No abstract

A novel wave energy converter, termed the Ocean Grazer, designed to extract energy from waves of varying profiles and energy contents has recently been proposed by the University of Groningen. The authors have performed preliminary modeling work to predict the behavior of the converter’s power take-off system, and constructed a proof-of-concept prototype to validate basic model predictions.

This study presents a revenue maximisation strategy for market integration of a novel wave energy converter (WEC), part of the Ocean Grazer platform. In particular, the authors evaluate and validate the aforementioned revenue maximisation model predictive control (MPC) strategy through extensive simulations and by checking the underlying assumptions of the strategy implementation. Accordingly, an annual simulation of the MPC strategy is shown, which illustrates seasonality effects; furthermore, a benchmark against a heuristic strategy is presented, followed by analyses of the parameter sensitivity and the assumptions on the control loop information that the MPC receives. These efforts shed some light on the impact of variations of the considered parameters and variables on the total revenue and provide insights to optimally scale the WEC. Lastly, the challenges associated with the deployment of such a strategy are addressed, followed by concluding remarks.