Energy capture optimization for an adaptive wave energy converter

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.

Investigating the adaptability of the multi-pump multi-piston power take-off system for a novel wave energy converter

“…For the optimal operation of our Ocean Grazer device, there are a number of critical sub-systems that require advanced control systems. For example, taking advantage of the adaptability of the MP 2 PTO, Barradas-Berglind et al (2016) developed a preliminary model predictive control (MPC) strategy aimed at maximizing the energy extraction of the SPP model from Vakis and Anagnostopoulos (2016) and made a preliminary analysis on the energy capture of a floater blanket consisting of five independent floater elements. The results showed that the energy capture could be optimized with a suitable MPC algorithm.…”

Nearest Neighbor Control For Practical Stabilization of Passive Nonlinear Systems

“…The central object of this paper is the nonlinear control systems Σ given in (1). The fundamental property that we associate with Σ is that, it is passive, i.e., for all pairs of input and output signals u, y, we have T 0 y(t), u(t) dt > −∞ for all T > 0; see [33,30,25] for some primary references on passive systems.…”

“…where co(S) is the convex closure of S. The set-valued mapping K(F ) is the Krasovskii regularization of F , and under certain regularity assumptions on F , K(F ) is compact and convex-valued, and moreover it is upper semicontinuous. 1 For an upper semicontinuous mapping Φ : R n ⇒ R n , consider the differential inclusion…”

“…However when there are design constraints imposed on the systems such that the input or output space is constrained to finite discrete sets, there are only a few control design approaches and control systems analyses that are applicable. Examples of such systems' design constraints are, for instance, the design of the power take-off systems of the Ocean Grazer wave energy converter (WEC), where the device can only actuate a fixed set of constant actuator systems [1,32] or a fixed configuration of constant thruster systems in space rockets. In these examples, the control input can only be assigned from a finite set of discrete values.…”

“…Definition 11 (Constant Incremental Passivity) Consider the nonlinear system Σ as in (1). The system Σ is said to be incrementally passive with respect to constant input if, for every (x * , u * ) ∈ E, the corresponding incremental system Σ in (23) with input u and output y, is passive; that is, there exists a storage function…”