Offshore Wind Farm Layout Optimization Using Adapted Genetic Algorithm: A different perspective
This work addresses layout optimization for offshore wind farms, which is an incremental improvement in renewable energy efficiency.
The paper tackles the problem of optimizing offshore wind farm layouts to minimize wake effects by proposing an adaptive genetic algorithm that uses location swaps instead of random crossovers to maintain a fixed number of turbines. The method achieves faster convergence than conventional GAs in experiments with different wind speed settings.
In this paper we study the problem of optimal layout of an offshore wind farm to minimize the wake effect impacts. Considering the specific requirements of concerned offshore wind farm, we propose an adaptive genetic algorithm (AGA) which introduces location swaps to replace random crossovers in conventional GAs. That way the total number of turbines in the resulting layout will be effectively kept to the initially specified value. We experiment the proposed AGA method on three cases with free wind speed of 12 m/s, 20 m/s, and a typical offshore wind distribution setting respectively. Numerical results verify the effectiveness of our proposed algorithm which achieves a much faster convergence compared to conventional GA algorithms.