Optimisation of Large Wave Farms using a Multi-strategy Evolutionary Framework
This work addresses energy optimization for wave farms, an incremental improvement in renewable energy technology.
The researchers tackled the problem of maximizing total harnessed power in large wave farms with three-tether wave energy converters by proposing a hybrid multi-strategy evolutionary framework, which outperformed state-of-the-art methods in convergence speed and farm output.
Wave energy is a fast-developing and promising renewable energy resource. The primary goal of this research is to maximise the total harnessed power of a large wave farm consisting of fully-submerged three-tether wave energy converters (WECs). Energy maximisation for large farms is a challenging search problem due to the costly calculations of the hydrodynamic interactions between WECs in a large wave farm and the high dimensionality of the search space. To address this problem, we propose a new hybrid multi-strategy evolutionary framework combining smart initialisation, binary population-based evolutionary algorithm, discrete local search and continuous global optimisation. For assessing the performance of the proposed hybrid method, we compare it with a wide variety of state-of-the-art optimisation approaches, including six continuous evolutionary algorithms, four discrete search techniques and three hybrid optimisation methods. The results show that the proposed method performs considerably better in terms of convergence speed and farm output.