Wave Sequential Data Assimilation in Support of Wave Energy Converter Power Prediction

TL;DR

This paper develops a real-time wave data assimilation framework using low-cost buoys and ensemble Kalman filter to improve wave predictions for wave energy converter power management, especially in remote microgrid applications.

Contribution

It introduces a hybrid physics-based and data-driven wave modeling approach with real-time data assimilation for enhanced WEC power prediction accuracy.

Findings

Assimilating wave spectra improves forecast skill.

Real-time data enhances prediction accuracy.

Field deployment shows promising results.

Abstract

Integration of renewable power sources into grids remains an active research and development area, particularly for less developed renewable energy technologies such as wave energy converters (WECs). WECs are projected to have strong early market penetration for remote communities, which serve as natural microgrids. Hence, accurate wave predictions to manage the interactions of a WEC array with microgrids is especially important. Recently developed, low-cost wave measurement buoys allow for operational assimilation of wave data at remote, site specific locations where real-time data have previously been unavailable. We present the development and assessment of a wave modeling framework with real-time data assimilation capabilities for WEC power prediction. The availability of real-time wave spectra from low-cost wave measurement buoys allows for operational data assimilation with the ensemble Kalman filter technique within a hybrid modeling procedure whereby physics-based numerical wave models are combined with data-driven error models that aim to capture the discrepancy in prescribed boundary conditions. With that aim, measured wave spectra are assimilated for combined state and parameter estimation while taking into account model and observational errors. The analysis allows for more accurate and precise wave characteristic predictions at the locations of interest. Initial deployment data obtained offshore Yakutat, Alaska, indicated that measured wave data from one buoy that were assimilated into the wave modeling framework resulted in improved forecast skill in comparison to traditional numerical forecasts.