Galih BanggaEditorial on the Research Topic
Climate change challenge-a wind energy perspective
1 Introduction
The ongoing effects of climate change have significant consequences on ecosystems, healthcare systems, and the overall economy in the next decades. Several studies have investigated various impacts of the climate change (Nardone et al., 2010; Tol, 2018; Mikhaylov et al., 2020). Although some impacts are not direct, they can have long lasting influences and alter the future the planet earth. Tackling the climate change issues has been a major topic in the last few decades. Therefore, prioritizing the advancement, implementation, and integration of renewable energy sources is crucial.
Wind energy has become the main part in human life and one of the major player in reducing the climate impacts due to carbon emission. Some of the main drivers for massive developments in wind energy technologies are the constant depletion of fossil fuels, fear of the energy crisis, price volatility, and people’s awareness of cleaner and greener sources of energy (Bangga, 2022a). These factors have driven a rapid development and massive demand in wind productions over the time, and wind energy has been observed as one of the most prominent solutions to produce clean energy without leaving noticeable carbon footprints (Bangga, 2022b).
For this purpose, the Research Topic collection series “Climate Change Challenge” aim to foster collaborative scientific decision-making and work toward a more sustainable future for the next-generation. The objective is to explore solutions and challenges in energy research, particularly from the perspective of Wind Energy. The present editorial article will provide a brief overview of recent research progress and highlights some key takeaway from the studies.
2 Research outcomes and key takeaway
The Research Topic “Climate Change Challenge - A Wind Energy Perspective” has collected articles from different perspectives in wind energy technologies and assessments. These range from forecasting, wind farm control and optimization, to load estimations.
To properly evaluate wind turbine performance, low to high order prediction tools may be used (Bangga, 2022b). They will provide different levels of fidelity and reliability. One of the main consideration when using high order prediction tool is the computational speed, which becomes the limiting factor in forecasting the energy production in real time. To accommodate these shortcomings, state-of-the art statistical approaches are now commonly used for various aspects to enhance the accuracy of low order models.
Su et al. proposed a method to improve short-term power forecasting based on secondary decomposition technique and grey wolf optimizer (GWO). The simulation results indicate that the forecasting model outperforms other forecasting models in terms of accuracy. Another application of data processing technique in wind energy is to estimate the acting blade pitch loads based solely on SCADA (supervisory control and data acquisition) data as carried out by Li et al. The main implication and usage of the studies might be directed for condition monitoring of wind turbines (Wang et al., 2014). The outcome can also be useful to improve the understanding of large turbine behavior in operation and also during strong wind gust or turbulence. This might be also beneficial to reconstruct the wind field in combination with the data-driven technique proposed by Geibel and Bangga (Geibel and Bangga, 2022). The reconstructed datasets together with the processed SCADA data may serve as an input for a digital twin technologies when combined with a dedicated calculation tool such as blade-element theory.
The energy generated by the turbine will affect the grid and determine the characteristics of other turbines within the wind farm. Several studies have reviewed the optimization of a wind farm based on the layout and control perspectives (Bossanyi, 2000; Bossanyi, 2018; Bossanyi, 2022; Chen et al. Desalegn et al.), for instance as documented by Desalegn et al. which covers some recent trend in wind farm control models. Bossanyi (Bossanyi, 2022) and Chen et al. both adopted a surrogate model for wind farm applications. This highlights the possible application of surrogate models for fast prediction tool. However, in many cases, the short-term effects will not be captured by surrogate models, but the general characteristics are commonly well estimated, e.g., when comparing time domain load fluctuations.
Based on the above discussion, it can be seen that progress has been made in wind energy research in various aspects. It is evident that statistical approaches play an increasingly crucial role in wind energy assessment and predictions. In the future, it is highly possible to see the integration of digital twin technologies with wind energy systems, allowing various aspects of wind technology to be incorporated.
Author contributions
Galih Bangga: Writing–original draft, Writing–review and editing.
Funding
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Acknowledgments
The author of this editorial G. Bangga gratefully acknowledges the other topic editors of the Research Topic “Climate Change Challenge - A Wind Energy Perspective” for their valuable inputs and dedication to the Research Topic: Mojtaba Nedaei (University of Padova, Italy), Yang Yang (Ningbo University, China) and Christopher Niezrecki (University of Massachusetts Lowell, United States).
Conflict of interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Bangga, G. (2022a). Progress and outlook in wind energy research. Energies 15, 6527. doi:10.3390/en15186527
Bangga, G. (2022b). Wind turbine aerodynamics modeling using CFD approaches. Melville, New York: AIP Publishing LLC. doi:10.1063/9780735424111
Bossanyi, E. (2018). Combining induction control and wake steering for wind farm energy and fatigue loads optimisation. J. Phys. Conf. Ser. 1037, 032011. doi:10.1088/1742-6596/1037/3/032011
Bossanyi, E. (2022). Surrogate model for fast simulation of turbine loads in wind farms. J. Phys. Conf. Ser. 2265, 042038. doi:10.1088/1742-6596/2265/4/042038
Bossanyi, E. A. (2000). The design of closed loop controllers for wind turbines. Wind energy 3, 149–163. doi:10.1002/we.34.abs
Geibel, M., and Bangga, G. (2022). Data reduction and reconstruction of wind turbine wake employing data driven approaches. Energies 15, 3773. doi:10.3390/en15103773
Mikhaylov, A., Moiseev, N., Aleshin, K., and Burkhardt, T. (2020). Global climate change and greenhouse effect. Entrepreneursh. Sustain. Issues 7, 2897–2913. doi:10.9770/jesi.2020.7.4(21)
Nardone, A., Ronchi, B., Lacetera, N., Ranieri, M. S., and Bernabucci, U. (2010). Effects of climate changes on animal production and sustainability of livestock systems. Livest. Sci. 130, 57–69. doi:10.1016/j.livsci.2010.02.011
Tol, R. S. (2018). The economic impacts of climate change. Rev. Environ. Econ. policy 12, 4–25. doi:10.1093/reep/rex027
Keywords: small and large wind turbines, lifecycle assessment, wind resources analyses, renewable energy, climate change
Citation: Bangga G (2024) Editorial: Climate change challenge-a wind energy perspective. Front. Energy Res. 12:1448211. doi: 10.3389/fenrg.2024.1448211
Received: 12 June 2024; Accepted: 24 June 2024;
Published: 22 July 2024.
Edited and reviewed by:
David Howe Wood, University of Calgary, CanadaCopyright © 2024 Bangga. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Galih Bangga, galih.bangga@dnv.com