Open AccessRevenue maximisation and storage utilisation for the Ocean Grazer wave energy converter: a sensitivity analysis
Author(s) -
BarradasBerglind Jose de Jesus,
Dijkstra Tom,
Wei Yanji,
Rooij Marijn,
Meijer Harmen,
Prins Wout A.,
Vakis Antonis I.,
Jayawardhana Bayu
Publication year - 2018
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2018.5107
Subject(s) - revenue , sensitivity (control systems) , benchmark (surveying) , software deployment , heuristic , computer science , control (management) , operations research , mathematical optimization , environmental economics , economics , engineering , finance , mathematics , geodesy , electronic engineering , artificial intelligence , geography , operating system
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.