%0 Electronic Article
%A Jose de Jesus Barradas-Berglind
%A Tom Dijkstra
%A Yanji Wei
%A Marijn van Rooij
%A Harmen Meijer
%A Wout A. Prins
%A Antonis I. Vakis
%A Bayu Jayawardhana
%K market integration
%K parameter sensitivity analysis
%K revenue maximisation model predictive control strategy
%K WEC
%K heuristic strategy
%K MPC strategy
%K storage utilisation
%K ocean grazer wave energy converter
%X 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.
%@ 1752-1416
%T Revenue maximisation and storage utilisation for the Ocean Grazer wave energy converter: a sensitivity analysis
%B IET Renewable Power Generation
%D August 2018
%V 12
%N 11
%P 1241-1248
%I Institution of Engineering and Technology
%U https://digital-library.theiet.org/;jsessionid=5fbafqssip4hk.x-iet-live-01content/journals/10.1049/iet-rpg.2018.5107
%G EN