This paper reports on an ongoing international effort to establish guidelines for numerical modeling of wave energy converters, initiated by the International Energy Agency Technology Collaboration Program for Ocean Energy Systems. Initial results for point absorbers were presented in previous work, and here we present results for a breakwater-mounted Oscillating Water Column (OWC) device. The experimental model is at scale 1:4 relative to a full-scale installation in a water depth of 12.8 m. The power-extracting air turbine is modeled by an orifice plate of 1–2% of the internal chamber surface area. Measurements of chamber surface elevation, air flow through the orifice, and pressure difference across the orifice are compared with numerical calculations using both weakly-nonlinear potential flow theory and computational fluid dynamics. Both compressible- and incompressible-flow models are considered, and the effects of air compressibility are found to have a significant influence on the motion of the internal chamber surface. Recommendations are made for reducing uncertainties in future experimental campaigns, which are critical to enable firm conclusions to be drawn about the relative accuracy of the numerical models. It is well-known that boundary element method solutions of the linear potential flow problem (e.g., WAMIT) are singular at infinite frequency when panels are placed directly on the free surface. This is problematic for time-domain solutions where the value of the added mass matrix at infinite frequency is critical, especially for OWC chambers, which are modeled by zero-mass elements on the free surface. A straightforward rational procedure is described to replace ad-hoc solutions to this problem that have been proposed in the literature.
The sustained development of wave energy in the past two decades makes it one of the most promising renewable energy resources to be added to the diverse mixture of supply systems. The inherent difficulty of grid integration of wave energy involves various aspects such as suitable control of power converters and power conditioning processes, allowing for the extraction of the best quality power. This paper presents a comprehensive review of different aspects of grid integration of wave energy devices, including classification of wave energy devices based on their impacts on grid integration, grid requirements imposed by the grid codes and storage technologies used for the grid integration of wave energy converters (WECs). This study also analyses various grid integration studies on wave energy converters, with particular emphasis on power converter technology and control. Furthermore, specific attention is given to the combinational studies that use wave energy combined with other renewable resources due to their positive synergies in lowering the costs of energy produced and that hold an opportunity for future research. An economic case is presented for wave energy devices based on value on the grid.
The unit commitment problem in power system is a highly nonlinear, non-convex, multi-constrained, complex, highly dimensional, mixed integer and combinatorial generation selection problem. The phenomenon of committing and de-committing represents a discrete problem that requires binary/discrete optimization techniques to tackle with unit commitment optimization problem. The key functions of the unit commitment optimization problem involve deciding which units to commit and then to decide their optimum power (economic dispatch). This paper confers a binary grasshopper optimization algorithm to solve the unit commitment optimization problem under multiple constraints. The grasshopper optimization algorithm is a metaheuristic, multiple solutions-based algorithm inspired by the natural swarming behavior of grasshopper towards food. For solving the binary unit commitment optimization problem, the real/continues value grasshopper optimization algorithm is mapped into binary/discrete search-space by using an Sshaped sigmoid function. The proposed algorithm is tested on IEEE benchmark systems of 4,
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