  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.
