No abstract

Wave energy has great potential as a renewable energy source, and can therefore contribute significantly to the proportion of renewable energy in the global energy mix. This is especially important since energy mixes with high renewable penetration have become a worldwide priority. One solution to facilitate such goals is to harvest the latent untapped energy of the ocean waves and convert it into electrical energy. A device performing such a task is known as a wave energy converter (WEC). In the present work, we focus on a specific type of WEC, which has the advantages of both significant energy storage capabilities, and adaptability to extract energy from the whole spectrum of ocean waves. This WEC consists of an array of point absorber devices, comprising adaptable piston-type hydraulic pumps powered by interconnected floaters, whose target is to extract optimally the energy from waves of varying heights and periods. Two different cases are considered in this paper; namely, the analysis of the energy extraction in a simplified floater blanket, and a model predictive control strategy to maximize the extracted energy of the WEC.

Increased penetration of renewable energy generation motivates a change of paradigm in the way power systems are structured and operated, as advocated by the smart grid concept. Accordingly, in this paper we investigate the lossless storage capabilities of the Ocean Grazer wave energy converter (WEC), which could facilitate the aforementioned paradigm shift. This specific WEC exhibits both adaptability with respect to the incoming waves and significant lossless storage capabilities. We propose a model predictive control (MPC) strategy based on a lumped dynamical model in order to mitigate power imbalances in the power grid and maximize the revenue of the WEC. Furthermore, we illustrate that the proposed strategy exploits the WEC energy storage capabilities and we show the economic benefits it brings. Lastly, the proposed strategy is compared with a heuristic approach and a setting without storage.

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.