Review on layout optimization strategies of offshore parks for wave energy converters

Teixeira-Duarte, Felipe; Clemente, Daniel; Giannini, Gianmaria; Rosa-Santos, Paulo; Taveira-Pinto, Francisco

doi:10.1016/j.rser.2022.112513

Cited by 27 publications

(3 citation statements)

References 82 publications

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“…The first to be discussed are based on evolution and natural selection-Evolutionary Algorithms. Distinct techniques exist, such as Genetic Programming, Evolutionary Programming, Evolution Strategies, Differential Evolution and Genetic Algorithms [18]. However, the key premises remain [43]:…”

Section: Direct Algorithms 221 Evolutionary Algorithmsmentioning

confidence: 99%

“…By applying a set of rules to these simplistic artificial lifeforms-separation (direct contact/collision avoidance), alignment (movement towards the average direction of local units) and cohesion (movement towards the centre of gravity of local units)-higher-level complex behaviours emerge, which can be tuned to solve optimization problems. Once again, distinct metaheuristic algorithms exist, such as Ant Colonies, Artificial Bee Colonies, Glowworm Swarming or Particle Swarm Optimization (PSO), among others [18]. Within the wave energy field of research, the latter is more commonly applied to solve optimization problems.…”

Section: Swarm Intelligencementioning

confidence: 99%

“…Some of the most advanced tools are supported by Big Data and employ artificial intelligence (AI) [17]. In this regard, a summarized taxonomy can be found in [18], which also features a review of WEC park layout optimization supported by AI techniques. An extended classification is provided in Figure 1, adapted to the main algorithms discussed in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Advancements on Optimization Algorithms Applied to Wave Energy Assessment: An Overview on Wave Climate and Energy Resource

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The wave energy sector has not reached a sufficient level of maturity for commercial competitiveness, thus requiring further efforts towards optimizing existing technologies and making wave energy a viable alternative to bolster energy mixes. Usually, these efforts are supported by physical and numerical modelling of complex physical phenomena, which require extensive resources and time to obtain reliable, yet limited results. To complement these approaches, artificial-intelligence-based techniques (AI) are gaining increasing interest, given their computational speed and capability of searching large solution spaces and/or identifying key study patterns. Under this scope, this paper presents a comprehensive review on the use of computational systems and AI-based techniques to wave climate and energy resource studies. The paper reviews different optimization methods, analyses their application to extreme events and examines their use in wave propagation and forecasting, which are pivotal towards ensuring survivability and assessing the local wave operational conditions, respectively. The use of AI has shown promising results in improving the efficiency, accuracy and reliability of wave predictions and can enable a more thorough and automated sweep of alternative design solutions, within a more reasonable timeframe and at a lower computational cost. However, the particularities of each case study still limit generalizations, although some application patterns have been identified—such as the frequent use of neural networks.

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Section: Direct Algorithms 221 Evolutionary Algorithmsmentioning

confidence: 99%

Section: Swarm Intelligencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advancements on Optimization Algorithms Applied to Wave Energy Assessment: An Overview on Wave Climate and Energy Resource

et al. 2023

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Towards efficient control synthesis for nonlinear wave energy conversion systems: impedance-matching meets the spectral-domain

Bonfanti,

Faedo,

Mattiazzo

2024

Nonlinear Dyn

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Existing studies within the literature that focus on designing parametric energy-maximizing controllers for Wave Energy Converter (WEC) systems predominantly rely on the impedance-matching (IM) principle, originally developed for linear time-invariant systems. Alternatively, iterative optimization routines are commonly employed for nonlinear WECs. However, these approaches often face a trade-off between effectiveness in maximizing energy extraction and computational efficiency. To address this limitation, this study proposes a computationally efficient controller tuning method for analogous synthesis in the case of nonlinear WECs. The proposed approach combines a statistical linearization technique known as spectral-domain modeling with the IM principle, to synthesize a Proportional–Integrative (PI) controller for a nonlinear WEC. Furthermore, a comparison is performed with two other synthesis methods: one based on a standard (i.e. linear) frequency-domain representation of the WEC that incorporates the IM principle, and the other employing a gradient-free optimization routine applied to the nonlinear time-domain model of the WEC for PI parameter tuning through exhaustive numerical search. A discussion on the effectiveness of each tuning method in maximizing energy absorption is provided, including an appraisal of their associated computational time requirements. Numerical analyses demonstrate that the proposed method, which integrates spectral-domain modeling and IM, can achieve (almost) optimal PI controller design for a nonlinear WEC. Furthermore, this study addresses the inaccuracies inherent in the frequency-domain approach and significantly reduces the computational time compared to the exhaustive search procedure. The findings of this research represent a significant advancement towards the development of simple, effective, and efficient IM-based techniques for synthesis of controllers in nonlinear WEC systems

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Farms of Wave Energy Converters and Grid Integration

Temiz,

Göteman

2024

Encyclopedia of Renewable Energy, Sustainability and the Environment

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Review on layout optimization strategies of offshore parks for wave energy converters

Cited by 27 publications

References 82 publications

Advancements on Optimization Algorithms Applied to Wave Energy Assessment: An Overview on Wave Climate and Energy Resource

Advancements on Optimization Algorithms Applied to Wave Energy Assessment: An Overview on Wave Climate and Energy Resource

Towards efficient control synthesis for nonlinear wave energy conversion systems: impedance-matching meets the spectral-domain

Farms of Wave Energy Converters and Grid Integration

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