Power Smoothing in a Wave Energy Conversion Using Energy Storage Systems: Benefits of Forecasting-Enhanced Filtering for Reduction in Energy Storage Requirements

Abstract

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