Optimization of wind turbines siting in a wind farm using genetic algorithm based local search

The present work is devoted to search for the optimum wind farm layout using binary real coded genetic algorithm (BRCGA) based local search (LS); gathering robust single wake model with suitable wake interaction modeling. The binary part of genetic algorithm (GA) is used to represent the location of turbines; while the real part is used to give the power generated by each turbine at its location. In addition, the solution quality is improved by implementing LS technique; where it intends to find the optimal solution near the approximated solution obtained by BRCGA. The Jensen wake model along with the sum of squares model are used to obtain the available power for each turbine; where it is considered one of the most common analytical models used for wind farm optimization. Siting improvement is achieved, as compared with earlier studies.