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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 21242

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Dr. Mehdi Neshat

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Guest Editor

Center for Artificial Intelligence Research and Optimisation, Torrens University Australia, Adelaide, SA 5000, Australia
Interests: supervised learning; deep learning; optimisation; evolutionary computations; meta-heuristic algorithms; swarm intelligence; renewable energy systems
Special Issues, Collections and Topics in MDPI journals

Dr. Soheil Esmaeilzadeh

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Guest Editor

Department of Energy Resources Engineering, Stanford University, Stanford, CA, USA
Interests: computational science; numerical modelling; wave energy; applied deep learning

Special Issue Information

Dear Colleagues,

In recent years, wave energy related technologies have developed considerably, and have garnered more and more interest and support from the energy industries as a promising alternative energy resource. This is mainly because wave energy has the highest power density compared to solar and wind energy sources, while having minimal environmental impact. Furthermore, in some real world deployments, multiple converters, which were laid out in a wave farm, are able to harness power from the waves more than 90% of the time. However, these technologies are not fully developed and immature compared to wind renewable technologies. For this reason, to develop the commercialization of ocean wave energy technologies and maximize the total power output of a wave farm, a wide range of optimization techniques have been performed including various numerical, genetic, swarm, and evolutionary algorithms. Recently, in order to develop different components of wave energy converters such as geometry parameters, layout and power take-off settings, advanced and hybrid optimization frameworks have been proposed. These modern optimization methods show a reliable and high-level performance compared with traditional optimization techniques.

This Special Issue invites articles that incorporate the application of state-of-the-art optimization methods in wave and tidal energy. Subjects of interest for this issue cover, but are not limited to the following fields:

Dr. Mehdi Neshat
Dr. Soheil Esmaeilzadeh
Guest Editors

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Keywords

optimization of control strategies and power take-off settings
layout optimization of wave energy converters
optimization of configurations, connection, and mooring
geometry optimization of wave energy converters
techno-Economic optimization for wave energy technologies

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Published Papers (10 papers)

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Research

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18 pages, 12505 KiB

Open AccessArticle

Experimental Investigation of a Hybrid Device Combining a Wave Energy Converter and a Floating Breakwater in a Wave Flume Equipped with a Controllable Actuator

by Luca Martinelli, Giulio Capovilla, Matteo Volpato, Piero Ruol, Chiara Favaretto, Eva Loukogeorgaki and Mauro Andriollo

Energies 2024, 17(1), 40; https://doi.org/10.3390/en17010040 - 21 Dec 2023

Cited by 2 | Viewed by 1213

Abstract

This paper presents a hydrodynamic investigation carried out on the “Wave Attenuator” device, which is a new type of floating breakwater anchored with piles and equipped with a linear Power Take Off (PTO) mechanism, which is typical for wave energy converters. The device is tested in the wave flume, under regular waves, in slightly non-linear conditions. The PTO mechanism, that restrains one of the two degrees of freedom, is simulated through an actuator and a programmable logic controller with preassigned strategy. The paper presents the system identification procedure followed in the laboratory, supported by a numerical investigation essential to set up a credible control strategy aiming at maximizing the wave energy harvesting. The maximum power conversion efficiency under the optimal PTO control strategy is found: it is of order 50–70% when the incident wave frequency is lower than the resonance one, and only of order 20% for higher frequencies. This type of experimental investigation is essential to evaluate the actual efficiency limitations imposed by device geometry. Full article

(This article belongs to the Special Issue Wave Energy Technologies and Optimization Methods)

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26 pages, 10169 KiB

Open AccessArticle

Implementation Process Simulation and Performance Analysis for the Multi-Timescale Lookup-Table-Based Maximum Power Point Tracking under Variable Irregular Waves

by Xuhui Yue, Feifeng Meng, Zhoubo Tong, Qijuan Chen, Dazhou Geng and Jiaying Liu

Energies 2023, 16(22), 7501; https://doi.org/10.3390/en16227501 - 9 Nov 2023

Viewed by 1201

Abstract

The efficacy of the multi-timescale lookup-table-based maximum power point tracking (MLTB MPPT) in capturing energy at various fixed sea states has already been demonstrated. However, it remains imperative to conduct a more comprehensive evaluation of the MPPT tracking performance under varying sea states in practical scenarios. Additionally, it is crucial to engage in an in-depth analysis of the dynamic process and energy loss/consumption associated with MLTB MPPT implementations. This paper focuses on the implementation process simulation and performance analysis for the MLTB MPPT under variable irregular waves. Firstly, the structure of the wave power controller based on a MLTB MPPT algorithm is described in detail, as well as that of a controlled plant, known as a novel inverse-pendulum wave energy converter (NIPWEC). Secondly, mathematical models for the MLTB MPPT are developed, taking into account the efficiency of each link. In this paper, we present simplified modelling methods for both permanent magnet synchronous generator (PMSG) vector control and permanent magnet synchronous motor (PMSM) servo control. Finally, the tracking performance of the MLTB MPPT in the presence of variable irregular waves is comprehensively analyzed by simulating the implementation process and comparing it with two other MPPT algorithms, i.e., the frequency- and amplitude-control-based MPPT and the lookup-table-based internal mass position adjustment combined with the optimal fixed damping search. Results show that the MLTB MPPT (Method 2) is a competitive algorithm. Besides, a significant portion (>12%) of the time-averaged absorbed power is actually lost during the power generation process. On the other hand, the power required for a mass-position-adjusting mechanism is relatively small (approximately 0.2 kW, <1.5%). The research findings can offer theoretical guidance for optimizing the operation of NIPWEC engineering prototypes under actual sea conditions. Full article

(This article belongs to the Special Issue Wave Energy Technologies and Optimization Methods)

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20 pages, 3636 KiB

Open AccessArticle

Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study

by Fatemehsadat Mirshafiee, Emad Shahbazi, Mohadeseh Safi and Rituraj Rituraj

Energies 2023, 16(1), 502; https://doi.org/10.3390/en16010502 - 2 Jan 2023

Cited by 8 | Viewed by 2549

Abstract

This study proposes a data-driven methodology for modeling power and hydrogen generation of a sustainable energy converter. The wave and hydrogen production at different wave heights and wind speeds are predicted. Furthermore, this research emphasizes and encourages the possibility of extracting hydrogen from ocean waves. By using the extracted data from the FLOW-3D software simulation and the experimental data from the special test in the ocean, the comparison analysis of two data-driven learning methods is conducted. The results show that the amount of hydrogen production is proportional to the amount of generated electrical power. The reliability of the proposed renewable energy converter is further discussed as a sustainable smart grid application. Full article

(This article belongs to the Special Issue Wave Energy Technologies and Optimization Methods)

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19 pages, 4161 KiB

Open AccessArticle

Shape Optimization of Oscillating Buoy Wave Energy Converter Based on the Mean Annual Power Prediction Model

by Tiesheng Liu, Yanjun Liu, Shuting Huang and Gang Xue

Energies 2022, 15(20), 7470; https://doi.org/10.3390/en15207470 - 11 Oct 2022

Cited by 4 | Viewed by 1972

Abstract

In order to improve the energy capture efficiency of an oscillating buoy wave energy converter (WEC), a buoy-shape optimization design method based on the mean annual power prediction model is proposed. According to the statistical data of long-term wave characteristics in the Chinese sea area, the optimal design space is determined. Sixty-three sample points were randomly selected in the optimized space. Based on simulation, the mean annual power corresponding to each sample point is calculated to quantitatively describe the energy capture ability. The response surface method (RSM), radial basis function neural network (RBFNN), and elliptical basis functions neural network (EBFNN) are used to establish the mean annual power prediction models, respectively. By combining the prediction model with the multi-island genetic algorithm (MIGA), the optimal solution in the design space is easily obtained. The reliability of the optimal solution is further proved by quantitative analysis about the influence of optimization parameters on the mean annual captured power. Compared with the common RSM and RBFNN methods, the prediction model established by the EBFNN method has a higher prediction accuracy. In the optimization process, the simulation calculation is replaced by a prediction model, which can effectively solve the problem of high simulation calculation cost. Full article

(This article belongs to the Special Issue Wave Energy Technologies and Optimization Methods)

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17 pages, 15283 KiB

Open AccessFeature PaperArticle

Peak Shaving Methods of Distributed Generation Clusters Using Dynamic Evaluation and Self-Renewal Mechanism

by Hongwei Li, Qing Xu, Shitao Wang and Huihui Song

Energies 2022, 15(19), 7036; https://doi.org/10.3390/en15197036 - 25 Sep 2022

Cited by 1 | Viewed by 1418

Abstract

As one of the power auxiliary services, peak shaving is the key problem to be solved in the power grid. With the rapid development of DGs, the traditional peak shaving scheduling method for centralized adjustable energy is no longer applicable. Thus, this paper proposes two-layer optimization methods of allocating the peak shaving task for DGs. Layer 1 mainly proposes four evaluation indexes and the peak shaving priority sequence can be obtained with modified TOPSIS, then the DG cluster’s task is allocated to the corresponding DGs. On the basis of dynamic evaluation and the self-renewal mechanism, layer 2 proposes a peak shaving optimization model with dynamic constraints which assigns peak shaving instructions to each cluster. Finally, the effectiveness of the method is verified by using the real DGs data of a regional power grid in China based on the MATLAB simulation platform. The results demonstrate that the proposed methods can simply the calculation complexity by ranking the DGs in the peak shaving task and update the reliable aggregate adjustable power of each cluster in time to allocate more reasonably. Full article

(This article belongs to the Special Issue Wave Energy Technologies and Optimization Methods)

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17 pages, 3098 KiB

Open AccessArticle

An Equivalent Model of Wind Farm Based on Multivariate Multi-Scale Entropy and Multi-View Clustering

by Ji Han, Li Li, Huihui Song, Meng Liu, Zongxun Song and Yanbin Qu

Energies 2022, 15(16), 6054; https://doi.org/10.3390/en15166054 - 21 Aug 2022

Cited by 2 | Viewed by 1498

Abstract

Wind farm (WF) equivalence is an effective method to achieve accurate and efficient simulation of large-scale WF. Existing equivalent models are generally suitable for one certain or very few scenarios, and have difficulty reflecting the multiple aspects of dynamic processes of WF. Aiming at these problems, this paper proposes an equivalent model of WF based on multivariate multi-scale entropy (MMSE) and multi-view clustering. Firstly, the influence of the factors on the dynamic process of the wind turbine (WT) is discussed, including control mode, wind speed and its wake effect, resistance of crowbar resistor and so on. The relationship between these factors and the dynamic equivalence of WF is analyzed. Secondly, an overview of MMSE is given, and the applicability of MMSE on WF equivalence is analyzed. On this basis, this paper proposes the extraction process of a WT clustering indicator using MMSE. Then, the multi-view fuzzy C means (MV-FCM) algorithm is used for the clustering of WTs, and the equivalent model of WF is obtained after calculating the equivalent parameters. Finally, the IEEE14 power system including WF is simulated. The results show that the equivalent model could be applied to dynamic process simulation in various fault scenarios of power systems, and the error is small when the cluster number is 4. Compared with the detailed model, the simulation time of the WF equivalent model proposed in this paper is shortened by 86%, and the simulation accuracy is improved by about 44% compared with the comparative model. Full article

(This article belongs to the Special Issue Wave Energy Technologies and Optimization Methods)

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24 pages, 5560 KiB

Open AccessArticle

The Prospect of Combining a Point Absorber Wave Energy Converter with a Floating Offshore Wind Turbine

by David M. Skene, Nataliia Sergiienko, Boyin Ding and Benjamin Cazzolato

Energies 2021, 14(21), 7385; https://doi.org/10.3390/en14217385 - 5 Nov 2021

Cited by 11 | Viewed by 2709

Abstract

With recent advances in offshore floating wind and wave energy technology, questions have emerged as to whether the two technologies can be combined to reduce their overall levelised cost of energy. In this paper, the potential for combining a floating offshore wind turbine to a point absorbing wave energy converter is investigated. The focus of the investigation is how much power might be produced by a combined floating wind and wave energy converter system, and the resultant changes in motion of the floating wind platform. A model for the combined wave and wind system is developed which uses the standardised NREL OC3 5 MW spar type wind turbine and a cylindrical buoyant actuator (BA), which is attached to the spar via a generic wave power take-off system (modelled as a spring-damper system). Modelling is conducted in the frequency domain and the tests span a wide range of parameters, such as wave conditions, BA sizes, and power take-off coupling arrangements. It is found that the optimal (with respect to power production) BA size is a draft and radius of approximately 14 m. It is found that this BA can theoretically produce power in the range of 0.3 to 0.5 MW for waves with a significant wave height of 2 m, and has the potential to produce power greater or near to 1 MW for waves with a significant wave height of at least 3 m. However, it is also found that, in terms of the relative capture width, significantly smaller BAs are optimal, and that these smaller BA sizes less significantly alter the motion of the floating wind platform. Full article

(This article belongs to the Special Issue Wave Energy Technologies and Optimization Methods)

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11 pages, 7688 KiB

Open AccessArticle

Low-RPM Torque Converter (LRTC)

by Andrej Savin, Dana Salar and Erik Hultman

Energies 2021, 14(16), 5071; https://doi.org/10.3390/en14165071 - 18 Aug 2021

Cited by 3 | Viewed by 2212

Abstract

The concept concerned in this paper is based on energy conversion of the ocean waves via rotational generators. The objective of this research is to develop a new type of slow-motion converter. The LRTC device consists of a drum that is connected via wire to a floating buoy. The drum is connected to rotary generators. The generators are heavily braked when the direction of movement changes (up/down); this is because the generators have been charged the maximum load in order to obtain maximum output power. For upcoming improvement, the generators should have some power storage as flywheel. In the future experiments, the torque converter can even be tuned to rotate in resonance with the incoming waves, strongly increasing power absorption. Constant force springs are applied for this purpose. The focus of this project is, therefore, a new generation of wave power device for utility-scale energy conversion offering a cost of energy that can compete with established energy resources. Full article

(This article belongs to the Special Issue Wave Energy Technologies and Optimization Methods)

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17 pages, 4351 KiB

Open AccessArticle

Multi-Mode Wave Energy Converter Design Optimisation Using an Improved Moth Flame Optimisation Algorithm

by Mehdi Neshat, Nataliia Y. Sergiienko, Seyedali Mirjalili, Meysam Majidi Nezhad, Giuseppe Piras and Davide Astiaso Garcia

Energies 2021, 14(13), 3737; https://doi.org/10.3390/en14133737 - 22 Jun 2021

Cited by 17 | Viewed by 2446

Abstract

Ocean renewable wave power is one of the more encouraging inexhaustible energy sources, with the potential to be exploited for nearly 337 GW worldwide. However, compared with other sources of renewables, wave energy technologies have not been fully developed, and the produced energy price is not as competitive as that of wind or solar renewable technologies. In order to commercialise ocean wave technologies, a wide range of optimisation methodologies have been proposed in the last decade. However, evaluations and comparisons of the performance of state-of-the-art bio-inspired optimisation algorithms have not been contemplated for wave energy converters’ optimisation. In this work, we conduct a comprehensive investigation, evaluation and comparison of the optimisation of the geometry, tether angles and power take-off (PTO) settings of a wave energy converter (WEC) using bio-inspired swarm-evolutionary optimisation algorithms based on a sample wave regime at a site in the Mediterranean Sea, in the west of Sicily, Italy. An improved version of a recent optimisation algorithm, called the Moth–Flame Optimiser (MFO), is also proposed for this application area. The results demonstrated that the proposed MFO can outperform other optimisation methods in maximising the total power harnessed from a WEC. Full article

(This article belongs to the Special Issue Wave Energy Technologies and Optimization Methods)

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Review

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28 pages, 3550 KiB

Open AccessReview

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

by Daniel Clemente, Felipe Teixeira-Duarte, Paulo Rosa-Santos and Francisco Taveira-Pinto

Energies 2023, 16(12), 4660; https://doi.org/10.3390/en16124660 - 12 Jun 2023

Cited by 3 | Viewed by 1939

Abstract

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. Full article

(This article belongs to the Special Issue Wave Energy Technologies and Optimization Methods)

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