Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

Yousef, Latifa A.; Yousef, Hibba; Rocha-Meneses, Lisandra

doi:10.3390/en16248057

Cited by 9 publications

(1 citation statement)

References 150 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Integrating the information on the impact of complex terrain phenomenon with machine learning [i.e. Dujardin and Lehning (2022)] and Digital Twin and its integration to Geographical Information System (GIS) (Agostinelli et al, 2022;Yousef et al, 2023;Piras et al, 2024) will also increase the accuracy of monitoring and performance prediction of wind turbines. The present research acts as a step forward in accurately estimating potential wind power and optimally using it for renewable energy production, particularly during periods when it is most needed, i.e., the winter season when power demand is high and other renewable sources are limited.…”

Section: Discussionmentioning

confidence: 99%

Influence of air flow features on alpine wind energy potential

Kristianti,

Gerber,

Gonzàlez-Herrero

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

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.

show abstract