Wave Farm Research Articles

Triple-layered chaotic differential evolution algorithm for layout optimization of offshore wave energy converters

Renewable energy sources are progressively assuming a pivotal role in shaping our future, and wave energy stands out as a promising avenue due to its substantial potential and minimal ecological impact. Consequently, extensive research has been directed towards optimizing the layout of wave energy converters (WECs). However, as the number of optimized buoys increases, so does the complexity of calculating hydrodynamic interactions, placing great demands on computing power. Simultaneously, these dynamic interactions can yield either constructive or detrimental outcomes, amplifying the intricacy of layout evaluation. Effectively and promptly determining the optimal buoy arrangement to achieve high energy output efficiency emerges as a pivotal research challenge. We propose a chaos-based differential evolutionary algorithm with a three-layer information structure, including excavation, balancing, and recycling layers, which reasonably adjusts the population structure and makes full use of individual information to optimize the next exploration by combining with chaotic maps. We compared our method with other state-of-the-art intelligent algorithms applied to the problem of wave energy generators and tested it in four real wave scenarios (Perth, Adelaide, Tasmania, and Sydney) using numerical modeling to calculate its energy output. The experimental results show that our improved strategy improves the energy output of the oscillating buoy-type wave energy generator by an average of 101.5%, 93.1%, 23.1%, and 0.7% compared to the mainstream state-of-the-art algorithm for the four scenarios, respectively.

Adaptive systematic optimization of a multi-axis ocean wave energy converter

Due to the growing global energy needs, renewable energy systems, particularly wave energy converters (WECs), are a feasible solution to satisfy current energy demand. Recently, wave farms with diverse technologies, geometries, and layouts have been developed; however, evaluating the performance of these devices is complicated and requires precise hydrodynamic modeling to efficiently deploy wave farms. This study proposes a multi-scenario model using boundary element method (BEM) solver, NEMOH, integrated with evolutionary many-objective algorithms to evaluate the performance of a multi-axis point absorber WEC with respect to cylindrical, triangular, quadrilateral, and octagonal geometries and varying dimensions, that is, radius, draft, and height. To this end, six objective functions were considered to maximize the energy absorption and significant velocity and to minimize the separation distance, levelized cost of energy, net present value, and q-factor. Accordingly, three EMnO frameworks were utilized: the non-dominated sorings genetic algorithm (NSGA-III), reference point-based NSGA-III (R-NSGA-III), and multi-objective evolutionary algorithm by decomposition (MOEA/D). The results of the three optimization algorithms indicate that R-NSGA-III converges faster than the other two and also found that the cylindrical and octagonal geometries produce more annual energy among other forms. Comparing the performances of the three different layouts for cylindrical and octagonal geometries reveals that the arrow layout with thirty buoys produced more energy and had a lower levelized cost of energy and net present value.

A novel framework for wave power plant site selection and wave forecasting based on GIS, MCDM, and ANN methods: A case study in Hainan Island, Southern China

Wave power plants (WPPs) hold significant promise for sustainable energy generation but face complex challenges in site selection and wave forecasting. To address these challenges, this study presents an integrated methodology that combines geographic information system (GIS), multi-criteria decision making (MCDM) and artificial neural network (ANN) techniques. The primary objective of this research is to identify optimal sites for WPP deployment and enhance the accuracy of wave power density (WPD) forecasting. The study focuses on Hainan Island, the second-largest island in China, and employs both macro-scale and micro-scale site selection methods. It establishes a comprehensive criteria system that encompasses environmental, technical, economic, and social aspects. To improve the objectivity of criteria weighting, a group decision making-analytic hierarchy process (GDM-AHP) approach is applied. The findings reveal that approximately 1.36 % (1427.93 km2) of Hainan Island's area offers suitable conditions for WPP deployment, with concentrations in the southern, northern, and eastern marine regions. Among the 27 identified alternative sites covering 208.76 km2 along the eastern coast, six optimal sites are pinpointed in Wanning, Wenchang, and Qionghai. Furthermore, the optimized ANN outperforms the standard ANN, achieving a mean relative error of 9.0 %. This research contributes to the theoretical understanding of WPP site selection and wave forecasting while offering a practical and adaptable framework for stakeholders involved in renewable energy projects, particularly in coastal areas such as Hainan Island. These results and methodologies have real-world implications for the efficient and effective deployment of wave energy projects, ultimately promoting sustainable energy generation and socioeconomic development.

Hybrid deep learning model for wave height prediction in Australia's wave energy region

Waves are emerging as a renewable energy resource, but the harnessing of such energy remains among the least developed in terms of renewable energy technologies on a regional or a global basis. To generate usable energy, wave heights must be predicted in near-real-time, which is the driving force for wave energy converters. This study develops a hybrid Convolutional Neural Network-Long Short-Term Memory-Bidirectional Gated Recurrent Unit forecast system (CLSTM-BiGRU) trained to accurately predict significant wave height (Hsig) at multiple forecasting horizons (30 min, 0.5H; 2 h, 02H; 3 h, 03H and 6 h, 06H. In this model, convolutional neural networks (CNNs), long-short-term memories (LSTMs), and bidirectional gated recurrent units (BiGRUs) are employed to predict Hsig. To construct the proposed CLSTM-BiGRU model, historical wave properties, including maximum wave height, zero-up crossing wave period, peak energy wave period, sea surface temperature, and significant wave heights are analysed. Several wave energy generation sites in Queensland, Australia were tested using the hybrid deep learning CLSTM-BiGRU model. Based on statistical score metrics, scatterplots, and error evaluations, the hybrid CLSTM-BiGRU model generates more accurate forecasts than the benchmark models. This study established the practical utility of the hybrid CLSTM-BiGRU model for modelling Hsig and therefore shows the model could have significant implications for wave and ocean energy generation systems, tidal or wave height monitoring as well as sustainable wave energy resource evaluation where a prediction of wave heights is required.

Research and concept design of wave energy converter on ocean squid jigging ship

Wave power capture applied on a squid jigging ship is assessed in this paper; this would decrease the operating cost of ships and reduce carbon emissions. Firstly, the history of wave energy devices and wave energy generation on ships are briefly reviewed. Then, a new wave energy device on an ocean squid jigging ship is designed. The linear mathematical system model of the device, and WAMIT software are used to calculate wave energy captured. The floating bodies of the device is designed to take advantage of resonance. By comparing wave capture width ratio of the float with arm hinged on the ship, it is found that wave capture width ratio of the device is highest when the arm length is half a wavelength. The numerical method is validated by experimental tests which are presented in details from setup, data treatment to wave energy capture ratio calculated. Based on a resonance criterion, 20 wave energy device layouts of the ocean squid jigging ship are designed. From numerical simulation the optimum radius of the floating body is 2 m, and the maximum annual average power among these devices is 136 kW at Northwest Pacific fishery ground, which can provide about 10 % of the power consumption for the ocean squid jigging ship.

Computationally efficient analytical O&M model for strategic decision-making in offshore renewable energy systems

To boost the deployment of all offshore renewable energy technologies, it is fundamental to adopt convenient long-term strategic operation and maintenance (O&M) decisions. Due to the lack of experience and reliable information, performing extensive sensitivity analysis is a key factor for supporting strategic O&M decision-making. By evaluating various scenarios, sensitivity analyses provide valuable insights to identify critical factors and enhance decision confidence. To that end, the development of computationally efficient O&M models, where accessibility, availability, energy, and economic aspects are adequately articulated is crucial. Simulation-based O&M models, i.e. based on Monte Carlo methods, have been widely used to incorporate those fours aspects. However, the computational burden of simulation-based O&M models is prohibitive, limiting the feasibility of conducting extensive sensitivity analyses. In view of this, this study presents a computationally efficient analytical O&M model based on Markov Chains. This analytical O&M model is compared with two case studies presented in the literature, where simulation-based O&M models are employed, studying a floating offshore wind and a wave energy farm. Results demonstrate that the analytical O&M model achieves the same level of fidelity as simulation-based models (within 10% deviation), while reducing the computational burden by at least five orders of magnitude.

Cooperative model predictive control for Wave Energy Converter arrays

It is well known that commercial Wave Energy Converters (WECs) are likely to be deployed in arrays, which gives the possibility to enhance the energy harvesting properties of the wave farm as a whole. Advanced control strategies are necessary to exploit the potential and capture as much energy as possible in a limited sea space. Centralized control is a natural benchmark, as it sees the array as a large-scale system, making energy maximization a large plantwide problem. However, centralized control has a high requirement for computational efficiency. In this paper, a cooperative model predictive control (MPC) is proposed to achieve energy maximization while reducing computational cost. A modified array model is constructed to capture the dynamics of interactions in arrays, explicitly showing the linear couplings in the system state, control input, and excitation force of each subsystem. Sensitivity analyses are performed to evaluate the performance for different array layouts, separation distances, and sea states. The results show that cooperative MPC can achieve an energy capture performance that approximates centralized MPC, outperforming decentralized MPC significantly, and is well adapted to sea states. Furthermore, the proposed method has the potential to improve computational efficiency by 14.86%–51.14% for simulations in the study.

Model predictive control based on air pressure forecasting of OWC wave power plants

Ocean waves hold a promising potential as a sustainable source of clean energy. However, in order to make wave energy converters financially viable, particularly in the case of the oscillating water column (OWC), it is crucial to optimize the power take-off system to maximize performance. Most control strategies focus solely on turbine performance, often at the expense of overlooking the overall wave-to-wire system performance. Therefore, a model predictive control (MPC) strategy using short-term air pressure prediction was proposed to enhance electrical energy delivered to the grid. The Mutriku wave power plant (Spain) was case-studied for evaluating the control approaches, which focused on maximizing average power (turbine or generator) from an OWC equipped with the innovative biradial turbine. In comparison to the reference baseline strategy, the implementation of MPC (with a 16-second forecast) resulted in a minor 2%-increase in the average turbine power. However, when it comes to the generator power accumulated over a year, the MPC strategy produced an improvement of more than 8%. Since generator power is the ultimate product, this improvement is significant. Nonetheless, the MPC strategy exhibited a non-smooth control response compared to other strategies. Improving control action quality should be a primary focus of future research.

Opportunities for wave energy in bulk power system operations

Wave energy resources have high, yet largely untapped potential as candidate generation technology. In this paper, we perform a data-driven analysis to characterize the impact of wave energy integration on bulk-scale power systems and market operations. Through data-driven sensitivity studies centered on an optimization-based production cost modeling formulation, our work characterizes the inflection point beyond which wave integration starts impacting power system operations, considering present day transmission infrastructure. Furthermore, our analysis also considers the joint effects of wave energy integration and system-wide transmission expansion. Finally, potential resilience scenarios such as wildfire-driven transmission contingencies and heat wave events are investigated, whereby the contributions of grid-integrated wave energy in alleviating the effects of the resilience events are analyzed. As our demonstration test bed, we consider a reduced-order network topology for the U.S. Western Interconnection with wave energy generation integrated at carefully selected sites across the coastal areas of Washington, Oregon, and northern California. Our results indicate that over a representative year of operations, wave energy integration systematically reduces locational marginal prices (LMPs) of energy and price volatility, especially during periods of high wave resource availability (winter months for the U.S. west coast). Average, maximum, and minimum of hourly LMPs over a typical year of operation was reduced by 2.95, 51.28, and 1.13 $/MWh respectively (over a baseline scenario with no wave energy integration), when the selected network model had a total of 5000 MW wave power installed capacity during the representative year of study. The effects of wave energy integration can remain localized with existing transmission infrastructure (identified to be most pronounced in the Pacific Northwest region in the example we studied). However, with concurrent transmission expansion, the impacts of wave energy integration are likely to have a higher geographical spread. Our results also indicate that wave energy may be able to assist power system operations during resilience events such as major transmission contingencies and heat wave events, although such benefits might be dependent on factors such as proximity of affected area to wave resources, availability of adequate resource potential and adequate transmission capacity.

Analysis of Mutriku's OWC performance

An analysis of oscillating water columns (OWC) at the Mutriku Wave Power Plant to understand their efficiency in transforming ocean energy into electricity. The study aims to estimate the rate of convergence of wave energy flux to kilowatts hour of each OWC and determine whether it is more accurate to estimate for each OWC independently or as part of a system of equations. The dataset includes electricity production data from 7 turbines in the MWPP during 2019, along with ocean measurements such as significant wave height, wave period, wave energy flux, swell direction, and wind direction. All models in the paper have varying coefficients which change with the significant wave height. The methodology includes the use of generalized least squares and local polynomial kernel methodologies for coefficient estimation.

Optimization of the chamber of OWC to improve hydrodynamic performance

Nearly 71% of Earth's surface area is occupied by the ocean, which is abundant in energy, with wave energy being one of its essential components. As a result, the investigation of wave energy generation devices is a crucial matter. This paper analyzes the performance of the oscillating water column (OWC) wave energy conversion device under the influence of unidirectional regular waves and optimizes the OWC device accordingly. The impact of different structural dimensions on the first-stage conversion efficiency of the OWC device is determined. Moreover, to evaluate the performance of the OWC device in real sea conditions, the wave climate at Sanmen Gorge is employed as the experimental parameter. The study examines the performance and characteristic response of the OWC device regarding the air chamber width, thickness of the front wall, and draught of the front wall. The findings reveal that the width of the OWC air chamber and the draught of the front wall significantly affect the OWC performance. Decreasing the width of the air chamber results in a stable horizontal plane, eliminating the occurrence of standing wave phenomena. As the draft of the front wall increases, the reflection coefficient of the air chamber gradually increases, and it becomes difficult for the wave to penetrate into the air chamber section. The Latin hypercube design experiment method was used to select experimental points and establish quadratic polynomial response functions. The OWC device was optimized based on multi-island genetic algorithm. The simulation and fault tolerant optimization methods presented in this paper can be widely used to improve system performance.

Techno-economic feasibility of marine eco-parks driven by wave energy: A case study at the coastal arid region of Mexico

Coastal arid regions face significant challenges in ensuring energy, water, and food security. Given the urgency of meeting these demands, it is necessary to consider innovative solutions that enhance adaptive capacity and sustainable development of coastal zones. Harnessing wave energy has become a potential ocean renewable resource that can provide significant benefits to mitigating coastal vulnerability and climate change. However, most wave energy projects still face different challenges that limit their financing and commercial deployment. This paper aims to evaluate the techno-economic feasibility of 0.5 MW Marine Ecoparks as maritime clusters coupled with wave energy converter, seawater desalination, and marine aquaculture modules to produce the water-energy-food nexus and boost, symbiotically, the viability of the wave energy sector and the blue economy in the arid coastal communities of Baja California, a Mexican Pacific region with a mid-wave energy resource. Based on technical and economic data available in the literature, the cost estimates and the contribution of each coupled module to the Marine Ecopark profitability were carried out and compared against other forms of energy generation using different current and projected 2030 market scenarios. All the cases analyzed show that the Pelamis device generates higher techno-economic performance than the WaveDragon. While individual wave energy devised siting is not profitable, economic scaling in increases maritime clusters' profitability. Optimally selected Pelamis facilities attain capacity factors over 40 % and a Levelized Cost of Energy of 480 $/MWh. The coupled seaweed aquaculture module generates the highest water-energy-food nexus profitability, whereas seawater desalination is not economically viable. All wave energy farms show a decrease in estimated electricity cost by 2030, reaching 198 USD/MWh for the most competitive Pelamis farms.

Frequency support by wave farms in low inertia power systems

New ancillary services and additional requirements for the grid integration of variable renewable energy (VRE) are being defined worldwide, in response to the technical challenges caused by increasing levels of VRE utilization in electric power grids. Currently, the use of wave energy is still limited to a few applications or demonstrations, where the level of penetration of wave power into the grid is not significant. To anticipate the future requirements for wave power integration, and the possibilities for provision of services, this paper considers the lessons being learned through the challenges caused by high penetration levels of other VRE sources into the grid, particularly wind power. On this basis, this paper presents an overview of grid support services that wave power plants can be expected to provide in power systems dominated by converter-interfaced generation, i.e. low inertia systems. Specifically, the focus is on services that support the active power balance in the power system. Then, the current capabilities and future perspectives for the provision of frequency support by wave farms are discussed.

Performance evaluation of 30kW class OWC wave power plant integrated with breakwater

In 2016, Korea started to develop a 30kW class wave power plant connected to a breakwater. After designing, manufacturing and performance testing of each energy conversion device, a demonstration plant was installed in Mukri Port, Jeju Island, Korea in 2021. After passing the completion inspection of the power generation facility, a full-scale grid-connected trial operation began in October 2021. The power plant consists of a sloped Oscillating Water Column, impulse air turbine, permanent magnet synchronous generator, AC-DC converter, energy storage system and integrated control system.
 This study introduces the performance evaluation results based on real sea operation data. The performance evaluation of the wave power plant under various wave height and period conditions was performed to evaluate the output power and efficiency of each bin. In addition, performance evaluations were conducted according to wave direction and tidal conditions to examine the effects. The correlation coefficient was derived by analyzing the correlation between wave height, period, wave directions, tide level and output power.

LUBRICATION OF OFFSHORE MECHANICAL COMPONENTS: TOWARDS SUSTAINABLE & RELIABLE POWER PRODUCTION

As different wave and tidal energy generators advance towards the commercial deployment phase, addressing potential issues related to the lubrication of the machine components integrating the Power Take Off system (PTO) due to the harsh operating conditions encountered in the marine environment becomes even more essential. Environmentally Acceptable Lubricants (EALs) based on water-soluble polymers are proposed as a way to reduce friction and wear of the mechanical components in wave and tidal energy generators. Although these fluids have the advantage of being biodegradable and non-toxic, they have not shown to be as effective as other synthetic fluids, or mineral oils in preventing corrosion, severe friction, and wear, thus increasing the risk of moving parts experiencing premature failure.
 This work explores the potential of different water-soluble polymers to be used in the formulation of EALs that can meet the strict environmental regulations while providing effective protection against wear, and corrosion in offshore operating conditions. To evaluate the potential of these lubricants as an alternative to replace conventional mineral oils, different polymers were analyzed from the point of view of their ability to form an effective full lubricant film that can keep separation between the contacting surfaces, mitigate wear, and prevent corrosion. The rheological properties of these polymers were also studied at different concentrations in order to optimize the performance in the application. The hydrodynamic film build-up properties of EALs formulated with water-soluble polymers with different molecular weight, concentration, and viscosity are reported. The corrosion resistance exhibited by steel components when exposed to the different formulations compared to seawater was an object of examination. The study also aimed to establish correlations between the lubricant film-build up properties, viscosity, and electrical impedance.
 The results showed that high molecular weight polymers can form a separating film at relative high pressure in the low-speed region even at low polymer concentrations. While with the increasing speed, the fluid viscosity becomes more important to sustain a full film between contacting surfaces. With the increasing concentration of polymer in the aqueous solution the open circuit potential (OCP) becomes more negative indicating the deterioration of the steel corrosion resistance.
 The results provide new insights into the design of EALs that can effectively protect the mechanical components of wave and tidal energy generators while minimizing environmental impact. The findings suggest that water-soluble polymers are a promising solution for offshore applications, as they can provide efficient full film lubrication, mitigate wear, and prevent corrosion. These polymers can help to improve the performance and lifespan of offshore power generators while minimizing the environmental impact.

Wave Farms Integration in a 100% renewable isolated small power system -frequency stability and grid compliance analysis.

In general terms, the variable penetration of RE in power systems has some inherent drawbacks, such as lack of manageability and resource variability [1]. Medium (in the range of minutes) and short term (in the range of seconds) variability has a negative impact on system reliability, causing a deterioration of system frequency quality in both interconnected and, moreover, isolated systems [1-2]. Specifically, the variability of the wave energy resource is medium- and short-term. Therefore, although wave energy could be very suitable to be integrated in islands due to its location, the variable nature of wave energy could negatively impact the stability of the power grid [3].
 The case study of the work focuses on the island of El Hierro (Canary Islands, Spain). It is an isolated electrical system with a very high penetration of renewable energy sources. The generation of the electrical system is composed of a wind farm, a pumped hydroelectric power plant and conventional generation by means of a diesel power plant.
 In a previous analysis [4], the integration of energy storage systems based on flywheels was analyzed. Based on this previous analysis, the manuscript studies the influence of the integration of the wave energy park in the electrical system of El Hierro.
 On the one hand, a wave farm will be proposed to evaluate the generated power and its associated oscillation [5]. The wave energy resource at different locations along the coast of El Hierro will be taken into account. On the other hand, an aggregated inertial dynamic mode of the electrical power system will be used to evaluate the impact of the generated power on the electrical frequency and the aging/degradation effects of the hydropumping elements. The Spanish Grid Code will be taken into account regarding frequency regulation mechanisms in isolated systems.
 The degradation of the hydraulic pumping systems due to additional frequency regulation stresses and electrical frequency deterioration will be calculated and evaluated in relation to the penetration of wave energy into the system, with and without the flywheel energy storage plant. This will allow quantification of certain technical limits to wave energy penetration in isolated systems and to draw conclusions with reference to the size of such a power system.
 [1] R. S. Kaneshiro et al. “Hawaii Island (Big Island) Wind Impacts” Proc. of Workshop on Active Power Control from Wind Power, Broomfield, CO, USA, 2013.
 [2] H. R. Iswadi et al. “Irish power system primary frequency response metrics during different system non synchronous penetration,” IEEE Eindhoven PowerTech 2015, doi: 10.1109/PTC.2015.7232425.
 [3] Isabel Villalba et al. “Wave farms grid code compliance in isolated small power systems,” IET Renewable Power Generation, 2019, doi: 10.1049/iet-rpg.2018.5351.
 [4] Hilel Garcia-Pereira et al. “Comparison and Influence of Flywheels Energy Storage System Control Schemes in the Frequency Regulation of Isolated Power Systems,” IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3163708.
 [5] M. Blanco et al. “Study of the impact of wave energy generation in the frequency of an island electric grid,” Proc. of the 12th European Wave and Tidal Energy Conference (EWTEC). 2017.

SEASNAKE: Impact - Marine operations modelling for evidence-based results detailing the impact of using a new fully dynamic cable design for ocean energy devices.

The SEASNAKE project - an OceanERA-NET project – is aiming for developing fully dynamic cables for ocean energy. Through new design and application of novel coatings the project is designing a dynamic cable that better suits the conditions and user requirements. In order to fully understand the benefits, the Wave Venture TEMPEST™ techno-economic analysis software will be used to simulate the performance of a proposed wave energy farm with a special focus on the contribution of the dynamic cable subsystem. The results obtained from the simulations will not only provide a deep understanding of the reliability and cost-risk areas regarding the use of dynamic cables, but will also allow a comparison with a baseline scenario consisting of a more traditional cabling system. The latter will be key to identify the advantages of the SEASNAKE project and its commercial viability. KPIs (Key Performance Indicators) will include farm availability and power production downtime as a direct result of cable related issues. The reliability, maintainability and survivability of the cabling subsystem will be tested and the KPIs will give clear indication on the performance benefits and ultimately the impact on the LCOE will be of greatest consideration.

Analysis of data from the full-scale prototype testing of the WASP – A novel wave measuring buoy.

To assess the viability of locations for wave energy farms, and design effective coastal protection measures, knowledge of local wave regimes is required. Current regime-measuring devices are expensive, and the aim of the WASP Project is to develop a low-cost, self-powering wave-measuring device. The Wave-Activated Sensor Power Buoy (WASP) comprises a floating body with a moonpool. The relative motion of the water level in the moonpool to the buoy will pressurise and depressurise the air above the water column, which will be used to drive a bidirectional turbine in the manner of an oscillating water column, which will operate in conjunction with a generator to recharge an on-board battery pack. It has previously been demonstrated through tank testing of scale-models that a sea-state may be estimated from measurements of the pressure within the air above the water column under controlled conditions [1]. Important statistical parameters relating to the sea state, such as the significant wave height, zero-cross period et cetera. may then be estimated from the spectral moments of the wave spectrum [2].
 This paper seeks to validate the use of measurements taken of the pressure in a volume of air contained in a chamber above an oscillating water column (OWC) (which is in communication with the ocean) to estimate the sea state which induces motion of the water column.
 The WASP was deployed from March to July 2019 at the Smart Bay test facility in Galway Bay, Ireland. The ¼ Scale test site was previously characterised by B Cahill UCC 2011 [3] using data captured by the Marine Institute Datawell WaveRider buoy from 2006 to 2011. Pressure signal data recorded by the WASP at a frequency of 8Hz was processed in to 30-minutes blocks to reflect the presented data from the WaveRider buoy, from which the WASP was moored a distance of 400m from during deployment. Power Density Spectra for 6,500 thirty-minute sea states were generated and an inverse transfer function between the WASP and the WaveRider produced. This allowed for the calculation of the associated significant wave height, Hs, and zero-crossing period, Tz, from the WASP pressure data for each thirty-minute block of data. At this time, outliers and bimodal spectra were removed to simplify the analysis, and the data was binned based on Hs and Tz ranges. For each bin, average inverse transfer functions were generated. A Bretschneider Spectrum [4] was produced using the Hs and Tz values from the WaveRider data for the respective binned WASP data. An average transfer function was then generated between the Bretschneider models and the corresponding sea states for the WASP. Using this final transfer function, a scatter plot of Hs and Tz for the test site was generated and compared against the previously produced scatter plot from the initial resource characterisation of the Galway Bay Test Site in 2011 in order to determine if the WASP is capable of measuring sea-states through measurement of time series pressure signals in the volume of air above the water column in the OWC.

Optimisation of Air turbines for OWC Wave Energy Converters: Sensitivity of Realistic Wave Climates

Wave Energy, Oscillating Water Column, Air turbines, Optimisation, Genetic Algorithms, Wave climates.
 Among all the Wave Energy Converter (WEC) technologies suggested in the last decades, the Oscillating Water Column (OWC) technology seems to be the most robust and reliable technology. Different are currently in operation, such as the Mutriku Wave Power Plant installed in a harbour, or are being developed, such as the MARMOK floating OWC device developed by IDOM and tested for over a year in the Biscay Marine Energy Platform (BIMEP). One of the key elements of the OWC technologies is the power take-off (PTO) system that converts the pneumatic energy trapped in the chamber into electrical energy. Such PTO system consists of an air turbine coupled to an electric generator, and has been the object of several studies, including numerical and experimental works that cover a wide range of different air turbine configurations, and some of the proposed research lines even reaching to combine both approaches. The most common turbine, mainly due to its relative simplicity both on the conceptual andmechanical aspects, is the Wells monoplane turbine, including variations such as the biplane and the counter-rotating configurations. However, other configurations such as the impulse turbine or the more recent bi-radial turbine have also been analysed.
 The preliminary design of these turbines usually relies on analytical models based on the blade element method, using dimensionless parameters for representing the behavioural charts of the different configurations. In fact, in order to better represent the behaviour of air turbines in realistic conditions with polychromatic waves, it is usual to consider the stochastic version of these dimensionless parameters so that they provide an overall indicator of their sea-state-related behaviour. However, the air turbines, regardless of their configuration, include a large number of different geometrical parameters, which complicates the optimisation procedure and leads to a decision-making process that relies on an expertise-based intuition. In this sense, suggests an optimisation method based on a Genetic Algorithm (GA) that enables the articulation of all the relevant parameters. This GA-based optimisation method articulates the information about the hydrodynamic behaviour of the WEC and the pneumatic conversion within the chamber. Hence, the optimisation is sensitive to the characteristics of the wave climate and, thus, the behaviour of the WEC in that specific wave climate.
 However, in order to make wave energy economically viable, mass production of the WECs, including their PTO systems, is a crucial point. As a consequence, standard WEC floaters and PTO system elements may need to be used in the different locations under different resource conditions. In order to evaluate the sensitivity of the optimal air turbine designs to the characteristics of specific wave climates, the present study will define optimal air turbines for different locations worldwide, comparing the characteristics of the different designs.

Identification of optimal sites for the deployment of wave energy converters: the importance of a technology-centred approach

Driven by climate issues and geopolitical uncertainties, Europe faces the need to transform its energy supply dramatically and quickly. Various renewable technologies are proposed as a medium- to long-term solution for an environmentally and economically sustainable energy mix: among the available solutions, wave energy converters (WECs) are attracting growing interest due to the large untapped wave energy potential in European seas. In this context, the choice of optimal locations for the use of wave energy is fundamental to limit the technological gap with other fully developed conversion technologies, and to ensure competitive energy costs. In this paper, we compare different possible strategies to identify suitable sites for the installation of WECs, namely the one based on pure analysis of the wave energy resource, and that considering the productivity of the device in different sea states, i.e., its power matrix. Using the performance matrices of notional WECs, particularly an Oscillating Surge Wave Energy Converter (OSWEC) and a Heaving Point Absorber (HPA), we estimate optimal locations on the Italian coasts and highlight the advantages and disadvantages of the two approaches. The analysis shows the importance of a technology-based approach for the spatial planning of future wave power plants and highlights significant differences compared to the approach that can be used for the preliminary identification of sites for wind farms and, especially, for photovoltaic plants. We use the obtained results to introduce the MORE-EST platform, a novel web-based tool for straightforward estimation of wave resources and WECs productivity in European seas. The proposed platform is able to integrate information on wave resource assessment, bathymetry, marine space use and technological features, and represents a tool aimed at researchers, WECs developers, and policy makers.