Manuel Corrales-Gonzalez

Marine renewable energy could support Costa Rica’s decarbonization pathway, but its offshore resource base and enabling conditions remain poorly characterized in the body of knowledge. This study provides the first integrated assessment of marine energy resources, grid integration opportunities, and governance challenges in Costa Rica. A meta-analysis of 76 technical, legal, and policy sources is combined with qualitative doctrinal analysis, GIS-based multi-criteria evaluation for Ocean Thermal Energy Conversion (OTEC), and satellite and reanalysis data for winds, waves, currents, and sea surface temperature to estimate power densities and extractable energy. Results show a contrast between the Pacific and Caribbean coasts. For instance, on the Northern Pacific coast, there are strong Papagayo winds, and persistent swells yield high offshore wind and wave energy potentials, with technical offshore wind resources of around 14.4 GW and Pacific wave power frequently exceeding 20–25 kW/m with relatively low seasonal variability. Furthermore, twelve OTEC-suitable zones are identified with two priority areas in the southern Pacific that combine steep bathymetry and strong thermal gradients with limited environmental conflicts, but they overlap with sensitive conservation and Indigenous territories. Current energy potential is more localized and modest in the Caribbean coast. The analysis highlights major infrastructural, legal, and social barriers but concludes that marine energy can play a pivotal role in diversifying Costa Rica’s renewable-dominated electricity market.

The energy transition in Ibero-American countries demands significant diversification, yet the vast potential of offshore renewable energies (ORE) remains largely untapped. Slow adoption is often attributed to the hostile marine environment, high investment costs, and a lack of institutional, regulatory, and industrial readiness. A critical barrier for policymakers is the absence of methodologically robust tools to assess national preparedness. Existing indices typically rely on simplistic weighting schemes or are susceptible to known flaws, such as the rank reversal phenomenon, which undermines their credibility for strategic decision-making. This study addresses this gap by developing a multi-criteria decision-making (MCDM) framework based on a problem-specific synthesis of established optimization principles to construct a comprehensive Offshore Readiness Index (ORI) for 13 Ibero-American countries. The framework moves beyond traditional methods by employing an advanced weight-elicitation model rooted in the Robust Ordinal Regression (ROR) paradigm to analyze 42 sub-criteria across five domains: Regulation, Planning, Resource, Industry, and Grid. Its methodological core is a non-linear objective function that synergistically combines a Shannon entropy term to promote a maximally unbiased weight distribution and to prevent criterion exclusion, with an epistemic regularization penalty that anchors the solution to expert-derived priorities within each domain. The model is guided by high-level hierarchical constraints that reflect overarching policy assumptions, such as the primacy of Regulation and Planning, thereby ensuring strategic alignment. The resulting ORI ranks Spain first, followed by Mexico and Costa Rica. Spain’s leadership is underpinned by its exceptional performance in key domains, supported by specific enablers, such as a dedicated renewable energy roadmap. The optimized block weights validate the model’s structure, with Regulation (0.272) and Electric Grid (0.272) receiving the highest importance. In contrast, lower-ranked countries exhibit systemic deficiencies across multiple domains. This research offers a dual contribution: methodological innovation in readiness assessment and an actionable tool for policy instruments. The primary policy conclusion is clear: robust regulatory frameworks and strategic planning are the pivotal enabling conditions for ORE development, while industrial capacity and infrastructure are consequent steps that must follow, not precede, a solid policy foundation.

This study assesses the feasibility and profitability of marine hybrid clusters, combining wave energy converters (WECs) and offshore wind turbines (OWTs) to power households and marine aquaculture. Researchers analyzed two coastal sites: La Serena, Chile, with high and consistent wave energy resources, and Ensenada, Mexico, with moderate and more variable wave power. Two WEC technologies, Wave Dragon (WD) and Pelamis (PEL), were evaluated alongside lithium-ion battery storage and green hydrogen production for surplus energy storage. Results show that La Serena’s high wave power (26.05 kW/m) requires less hybridization than Ensenada’s (13.88 kW/m). The WD device in La Serena achieved the highest energy production, while PEL arrays in Ensenada were more effective. The PEL-OWT cluster proved the most cost-effective in Ensenada, whereas the WD-OWT performed better in La Serena. Supplying electricity for seaweed aquaculture, particularly in La Serena, proves more profitable than for households. Ensenada’s clusters generate more surplus electricity, suitable for the electricity market or hydrogen conversion. This study emphasizes the importance of tailoring emerging WEC systems to local conditions, optimizing hybridization strategies, and integrating consolidated industries, such as aquaculture, to enhance both economic and environmental benefits.

The development of renewable marine energy is essential for combating the current global climate situation. Harnessing the immense potential of oceans offers a sustainable energy source that complements wind and solar, contributing significantly to decarbonizing energy systems and achieving net-zero emissions targets worldwide. Europe has emerged as a leader in advancing marine renewable energy, driven by its commitment to climate action and clean energy innovation. European initiatives prioritize investment and collaboration to scale up marine energy technologies, demonstrating the region's determination to accelerate the clean energy transition. International energy committees highlight marine renewables' transformative potential to diversify energy portfolios and enhance global resilience. Advancements in this sector align with global sustainability goals, ensuring a cleaner, more secure, and carbon-neutral energy future. In the present research, wave downscaling along European coasts was conducted using the SWAN model as part of the EU multi-partner project Blue-X. The wave modeling results were utilized to estimate wave power potential, theoretical wave energy extraction from advanced Wave Energy Converters (WECs), and subsequent economic assessments. A prior review of WECs in diverse marine environments, such as the Pacific Ocean and Ligurian Sea, served as the foundation for extending the analysis to the Mediterranean Sea, Iberian coasts, Cantabrian Sea, and North Sea in this study. High spatial and hourly temporal resolution data were considered to ensure precise WEC evaluations, although WEC-specific downscaling was not included. Based on the total power produced, capacity factors and indexes as Selection index for Wave Energy Deployments (SIWED) or Wave Energy Deployment Index (WEDI), an economic evaluation tailored to the unique characteristics of different coastal regions was performed, considering renewable energy investment discount rates, in the levelized costs, and current energy market dynamics. Additional considerations included mapping Marine Protected Areas and marine vegetation, which impose constraints on WEC placement. No realistic marine energy evaluation dismisses the available zones to deploy the marine energy placements, including the environmental and national regulations. The study also emphasized the importance of long-term cost evaluations in wave energy investments, highlighting potential damage from storm events, quantified through indicators like SIWED or the ratio of maximum WEC power output to wave energy potential. Key findings underscore the influence of WEC type, wave system variability across different seas, and depth of deployment as critical factors shaping the results.

We present a methodology for the detection of directional wave systems at given locations. The methodology relies on image processing algorithms used to extract relevant features from time-series of integral parameters of spectral partitions. A case study is developed in the Mediterranean Sea (MS); the wave systems extracted are next used to assess the frequency of occurrence of crossing seas in the area, which may be dangerous for the safety of navigation.

The wave energy sector aims to reduce the costs and maximize the Wave Energy Converter (WEC) power absorption to reach parity with other renewable energies. Likewise, the continuous change in the local metocean conditions due to several mid and long-term atmospheric and ocean events, and the reduction in air pollution from the extraction of non-renewable energies have prompted a meticulous examination regarding the WEC performance (Lavidas, 2019). Specifically, for the Belgian Continental Shelf (BCS) a performance assessment of several WECs at different locations has been developed. The eleven locations for evaluating the WECs are shown. The wave information at those locations has been estimated through numerical wave modeling by using the Simulation Wave Nearshore (SWAN) model (Booij, et. al., 1996). The numerical model setup consists of three nested meshes with resolutions of 5 x 5 km, 1 x 1 km, and 200 x 200 m. The bathymetry is based on ETOPO-1 (Eakins, et. al., 2010) and Agency for Maritime and Coastal Services (MDK). The model setup was forced with the ERA5-reanalysis (Hersbach, et. al. ,2023) wind database for 11 years (2010 – 2021). Then, the spectral parameters were validated for 2015 with the BVH buoy data (Meetnet Vlaamse Banken, 2023).

Nowadays, numerous governments have instituted diverse regulatory frameworks aimed at fostering the assimilation of sustainable energy sources characterized by reduced environmental footprints. Solar, wind, geothermal, and ocean energies were subject to extensive scrutiny, owing to their ecological merits. However, these sources exhibit pronounced temporal fluctuations. Notably, ocean dynamics offer vast energy reservoirs, with oceanic waves containing significant amounts of energy. In the Central American Pacific context, the exploration of wave energy resources is currently underway. Accurate numerical wave models are required for applied studies such as those focused on the estimation of exploitable wave power; and even more so in Central American region of the Pacific Ocean where existing numerical models simulations have so far relied on coarse resolution and limited validation field data. This work presents a high-resolution unstructured wave hindcast over the Central American Pacific region, implemented using the third-generation spectral wave model WAVEWATCH III over the period between 1979 and 2021. The results of the significant wave height have been bias-corrected on the basis of satellite information spanning 2005 to 2015, and further validation was performed using wave buoy and acoustic Doppler current profiler (ADCP) records located in the nearshore region of the Central America Pacific coast. After correction and validation of the wave hindcast, we employed the dataset for the evaluation and assessment of wave energy and its possible exploitation using different wave energy converters (WECs). This evaluation addressed the need to diverse the energy portfolio within the exclusive economic zones of Guatemala, El Salvador, Honduras, Nicaragua, Costa Rica, Panama, Colombia, and Ecuador in a sustainable manner. Moreover, a comprehensive analysis was carried out on the advantages of harnessing wave energy, juxtaposed with the imperative of regulatory frameworks and the current dearth of economic and environmental guidelines requisite for development within the region.

Understanding marine dynamics, particularly in coastal areas with high wave activity, cannot be achieved without physical modeling. However, when it comes to downscaled physical wave modeling, accurately recording wave data becomes a challenge, especially near coastlines where disturbances are common. Traditional water surface measurement tools, such as invasive wave probes, have proven to be both inaccurate and impractical. To overcome these limitations, this study presents a new data acquisition system (DAQ) utilizing resistive sensors and wireless transmission protocols to enhance the accuracy of wave measurements in laboratory-scale experiments. The data acquisition technology effectively measures water surface electric potential by utilizing specialized resistive probes. Additionally, the DAQ is equipped with an automatic calibration system using a vertical potentiometer, providing a cutting-edge solution to calibrate the invasive wave probes usually employed in several types of hydraulic physical modeling. The recorded data is efficiently handled by Arduino® board controllers, allowing for convenient wireless transmission for laboratory usage. This exceptional system surpasses traditional methods, offering a combination of versatility, cost-efficiency, and enhanced accuracy in capturing wave characteristics. In addition to describing the development of controllers and data processing algorithms, this study highlights their seamless integration into a unified solution for superior wave data collection.