A review of wind turbines in complex terrain

Elgendi, Mahmoud; AlMallahi, Maryam Nooman; Abdelkhalig, Ashraf; Selim, Mohamed Y. E.

doi:10.1016/j.ijft.2023.100289

Cited by 54 publications

(26 citation statements)

References 96 publications

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“…The most common ambient energy source utilized worldwide to generate enormous amounts of electricity for big-scale electrical equipment is wind [42]. Wind energy generating techniques and technologies have been used to scavenge energy from the wind for many years.…”

Section: Wind Energymentioning

confidence: 99%

A Review on Energy Management of Community Microgrid with the use of Adaptable Renewable Energy Sources

Saha,

Haque,

Halim

et al. 2023

IJRCS

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The main objective of this paper is to review the energy management of a community microgrid using adaptable renewable energy sources. Community microgrids have grown up as a viable strategy to successfully integrate renewable energy sources (RES) into local energy distribution networks in response to the growing worldwide need for sustainable and dependable energy solutions. This study presents an in-depth examination of the energy management tactics employed in community microgrids using adaptive RES, covering power generation, storage, and consumption. Energy communities are an innovative yet successful prosumer idea for the development of local energy systems. It is based on decentralized energy sources and the flexibility of electrical users in the community. Local energy communities serve as testing grounds for innovative energy practices such as cooperative microgrids, energy independence, and a variety of other exciting experiments as they seek the most efficient ways to interact both internally and with the external energy system. We discuss several energy management tactics utilized in community microgrids with flexible RES, Which include various renewable energy sources (wind, solar power, mechanical vibration energy) and storage devices. Various energy harvesting techniques have also been discussed in this paper. It also includes information on various power producing technology. Given the social, environmental, and economic benefits of a particular site for such a community, this paper proposes an integrated technique for constructing and efficiently managing community microgrids with an internal market. The report also discusses the obstacles that community microgrids confront and proposed methods for overcoming them. This paper analyzes future developments in community microgrids with adaptive RES. The study discusses potential developments in community microgrids with flexible energy trading systems.

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Section: Wind Energymentioning

confidence: 99%

A Review on Energy Management of Community Microgrid with the use of Adaptable Renewable Energy Sources

Saha,

Haque,

Halim

et al. 2023

IJRCS

View full text Add to dashboard Cite

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“…Most of the wind measurements are taken near the surface at 10 m above ground (MeteoSwiss, 2022;WSL SLF, 2022), while most of the operational wind turbines feature a hub height of 100 m. A logarithmic wind profile is often utilized to extrapolate the wind speed measured near the ground to the turbine hub height. However, the vertical wind profile in complex terrain generally does not follow a logarithmic shape (Dar et al, 2019;Elgendi et al, 2023). Despite the additional problems and challenges, it has been found that terrain complexity can also provide benefits to the local wind power potential (Clifton et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Influence of air flow features on alpine wind energy potential

Kristianti,

Gerber,

Gonzàlez-Herrero

et al. 2024

Front. Energy Res.

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Wind energy is one of the potential options to fill the gap in renewable energy production in Switzerland during the winter season when the energy demand exceeds local production capacities. With likely further rising energy consumption in the future, the winter energy deficit may further increase. However, a reliable assessment of wind energy potential in complex terrain remains challenging. To obtain such information, numerical simulations are performed using a combination of the “Consortium for Small-scale Modeling” and “Weather Research and Forecasting” (COSMO-WRF) models initialized and driven by COSMO-1E model, which allows us to simulate the influence of topography at a horizontal resolution of 300 m. Two LiDAR measurement campaigns were conducted in the regions of Lukmanier Pass and Les Diablerets, Switzerland. Observational LiDAR data and measurements from nearby wind sensor networks are used to validate the COSMO-WRF simulations. The simulations show an improved representation of wind speed and direction near the ground compared to COSMO-1E. However, with increasing height and less effect of the terrain, COSMO-WRF tends to overestimate the wind speeds, following the bias that is already present in COSMO-1E. We investigate two characteristic mountain–terrain flow features, namely waves and Foehn. The effect of mountain-induced waves of the flow is investigated through an event that occurred in the area of Diablerets. One-year analysis for the frequency of conditions that are favorable for mountain wave formation is estimated. The Foehn impact on wind was observed in the Lukmanier domain. We attempt quantification of the probability of occurrence using the Foehnix model. The result shows a high probability of Foehn occurrence during the winter and early spring seasons. Our study highlights the importance of incorporating complex terrain-related meteorological events into the wind energy assessment. Furthermore, for an accurate assessment of wind speed in complex terrain, our study suggests the necessity to have a better representation of the topography compared to COSMO-1E.

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“…The remarkable performance of machine learning techniques, particularly Linear Regression (LR) and Artificial Neural Networks (ANNs), in predictive modeling, highlights the promising role of machine learning in improving renewable energy systems. This underscores the significance of employing machine learning to advance the prediction abilities within complex systems, thereby fostering the development of more effective and sustainable energy solutions [13,14].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Pitch Response of Floating Offshore Wind Turbines with a Deep Learning Model

Barooni,

Velioglu Sogut

2024

Clean Technol.

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The design and optimization of floating offshore wind turbines (FOWTs) pose significant challenges, stemming from the complex interplay among aerodynamics, hydrodynamics, structural dynamics, and control systems. In this context, this study introduces an innovative method for forecasting the dynamic behavior of FOWTs under various conditions by merging Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) network. This model outperforms traditional numerical models by delivering precise and efficient predictions of dynamic FOWT responses. It adeptly handles computational complexities and reduces processing duration, while maintaining flexibility and effectively managing nonlinear dynamics. The model’s prowess is showcased through an analysis of a spar-type FOWT in a multivariate parallel time series dataset using the CNN–GRU structure. The outcomes are notably promising, underscoring the model’s proficiency in accurately forecasting the performance of FOWTs.

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A review of wind turbines in complex terrain

Cited by 54 publications

References 96 publications

A Review on Energy Management of Community Microgrid with the use of Adaptable Renewable Energy Sources

A Review on Energy Management of Community Microgrid with the use of Adaptable Renewable Energy Sources

Influence of air flow features on alpine wind energy potential

Forecasting Pitch Response of Floating Offshore Wind Turbines with a Deep Learning Model

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