Wave Energy Converter Optimal Design Under Parameter Uncertainty

Abstract

In the field of renewable technologies, the possibility to obtain energy exploiting seas and oceans’ wave motion has been known for a long time. Devices that transform wave energy into electric energy exploiting wave motion are called Wave Energy Converters (WEC). Following the design studies carried out in recent years, the research now proceeds towards the development of useful processes for the optimization of these devices. In this work we develop a preliminary robust optimal design process for the WEC system devices, in order to increase their reliability and robustness. Robust optimal design is a probabilistic optimization method for realistic optimization problems, in which, the uncertainty that occurs between real-world implementations and their ideal project value is taken into account. This method studies these parameters and finds suitable solutions to avoid unsatisfactory system performances and designs which can compromise their performances. Therefore, the process final purpose is to obtain a robust optimum instead of a global optimum. In this work, we developed the robust design optimization strategy for the design of a pitching wave energy converter, able to minimize its Levelized Cost of Energy (LCoE). This is done exploiting information given by two selected robustness indexes.

Measuring the Robustness of Optimal Design Solutions for Wave Energy Converters via a Stochastic Approach

“…A well-chosen robustness measure offers a valuable means to discern a system's responsiveness (or lack thereof) to factors that might result in sub-optimal performance. A glimpse of some of the robustness metrics employed in the literature [43,45], and considered within the present study, is provided in Table 1, together with their formulations. The notation used in Table 1 refers to the so-called zero-uncertainty vector ⃗ u 0 as the parameters uncertainty vector with all elements equal to zero, i.e., ⃗ u 0 = [0...0] ⊺ ∈ R N .…”

“…The first parameter was the device's pitch inertia M (5,5) . Previous analyses [45] highlight its impact on device performance. To propagate uncertainty concerning this variable, it was necessary to define a probability distribution for the associated uncertainties.…”