estimate, in which the costs of undertaking such an undertaking are shown. [ABSTRACT FROM AUTHOR]

In recent years, the demand and requirement for renewable energy have significantly increased due to concerns regarding energy security and the climate crisis. This has led to a significant focus on wind power generation. As the deployment of wind turbines continues to rise, there is a growing need to assess their lifespan and improve their stability. Access to accurate load data is crucial for enhancing safety and conducting remaining life assessments of wind turbines. However, maintaining and ensuring the reliability of measurement systems for long-term load data accumulation, stability assessments, and residual life evaluations can be challenging. As a result, numerous studies have been conducted on load prediction for wind turbines. However, existing load prediction models based on 10 min statistical data cannot adequately capture the short-term load variations experienced by wind turbines. Therefore, it is essential to develop models capable of predicting load with a high temporal resolution to enhance reliability, especially with the increasing scale and development of floating wind turbines. In this paper, we developed prediction models with a 50 Hz resolution for the bending strain at the tower bottom of offshore wind turbines by combining SCADA data and acceleration data using machine learning techniques and analyzed the results. The load prediction models demonstrated high accuracy, with a mean absolute percentage error below 4%. [ABSTRACT FROM AUTHOR]

One of the most crucial solutions to the issues of climate change and global warming is clean energy. However, creating intelligent, resilient, and sustainable systems is a worldwide problem, particularly for grid-connected Renewable Energy Systems (RES). Therefore, it is important to investigate how prospective changes in electricity pricing, renewable energy sources, and load demand could affect system performance during the projects. This paper presents a techno-economic analysis of a grid-connected wind energy system located in the Al-Jouf region in Saudi Arabia. To this end, the potential of renewable energy sources was assessed using Hybrid Optimization of Multiple Electric Renewables (HOMER) software, that also carried out the technical and economic study utilizing multi-year parameters. The novelty of this study is that it is the first-ever investigation of a grid-connected wind farm system in Saudi Arabia that considers the impact of multi-year parameters such as the grid price, system fixed operation, maintenance cost, and the AC electric load. The results showed that the proposed system in the chosen area recorded a very low Levelized Cost of Energy (LCOE) of around 0.06 USD/kWh compared to other systems. Also, running a multi-year model showed that the considered parameters have an impact on the system's performance, and this reflects the importance of considering these parameters in any such system that will increase the study's accuracy. [ABSTRACT FROM AUTHOR]

This paper aims to study the current regulation and governance of wind energy turbines on the high seas and detect regulatory challenges. We focus on the existing regulatory framework to develop marine wind farms in areas beyond national jurisdiction (ABNJ), the nature of wind farms and wind turbines in said areas, and which governance schemes and institutions ought to coordinate and regulate any future marine wind energy development. Our research shows that under public international law, the deployment of wind turbines on (most parts of) the high seas for all States is possible, but many issues still remain, either thinly regulated or unanswered. We inquire where, by whom, and how can marine wind parks be built on the high seas according to public international law and the United Nations Convention on the Law of the Sea (the LOSC). Lastly, we evaluate the possible role of marine spatial planning (MSP) in developing wind energy on the high seas. [ABSTRACT FROM AUTHOR]

Currently, the world is moving rapidly toward carbon neutrality, and renewable energy technology is very important in carbon neutrality. Among various renewable energy technologies, wind power is attracting much attention due to its sustainability, resource abundance, and high applicability. This study analyzed wind power patents from 2010 to 2021 to derive current global originality and convergence information. For IP5 countries, the growth stages of technology and the patent convergence networks were inferred by time series analysis and an association rule mining algorithm, respectively. The results showed that, during the analysis period, about 5000 patents were applied on annual average, and it was analyzed that China held the most patents at 48%, followed by the US, Europe, Korea, and Japan. According to the technology convergence network of patent codes from all the IP5 countries, patents related to 'wind turbines with rotation axes in the wind direction' occupied a very central position, and 'power conversion electric or electronic aspects' and 'integration of patents related to renewable energy sources in buildings with wind power' were found to show high connection strength. By country, it was analyzed that the US and China showed high patent competitiveness onshore and possessed many 'independent power conversion cluster' technologies. The research hypothesis was that technology development trends can be analyzed and the characteristics of each country can be understood through patent analysis in the wind power field. This hypothesis was analyzed through various patent analysis techniques, and this paper has novelty in that it presents the global megatrend in the wind power field through patent analysis and quantitatively presents the current status of technology development in five major countries. [ABSTRACT FROM AUTHOR]