Micro sitting of floating wind turbines in a wind farm using a multi-criteria framework

Díaz, Hugo; Silva, D.; Bernardo, C.; Soares, C. Guedes

doi:10.1016/j.renene.2023.01.016

Cited by 15 publications

(6 citation statements)

References 76 publications

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“…As shown in Table 6, the primary methods include metaheuristic algorithms and reinforcement learning algorithms. Additionally, global optimization algorithms [89][90][91][92][93] and multicriteria decision methods [94] are also employed. Due to their broad applicability and ease of use, metaheuristic algorithms are widely reported in most literature for solving offshore wind farm layout optimization problems.…”

Section: Layout Optimization Of Offshore Wind Farmsmentioning

confidence: 99%

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Song,

Shen,

Huang

et al. 2024

JMSE

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As global energy crises and climate change intensify, offshore wind energy, as a renewable energy source, is given more attention globally. The wind power generation system is fundamental in harnessing offshore wind energy, where the control and design significantly influence the power production performance and the production cost. As the scale of the wind power generation system expands, traditional methods are time-consuming and struggle to keep pace with the rapid development in wind power generation systems. In recent years, artificial intelligence technology has significantly increased in the research field of control and design of offshore wind power systems. In this paper, 135 highly relevant publications from mainstream databases are reviewed and systematically analyzed. On this basis, control problems for offshore wind power systems focus on wind turbine control and wind farm wake control, and design problems focus on wind turbine selection, layout optimization, and collection system design. For each field, the application of artificial intelligence technologies such as fuzzy logic, heuristic algorithms, deep learning, and reinforcement learning is comprehensively analyzed from the perspective of performing optimization. Finally, this report summarizes the status of current development in artificial intelligence technology concerning the control and design research of offshore wind power systems, and proposes potential future research trends and opportunities.

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Section: Layout Optimization Of Offshore Wind Farmsmentioning

confidence: 99%

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Song,

Shen,

Huang

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…There are many papers on layout optimization of wind farms (e.g., [1,2,3,4,5,6]), and a number of studies on layout optimization specific to offshore wind farms (e.g., [7,8,9]). Within this literature, methods for optimizing turbine positions within a bounded area are well [3].…”

Section: Introductionmentioning

confidence: 99%

“…Some recent studies have looked into optimizing floating wind farm layouts. Diaz et al [6] developed an approach for placing turbines within a lease area based on a mix of technical and space-use constraints. Rapha [10] developed a layout optimization tool that includes some prescribed adjustment to mooring and cabling designs based on depth variations.…”

Section: Introductionmentioning

confidence: 99%

Floating Wind Farm Layout Optimization Considering Moorings and Seabed Variations

Hall,

Biglu,

Housner

et al. 2024

J. Phys.: Conf. Ser.

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This paper presents a method for optimizing the layout of floating wind farms that accounts for realistic seabed variations and the consequent adjustments to the mooring systems required for different turbine positions. The mooring lines of floating wind farms create large spatial constraints that are depth-dependent, since mooring designs must adapt to variations in seabed conditions over the array area. We develop a layout optimization methodology that addresses this, adjusting mooring system designs based on the local seabed characteristics as the layout changes and using steady-state models for the wake effects and mooring lines. The approach includes design algorithms that adjust the anchor positions and line length to achieve the desired mooring line profile for different water depths, and a layout optimization framework that implements spatial constraints between the turbines, mooring lines, and lease area boundaries. Demonstrating the method on several cases shows the effect of the seabed and spatial-constraint factors, as well as their interactions, on the optimal array layout. This demonstration paves the way for scaling up the method, using more powerful optimization algorithms to handle larger farm sizes and situations with more intensely varied seabed conditions.

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“…In fact, onshore wind farm geometries mostly depend on terrain orography, whereas offshore environment offers a more flexible room for layout optimization. There has been a lot of effort studying the wind farm design in the literature, whose research focus span from initial feasibility analysis of a certain site to detailed wind farm design optimizations using different methods, for example, multiple criteria decision analysis [1] and multidisciplinary analysis and optimization [2].…”

Section: Introductionmentioning

confidence: 99%

Floating wind farm design using social and environmental constraints

Yilmazlar,

White,

Cacciola

et al. 2024

J. Phys.: Conf. Ser.

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This work proposes a wind farm design methodology, integrating several design variables from different design aspects into an optimization problem, such as turbine power, number of turbines, cable types, wind farm layout and site location. A mixed integer genetic algorithm is employed to combine discrete and continuous design variables to find the design with minimum Levelized Cost of Energy (LCoE). The wind farm LCoE is calculated using a wind farm model, which combines detailed cost functions for different cost elements and engineering wake models to estimate Annual Energy Production. The design is constrained not only by technical aspects such as total power, farm area, but also by environmental and social constraints. These include the wind farm visibility, which is a popular concern among the people living along the coast of nearby wind farms, and availability, implying the compliance with the designated exclusion zones. To demonstrate the sensitivity of design constraints, two floating wind farms are sited and optimized as a case study, using different visibility constraints. The developed design tool within this study aims to bridge the gap between wind farm developers and local authorities who are responsible for permitting and zoning of development areas.

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Micro sitting of floating wind turbines in a wind farm using a multi-criteria framework

Cited by 15 publications

References 76 publications

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Floating Wind Farm Layout Optimization Considering Moorings and Seabed Variations

Floating wind farm design using social and environmental constraints

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