Ocean Energy and Coastal Protection

Among the challenges that managers and policy-makers should confront in the coming years, two of the most relevant are the development and use of carbon-free energy sources and the adaptation to the consequences of climate change, including sea-level rise. Wave energy is one of the most promising renewable energy sources due its great potential and its low environmental impacts, including visual pollution. This chapter presents a summary of both the overview in ocean energy and the current state of the art in wave energy. Then, the main objective of the book and the selected study site are specified. Finally, it is indicated the structure of the book, detailing the main purposes of each chapter.

In this chapter, a new methodology to manage coastal protection by means of wave farms is proposed. Artificial intelligence tools, more specifically artificial neural networks (ANNs), were used to assess dry beach surface differences between the no wave farm situation and different wave farm project scenarios. A number of alongshore locations and layouts—represented as the number of rows and the spacing between devices—formed the wave farm scenarios and the influence of the wave climate—significant wave height and mean wave direction—was also taken into account. The selected study site was Playa Granada (southern Iberian Peninsula), a beach with important erosion problems. The datasets used for training and testing the ANN were obtained by means of a suite of numerical models including a third generation wave propagation model, a sediment transport formulation and a shoreline response equation. In order to obtain the ANN which provides the best fit to the data, a comparative study involving more than forty different architectures formed by one and two hidden layers and trained by means of two training algorithm was carried out. The [5-10-1] architecture obtained the best results with a correlation coefficient and RMSE of 0.9489 and 4.22 m\(^2\), respectively. Once the best architecture was found, the ANN was applied to the study site in order to obtain the optimum location and layout for a wave farm project and the results indicate that dry beach surface could increase up to 5400.18 m\(^2\) per year. These results show that ANNs can be useful for managers to optimize the design of wave farms for coastal protection.

This chapter analyses the influence of the wave energy converter geometry, in particular the wedge angle of WaveCat devices, on the performance of wave farms as coastal protection elements against erosion. Laboratory experiments were conducted for two angles between hulls under low-, mid- and high-energy conditions to obtain the reflection and diffraction coefficients. These values were used as input for the joint application of a wave propagation model, a longshore sediment transport formulation and the one-line model to a study site in southern Spain. The shoreline evolution and dry beach area availability for wave farms with by both devices were assessed. The results indicate that WaveCat devices with a wedge angle of 60\(^\circ \) provide more protection than those with 30\(^\circ \) for long wave periods and less protection for short periods. Thus, to optimize the efficiency of wave farms for coastal defence, the geometry of the wave energy converters should be adapted dynamically to the incoming wave condition.

In this chapter, the effects of wave energy converter geometry on coastal flooding are explored. In particular, it is assessed the efficiency of two angles between hulls of the WaveCat devices (30\(^\circ \) and 60\(^\circ \)) for the mitigation of coastal inundation. The case study consists of a wave farm composed by 11 devices deployed off a deltaic beach is southern Spain (Playa Granada). First, laboratory tests were performed to determine the reflection and transmission coefficients under low-, mid- and high-energy conditions. Then, these coefficients were used to jointly apply Delft3D-Wave and XBeach-G in Playa Granada considering wave farms with both wedge angles. The results highlight that devices with wedge angles of \(60^\circ \) are more efficient than those with \(30^\circ \) to reduce nearshore wave heights, run-up values and flooded dry beach areas for long wave periods and high-energy conditions. Thus, since coastal flooding commonly occurs under storm conditions, the devices with wedge angles of \(60^\circ \) are more efficient as coastal defence elements against flooding.

In this chapter, the efficiency of wave farms in coastal protection under sea-level rise is investigated. A wave farm formed by 11 wave energy converters was modelled off Playa Granada, a gravel-dominated coast in Southern Spain, under three sea-level rise scenarios: the current water level and the water level in 2100 according to a low- and high-emission scenario. In order to explore the effects produced by the wave farm, the natural scenario without wave farm was also studied. Waves were propagated through the wave farm by means of Delft3D-Wave and breaking parameters were obtained in order to apply a longshore sediment transport (LST) formulation. The results obtained with the LST formulation were used in a one-line model to compute the changes in the position of the shoreline at the study site. The results highlight that wave farms are able to decrease beach erosion (shoreline retreat) even under sea-level rise scenarios. That makes wave farms attractive management strategies, as they contribute to the decarbonisation of the energy mix and more efficient in terms of coastal protection under sea-level rise than traditional hard-engineering structures.

This chapter analyzes the effects of wave energy converter farms on storm-induced coastal flooding under three sea-level rise scenarios: present situation, optimistic projection and pessimistic projection. For that, the Delft3D-Wave and XBeach-G models were jointly apply to a gravel-dominated coast in southern Spain. The results show that the wave farm induces redutions, for the three scenarios, in breaking wave heights (about 10% and 25% under westerly and easterly storms, respectively) and total run-up values (8% and 10%, respectively). This leads to reduction in flooded cross-shore distances and dry beach areas. The decreases in flooded dry beach areas for the three sea-level rise scenarios are between 1400 and 3900 m\(^2\) under south-westerly storms, and between 2100 and 3400 m\(^2\) under south-easterly storms. Therefore, wave farms are efficient management strategies to mitigate coastal flooding even under sea-level rise conditions.