Non-centralised coordinated optimisation for maximising offshore wind farm power via a sparse communication architecture

Shu, Tong; Song, Dongran; Joo, Young Hoon

doi:10.1016/j.apenergy.2022.119705

Cited by 11 publications

(3 citation statements)

References 46 publications

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“…These models often require solving complex partial differential equations, leading to the development of numerical optimization algorithms like game theoretic (GT) [38,39], sequence quadratic program (SQP) [40,41], and alternating direction method of multipliers (ADMM) [42]. Additionally, a hybrid method combining ADMM and SQP are proposed according to the wake coupling degree [43]. However, numerical optimization algorithms often struggle with nonconvex optimization problems, leading to growing interest in artificial intelligence algorithms.…”

Section: Wake Control 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: Wake Control 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

Self Cite

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“…Assumptions were formulated as functions of site-specific variables (distance to shore, water depth, and annual energy production). Shu et al (2022) worked on optimizing offshore wind farm output using a non-centralized approach. The study evaluated a wind farm comprising 36 wind turbines.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the wind energy potential and economic viability of selected sites along Nigeria’s coastal and offshore locations

et al. 2023

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Wind energy is a promising sector in the power generation industry because it is renewable and globally available. In this research work, the wind energy potential and the economic viability of using wind turbines to generate electricity in some selected sites along Nigeria’s coastline and offshore locations were evaluated. Using the statistical two-parameter Weibull probability density function method, wind speed data retrieved from an indigenous oceanography company and global information system (GIS) were analyzed for wind energy harvest. The energy output, unit cost of electricity generated by three commercially available wind turbine models (3 MW, 4 MW, and 6 MW), net present value (NPV), and payback period were evaluated. Levelized cost of electricity (LCOE) sensitivity to the discount rate, foundation cost, and turbine lifespan were also examined. The findings from the study showed that the offshore sites have four times greater wind power potential than the coastal sites. The offshore sites can be categorized as “class IIIb” wind sites, making the locations suitable for wind energy harvest. The techno-economic analysis showed that the net gains from investing in a 60-MW wind farm in the region can be as high as $62,000,000.00, while the project payback time can be as low as 5.74 years. Two of the offshore sites are recommended for the development of an offshore wind farm in the country because of their relatively low LCOE (0.04 $/kWh), higher NPV, and lower investment payback time. The Vesta-117 model wind turbine is the most suitable wind turbine system and recommended for use in the region because of its low cut-in speed (3 m/s). Sensitivity analysis showed that the LCOE of offshore-01 was reduced by 31% when the lifespan of the V117 turbine was increased from 20 to 25 years. The results also showed that reductions in the discount rate and foundation cost positively affect the LCOE.

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“…An adaptive wake intensity threshold algorithm has been developed to determine the partition of turbines, and a pruned wake direction graph was established [18,19]. A wake-based graph partitioning strategy was proposed, which applied the graph sparseness algorithm to achieve multiple smaller turbine subsets [20,21]. Nonetheless, the sparseness constraint condition is challenging to determine.…”

Section: Introductionmentioning

confidence: 99%

Non-Centralised Balance Dispatch Strategy in Waked Wind Farms through a Graph Sparsification Partitioning Approach

Shu,

Joo

2023

Energies

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A novel non-centralised dispatch strategy is presented for wake redirection to optimise large-scale offshore wind farms operation, creating a balanced control between power production and fatigue thrust loads evenly among the wind turbines. This approach is founded on a graph sparsification partitioning strategy that takes into account the impact of wake propagation. More specifically, the breadth-first search algorithm is employed to identify the subgraph based on the connectivity of the wake direction graph, while the PageRank centrality computation algorithm is utilised to determine and rank scores for the shared turbines’ affiliation with the subgraphs. By doing so, the wind farm is divided into smaller subsets of partitioned turbines, resulting in decoupling. The objective function is then formulated by incorporating penalty terms, specifically the standard deviation of fatigue thrust loads, into the maximum power equation. Meanwhile, the non-centralisation sequential quadratic programming optimisation algorithm is subsequently employed within each partition to determine the control actions while considering the objectives of the respective controllers. Finally, the simulation results of case studies prove to reduce computational costs and improve wind farm power production by balancing accumulated fatigue thrust loads over the operational lifetime as much as possible.

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Non-centralised coordinated optimisation for maximising offshore wind farm power via a sparse communication architecture

Cited by 11 publications

References 46 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

Assessment of the wind energy potential and economic viability of selected sites along Nigeria’s coastal and offshore locations

Non-Centralised Balance Dispatch Strategy in Waked Wind Farms through a Graph Sparsification Partitioning Approach

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