GARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization

Abstract

Wind energy has emerged as a potential alternative to traditional energy sources for economical and clean power generation. One important aspect of wind energy generation is the layout design of the wind farm so as to harness maximum energy. Due to its inherent computational complexity, the wind farm layout design problem has traditionally been solved using nature-inspired algorithms. An important issue in nature-inspired algorithms is the termination condition, which governs the execution time of the algorithm. To optimize the execution time, appropriate termination conditions should be employed. This study proposes the concept of a rewarding mechanism to achieve optimization in termination conditions while maintaining the solution quality. The proposed rewarding mechanism, adopted from the stochastic evolution algorithm, is incorporated into a genetic algorithm. The proposed genetic algorithm with the rewarding mechanism (GARM) is empirically tested using real data from a potential wind farm site with different rewarding iterations.

Keywords

• wind farm layout design
• wind farm micrositing
• wind energy
• optimization
• stochastic evolution
• artificial intelligence
• nature-inspired algorithms
• genetic algorithms