Assessing long-term metocean data variability for optimal energy system planning via static robust optimization approach

Introduction: Palm syrup is a typical product from the Canary Islands, traditionally produced from the sap of the tropical palm tree Phoenix canariensis. Its high caloric content has led to its increasing use as a health food supplement for athletes, children and elderly. Furthermore, demand for this natural syrup is continuously increasing due also to its medicinal uses in homeopathic medicine. Objective: Palm Tree syrup samples prepared with palm sap from primary producers in La Gomera island (Canary Islands, Spain) were analyzed for their nutritional composition (moisture, ash, sugars, fat, vitamins and minerals). Methods: 35 syrup samples from five different producing regions in La Gomera island were analyzed. High-performance liquid chromatography (HPLC) was used to determine sugars and vitamins and Flame Atomic Absorption Spectrophotometry (FAAS) was used to analyze the minerals. Results: Major carbohydrates were sucrose (37.8%), glucose (9.50%) and fructose (4.80%), respectively. The presence of arabinose could not be confirmed. Niacin was the water-soluble vitamin with the highest concentration with an average content of 0.003%. Fat content was found to be under 0.20%. Potassium was the mineral with highest contents (0.45%). Conclusions: Results suggest that palm tree syrup can play an important role as a sugar and mineral source in human nutrition, suggesting that future applications for this product could be developed.

Ocean energy technologies are in their developmental stages, like other renewable energy sources. To be useable in the energy market, most components of wave energy devices require further improvement. Additionally, wave resource characteristics must be evaluated and estimated correctly to assess the wave energy potential in various coastal areas. Multiple algorithms integrated with numerical models have recently been developed and utilized to estimate, predict, and forecast wave characteristics and wave energy resources. Each algorithm is vital in designing wave energy converters (WECs) to harvest more energy. Although several algorithms based on optimization approaches have been developed for efficiently designing WECs, they are unreliable and suffer from high computational costs. To this end, novel algorithms incorporating machine learning and deep learning have been presented to forecast wave energy resources and optimize WEC design. This review aims to classify and discuss the key characteristics of machine learning and deep learning algorithms that apply to wave energy forecast and optimal configuration of WECs. Consequently, in terms of convergence rate, combining optimization methods, machine learning, and deep learning algorithms can improve the WECs configuration and wave characteristic forecasting and optimization. In addition, the high capability of learning algorithms for forecasting wave resource and energy characteristics was emphasized. Moreover, a review of power take-off (PTO) coefficients and the control of WECs demonstrated the indispensable ability of learning algorithms to optimize PTO parameters and the design of WECs.

Advanced wave energy conversion technologies for sustainable and smart sea: A comprehensive review

The effective integration of offshore wind and wave energy is highly challenging due to their distinct intermittency characteristics. The power take-off (PTO) level power smoothing in the wave energy converter (WEC) is needed to reduce its power variations and optimize the generator and associated converter sizing. However, there is a lack of understanding of the impact of mechanical storage systems at the PTO on the WEC operation; hence, a guide on the selection of the electrical generator rating under a wide range of wave climates is missing. This article addresses these research gaps by using a representative point absorber WEC with a hydraulic PTO system. A general method of selecting the optimum generation system rating is proposed from the technical and economic viewpoints. In addition, the impacts of two mechanical storage systems (added inertia and gas accumulator) on WEC system efficiency and availability are investigated under four distinct levels of practical wave climates (399 sea states). It is shown that both mechanical storage methods can effectively reduce the generator ratings but their impacts vary under different wave climates. It is also concluded that the storage systems have limited impact on the optimal generator ratings, while wave specifications at various sea locations present a greater impact.

Ocean wave energy is an essential source of renewable power for coastal communities. Choosing the optimal site for the wave energy converter (WEC) deployment depends on a number of criteria. The characteristics of the WEC must be taken into account in the prediction power supply, whereas the local sea state is connected to elements like wave condition (as a representation of construction budget) and energy output as well as the influence of the exploitable storage of wave energy and its trend. As a result, this research provides a multi-criteria decision-making (MCDM) strategy for considering several factors simultaneously to choose the best possible site. The suggested MCDM technique incorporates two primary factors, i.e., exploitable storage of wave energy and energy production, into a single metric that takes into account both WEC efficiency of a particular type, WEPTOS, and sea state to aid decision-makers in the development of a pilot project. The method was then used to analyse the waves at two locations that had been identified as promising sites for harvesting wave energy along the coast of Oman. To further assess a site's potential upcoming pilot project and select the most efficient WEC, we compared the MCDM results at the stations with certain WEC types. In conclusion, optimal sites for placement of the WEPTOS WEC along the coast of Oman were identified considering the highest annual energy production and exploitable energy storage. Through solving the MCDM technique, 17 sites were pinpointed, and only 6 points were picked up.

The emerging global wave energy industry has the potential to contribute to the world’s energy needs, but careful consideration of potential impacts to coastal processes in the form of an impact assessment is required for each new wave energy site. Methods for conducting a coastal processes impact assessment for wave energy arrays vary considerably in the scientific literature, particularly with respect to characterising the energy absorption of a wave energy converter (WEC) array in a wave model. In this paper, modelling methods used in the scientific literature to study wave farm impacts on coastal processes are reviewed, with the aim of determining modelling guidance for impact assessments. Effects on wave climate, beach morphology, and the surfing resource for coastal water users are considered. A novel parameterisation for the WEC array transmission coefficient is presented that, for the first time, uses the permitted power rating of the wave farm, which is usually well defined at the impact assessment stage, to estimate the maximum likely absorption of a permitted WEC array. A coastal processes impact assessment case study from a wave farm in south-west Ireland is used to illustrate the application of the reviewed methods, and demonstrates that using the new ‘rated power transmission coefficient’ rather than a WEC-derived transmission coefficient or complete energy absorption scenario can make the difference between significant and non-significant levels of coastal impacts being predicted.

Uncertainty in wave energy resource assessment. Part 2: Variability and predictability

The ocean energy resources hold the promise of a sustainable solution within global efforts to diversify energy sources and mitigate climate change. Wave energy conversion (WEC) systems, as emerging technologies, offer adaptability and the potential to harness predictable wave energy. However, integration of WEC systems into a power grid brings challenges for system operators due to their nature of operation. Addressing these demands is a multilayered process that involves highly efficient power electronic devices, control systems, and efficient energy storage solutions. This paper specifically focuses on the methodologies of the grid integration of a specific wave energy conversion system—a point absorber developed by the company Sigma Energy. Proposed methodologies are experimentally tested using power hardware-in-loop (PHIL) emulation of a fully monitored and controlled small-scale microgrid equipped with a battery energy storage system (BESS), different emulators of loads, and distributed generators (DG).

The power output from a wave energy converter is typically predicted using experimental and/or numerical modelling techniques. In order to yield meaningful results the relevant characteristics of the device, together with those of the wave climate must be modelled with sufficient accuracy. The wave climate is commonly described using a scatter table of sea states defined according to parameters related to wave height and period. These sea states are traditionally modelled with the spectral distribution of energy defined according to some empirical formulation. Since the response of most wave energy converters vary at different frequencies of excitation, their performance in a particular sea state may be expected to depend on the choice of spectral shape employed rather than simply the spectral parameters. Estimates of energy production may therefore be affected if the spectral distribution of wave energy at the deployment site is not well modelled. Furthermore, validation of the model may be affected by differences between the observed full scale spectral energy distribution and the spectrum used to model it. This paper investigates the sensitivity of the performance of a bottom hinged flap type wave energy converter to the spectral energy distribution of the incident waves. This is investigated experimentally using a 1:20 scale model of Aquamarine Power’s Oyster wave energy converter, a bottom hinged flap type device situated at the European Marine Energy Centre (EMEC) in approximately 13m water depth. The performance of the model is tested in sea states defined according to the same wave height and period parameters but adhering to different spectral energy distributions. The results of these tests show that power capture is reduced with increasing spectral bandwidth. This result is explored with consideration of the spectral response of the device in irregular wave conditions. The implications of this result are discussed in the context of validation of the model against particular prototype data sets and estimation of annual energy production.

The life cycle assessment (LCA) is an inevitable part of ocean wave energy systems. This paper presents an LCA of the Sindhuja-I ocean wave energy converter (WEC). The WEC is a point absorber type WEC tested at the Tuticorin port in India. This study utilized the ISO 14044 standard and SimaPro LCA software to assess the environmental impact of the WEC in comparison to a coal power plant, offshore wind, and tidal energy devices. The functional unit considered is the generation of 1 kWh of electricity. The WEC has a global warming potential of 174 gCO 2 -eq/kWh, showcasing an impressive 81.9% reduction in emissions compared to coal power plants. Stainless steel contributes significantly to emissions, accounting for 93.7%, 94.6%, 99.4%, and 94.1% of total CO 2 , NO x , SO 2 , and PM 2.5 emissions, respectively. The paper emphasizes the potential environmental benefits of recycling WEC materials at the end of their lifespan. Furthermore, the study suggests that the longevity of a point absorber device can positively impact its overall environmental footprint, given its minimal moving parts and low maintenance requirements. The WEC has a promising environmental footprint of ocean wave energy conversion, particularly compared to traditional fossil fuel-based power generation.

Knowledge of the effects of wave energy converters (WECs) on the near and far wave fields is critical to the efficient and low-risk design of waveforms. Several computational wave models enable the evaluation of WEC array effects, but model validation has been limited. In this chapter, we validate two popular models with very different formulations: the phase-resolving model WAMIT and the phase-averaged Simulating WAves Nearshore (SWAN) model. The models are validated against wave data from an extensive set of WEC array laboratory experiments conducted by Oregon State University and Columbia Power Technologies, Inc (CPT). The experimental WECs were 1:33 scale versions of a commercial device (CPT “Manta”), and several different WEC array configurations were subjected to a range of regular waves and random sea states. The wave field in the lee of the WEC arrays was mapped, and the wave shadow was quantified for all sea states. In addition, the WEC power capture performance was measured independently via a motion-tracking system and compared to the observed wave energy deficit (i.e., the wave shadow). Overall, WAMIT displays skill in predicting the wave field both in offshore and in the lee of the WEC arrays. WAMIT simulations demonstrate partial standing wave patterns that are consistent with the observations. These patterns are related to wave scattering processes, and their presence increases the magnitude of the wave shadow in the lee of WECs . The pattern is less pronounced at longer wave periods where WECs behave more like wave followers. In these situations, the wave shadow is primarily controlled by the WEC energy capture and less so by scattering. The SWAN model accounts for the frequency-dependent energy capture of the devices and performs well for cases when the wave shadow is primarily controlled by the WEC energy capture. For regular wave cases, inclusion of the wave diffraction process is necessary, but SWAN simulations for wave fields with frequency and directional spreading capture the general character of the wave shadow even without diffraction. Finally, we suggest that WECs designed to operate such that the expected significant wave energy lies at periods near, or larger than, the period of peak energy extraction will minimize the wave shadow effect for a given gross extraction of wave energy, which leads to more efficient arrays with respect to environmental impact.

This paper investigates the energy extraction of three types of wave energy converters (WEC) arranged in arrays and subjected to multi-directional seas of different wave spreading. The changes in the wave climate observed in the neighborhood of the arrays by their energy extraction were also reported. The hydrodynamic software, WAMIT, has been used to generate the performance of the WEC arrays in regular waves, whereas, a post-processing programming code developed in-house has been used to generate the device arrays’ performance in multi-directional seas characterized by single-peaked (JONSWAP and Bretschneider) and double-peaked (Ochi–Hubble) wave energy spectra. The results showed interesting findings on the WEC array performance and change in the wave environment around the devices, both strongly depend on the wave energy generation mechanism of the WECs and the wave spreading. The results obtained from the multi-directional seas with double-peaked wave spectrum showed significant differences in the energy production performance by the WEC arrays to those with single-peaked wave spectra. The uni-directional seas resulted in a larger q-factor. The q-factor differed with the peak wave periods, and at small wave periods, the q-factor for the terminator and attenuator WEC arrays are found to be higher than their counterparts for the point absorber WECs. With reference to the wave height modification in the neighborhood of the WEC arrays, the attenuator generated a greater wave disturbance followed by the terminator and point absorbers.

Wave energy fluctuating a great deal endangers the security of power grid especially micro grid in island. A DC nano grid supported by batteries is proposed to smooth the output power of wave energy converters (WECs). Thus, renewable energy converters connected to DC grid is a new subject. The characteristics of WECs are very important to the connection technology of HPTO type WECs and DC nano grid. Hydraulic power take-off system (HPTO) is the core unit of the largest category of WECs, with the functions of supplying suitable damping for a WEC to absorb wave energy, and converting captured wave energy to electricity. The HPTO is divided into a hydraulic energy storage system (HESS) and a hydraulic power generation system (HPGS). A primary numerical model for the HPGS is established in this paper. Three important basic characteristics of the HPGS are deduced, which reveal how the generator load determines the HPGS rotation rate. Therefore, the connector of HPTO type WEC and DC nano grid would be an uncontrollable rectifier with high reliability, also would be a controllable power converter with high efficiency, such as interleaved boost converter-IBC. The research shows that it is very flexible to connect to DC nano grid for WECs, but bypass resistance loads are indispensable for the security of WECs.

Wave energy conversion and hydrodynamics modelling technologies: A review

Wave energy, as a significant renewable and clean energy source with vast global reserves, exhibits no greenhouse gas or other pollution during real-sea operational conditions. However, throughout the entire lifecycle, wave energy convertors can produce additional CO2 emissions due to the use of raw materials and emissions during transportation. Based on laboratory test data from a wave energy convertor model, this study ensures consistency between the model and the actual sea-deployed wave energy convertors in terms of performance, materials, and geometric shapes using similarity criteria. Carbon emission factors from China, the European Union, Brazil, and Japan are selected to predict the carbon emissions of wave energy convertors in real-sea conditions. The research indicates: (1) The predicted carbon emission coefficient for unit electricity generation (EFco2) of wave energy is 0.008–0.057 kg CO2/kWh; when the traditional steel production mode is adopted, the EFco2 in this paper is 0.014–0.059 kg CO2/kWh, similar to existing research conclusions for the emission factor of CO2 for wave energy convertor (0.012–0.050 kg CO2/kWh). The predicted data on carbon emissions in the lifecycle of wave energy convertors aligns closely with actual operational data. (2) The main source of carbon emissions in the life cycle of a wave energy converter, excluding the recycling of manufacturing metal materials, is the manufacturing stage, which accounts for 90% of the total carbon emissions. When the recycling of manufacturing metal materials is considered, the carbon emissions in the manufacturing stage are reduced, and the carbon emissions in the transport stage are increased, from about 7% to about 20%. (3) Under the most ideal conditions, the carbon payback period for a wave energy convertor ranges from 0.28 to 2.06 years, and the carbon reduction during the design lifespan (20 years) varies from 238.33 t CO2 (minimum) to 261.80 t CO2 (maximum).