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 paper presents a power smoothing strategy for wave energy converters (WECs) by means of energy storage systems (ESS) with integrated forecasting filtering algorithms applied to their control. The oscillatory nature of wave energy leads to high variability in power output, posing significant challenges for grid integration. A case study in Tenerife, Spain, was modeled in MATLAB-Simulink (release r2020b) to evaluate the impact of prediction-enhanced smoothing filters on ESS sizing. Various forecasting algorithms were assessed, including Bayesian Neural Networks, ARMA models, and persistence models. The simulation results demonstrate that the use of forecasting algorithms substantially reduces energy storage requirements while maintaining grid stability. Specifically, the application of Bayesian Neural Networks reduced the required ESS energy by up to 36.52% compared to traditional filters. In a perfect prediction scenario, reductions of up to 53.91% were achieved. These results highlight the importance of combining appropriate filtering strategies with advanced forecasting techniques to improve the technical and economic viability of wave energy projects. The paper concludes with a parametric analysis of moving average filter windows and prediction horizons, identifying the optimal combinations for different sea conditions. In summary, this study provides practical information into reducing the storage capacity required for power smoothing in wave energy systems, thereby contributing to the mitigation of grid integration challenges that may arise with the large-scale deployment of marine renewable energy